Corporate Manager

Read about the company’s commitment to the communities we live in.

Sr. Manager, CR and Environment

Community

We work with education partners all over the world to help students build strong, lasting digital safety habits. And we equip teachers with tools and resources that make those digital skills part of everyday learning in their school curriculums, not a one-off lesson.&nbsp;&nbsp;Since 2021, more than 42,300 students across 200 schools have taken part in Cyber Safety training through a collaboration between Norton, part of the Gen family of brands, and Bal Raksha Bharat (Save the Children – India).&nbsp;&nbsp;The focus is practical: helping young people recognize online risks and build habits that keep them safer—from spotting scams to protecting their personal information. And by bringing teachers and families into the learning process, the program ensures those behaviors don’t stop at the classroom door.&nbsp;

<h2>How it works&nbsp;</h2>We start early and keep it relevant. Students ages 10-16 learn practical Cyber Safety skills through age-based lessons covering topics like scams, phishing and online privacy. Many schools also run Cyber Safety Clubs to keep those habits going beyond the classroom.&nbsp;&nbsp;Teachers are trained alongside students with tools they can use every day — not just once.&nbsp;And the learning doesn’t stop at school. Workshops bring in parents, caregivers, School Management Committees and even local law enforcement, so the same messages show up at school, at home and in the community.&nbsp;Between April 2025 and March 2026 alone, these sessions reached more than 9,400 people, including nearly 7,800 students and more than 1,700 caregivers.&nbsp;<h2>The results are in. The impact is clear. &nbsp;</h2><ul type="disc"><li data-list-item-id="e50336e8fde80f2a8f28a0d021f201cb1">99.5% of students surveyed showed improved digital safety knowledge&nbsp;</li><li data-list-item-id="e728025570a6c3d168ca5e928feab5a43">Students scoring 70% or higher on the Cyber Safety assessment increased from 5% before the session to 91% afterwards&nbsp;</li><li data-list-item-id="e3ea6261ccdf7b675e38313b9e0410513">96% of parents said the training helped them guide their children&nbsp;&nbsp;&nbsp;</li><li data-list-item-id="e4bc2abde24be47a263d698de4f102fee">91% of teachers saw positive changes in digital behavior, like stronger passwords and scam awareness&nbsp;&nbsp;</li></ul>These results come from a sample of schools in Pune, Maharashtra and Thiruvallur, Tamil Nadu that have been running the program for at least a year. &nbsp;

<h2>Why this approach matters&nbsp;</h2>This work in India is part of something bigger.&nbsp;Across the globe, Gen is helping expand access to digital education that is locally relevant, scalable and built to last—so more people can navigate the digital world with confidence.&nbsp;We’re building on this through partnerships with Discovery Education on <a href="https://www.gendigital.com/blog/impact/community/discovery-education-ai-curriculum" target="_blank" rel="noreferrer noopener">AI literacy</a> and our <a href="https://www.gendigital.com/blog/impact/community/financial-literacy-month-2026" target="_blank" rel="noreferrer noopener">financial wellness efforts</a> with the Jump$tart Coalition and the National Consumers League.&nbsp;&nbsp;

How our partnership with Save the Children India builds safer digital habits

Expanding Digital Education for Students in India

Phishing scams are more convincing. AI is making fake messages harder to spot. And once attackers get into one account, they often don’t stop there. They move laterally – email, banking, shopping, even identity data — turning a single mistake into something much bigger.That’s how account takeovers become&nbsp;identity theft.And the signals people used to rely on? They’re fading fast.As James E. Lee, President of the Identity Theft Resource Center (ITRC), explains: “Remember when we used to tell people bad spelling and poor grammar were the best way to tell if someone was trying to scam you or steal your identity? AI-assisted attacks now make it easy to convince people a scam is real, compromising or bypassing passwords along the way. That’s why we all need to change our habits to include a multi-layer approach to protecting our personal information, starting with passkeys where available, and long, unique passwords created by a password manager + MFA when passkeys are not available.”The good news: protecting yourself doesn’t require expert-level skills. It requires a smarter, layered approach to how you log in; one that makes life harder for attackers, not for you. The new reality: passwords alone aren’t enoughPasswords are still everywhere. But on their own, they’re no longer strong enough to stand up to modern threats like phishing, credential stuffing, and data breaches.Today, the most effective defense is layered:<ul type="disc"><li data-list-item-id="ebe7fef585caa31819c6accdda95c3775">Strong, unique passphrases&nbsp;</li><li data-list-item-id="e29bc7f2b2042241054279627351da17d">A password manager&nbsp;</li><li data-list-item-id="e92657b16dd661843074c9f463fa52be1">Multi-factor authentication (MFA)&nbsp;</li><li data-list-item-id="e6ef7fd2c3211fd022bd5c30000356466">Authenticator apps&nbsp;</li><li data-list-item-id="e99b977e01d337356e97b88ed370094ea">Passkeys&nbsp;</li></ul>Each layer adds friction for attackers and resilience for you.<h2>Start with stronger passwords or passphrases</h2>Short, complex passwords are fading out. Long, unique&nbsp;passphrases are in.A passphrase (something like a string of unrelated words) is:<ul type="disc"><li data-list-item-id="e286a4823c6a31ced6d37539a0ef92c7a">harder to crack&nbsp;</li><li data-list-item-id="e51ada4f98556fc6e4edfd506d5b0811e">easier to remember&nbsp;</li><li data-list-item-id="e10a8c3ae3ceb601bfe4d90320037d1d7">more resistant to brute-force attacks&nbsp;</li></ul>Aim for at least&nbsp;12–15 characters and never reuse credentials across accounts.Why? Because password reuse is one of the fastest paths to identity theft. One breached account can unlock many more—especially email, which acts as a gateway to everything else.

<h2>A password manager makes strong security a reality</h2>Here’s the truth: no one can securely manage dozens of unique passwords on their own.That’s where a password manager comes in. A tool like&nbsp;<a href="https://my.norton.com/extspa/passwordmanager" target="_blank">Norton Password Manager</a> helps you:<ul type="disc"><li data-list-item-id="e93d3e2f5a1f3d684b90b6064c19fbb2a">generate strong, unique passwords&nbsp;</li><li data-list-item-id="e280b796fe7b27859a3108336caf07c71">store them securely&nbsp;</li><li data-list-item-id="e695229e2672c6f45484d8ff402915c28">autofill them across devices&nbsp;</li></ul>👉 Instead of reusing passwords or writing them down, you create a system that scales safely.And that matters because better password habits are one of the most effective ways to prevent account takeovers before they start.<h2>MFA: the simplest upgrade with the biggest impact</h2>Even strong passwords can be stolen through phishing, malware or breaches.That’s why&nbsp;multi-factor authentication (MFA) is critical.MFA requires a second form of verification:<ul type="disc"><li data-list-item-id="e70545fda0a315f0787bd5c2d97b9297e">something you know (password)&nbsp;</li><li data-list-item-id="e9c798773de7ff0641db0b83bf87b026b">something you have (device or app)&nbsp;</li><li data-list-item-id="e5b11b8077468b93b11b9eea30a434e3e">something you are (biometrics)&nbsp;</li></ul>So even if an attacker gets your password, they still hit a wall.Think of it this way: A password locks the door. MFA adds a deadbolt.<h2>Authenticator apps &gt; SMS (and here’s why)</h2>Many people rely on text message codes for MFA. It’s better than nothing—but it’s not the strongest option.Authenticator apps are more secure because:<ul type="disc"><li data-list-item-id="e1becac39799082e5fee0a8f2c7adba0b">codes are generated on your device&nbsp;</li><li data-list-item-id="eea63cb614fe3a9dfbb519f6a3e81937a">they aren’t transmitted over networks&nbsp;</li><li data-list-item-id="ee5f9827b24141bed1f6ec8e7c1acc7c4">they’re less vulnerable to SIM-swap attacks&nbsp;</li></ul>They also strike the right balance: strong security, minimal friction.<h2>Passkeys: a simpler, phishing-resistant future</h2>Passkeys are gaining traction for a reason. They remove the password entirely.Instead of typing anything, you log in using:<ul type="disc"><li data-list-item-id="e9dcf2622584197e5cb16fda3f11ef6ac">fingerprint&nbsp;</li><li data-list-item-id="ea7d11d74d81497a4acd16e7c6c29d6cd">face scan&nbsp;</li><li data-list-item-id="e0426ea4a37edded97d0c20340c5f78d6">device PIN&nbsp;</li></ul>Behind the scenes, passkeys use&nbsp;public key cryptography, which means:<ul type="disc"><li data-list-item-id="e58dcc6362b3c8cd82d1651546d8ed350">no shared secrets to steal&nbsp;</li><li data-list-item-id="e192dbdc4720550b21d96ceefc468888e">no passwords to phish&nbsp;</li><li data-list-item-id="e6ac56d7e9a1e70491553d822f360e9dd">no risk of entering credentials into fake sites&nbsp;</li></ul>They’re designed to be&nbsp;phishing-resistant by default.Passkeys aren’t universal yet, but where they’re available, they’re one of the strongest upgrades you can make.<h2>How account takeovers turn into identity theft</h2>This is where it gets real.When attackers access an account, they don’t just stop there. They:<ul type="disc"><li data-list-item-id="e64049057c95e55d1542faa1c6cae8b82">reset passwords&nbsp;</li><li data-list-item-id="e102a5f8ec88ebe93219c8c025126d42f">scrape personal data&nbsp;</li><li data-list-item-id="e91eff0019645f2083dfcbf411937ac9d">impersonate you&nbsp;</li><li data-list-item-id="efb44ea2fdf24f5e2381c8e5acae6b8e3">pivot into financial or identity systems&nbsp;</li></ul>That’s how a compromised login becomes&nbsp;identity theft.According to the&nbsp;<a href="https://www.idtheftcenter.org/" target="_blank">Identity Theft Resource Center</a> (ITRC), stolen credentials are one of the most common starting points for identity-related crimes. The <a href="https://identitytheft.gov/" target="_blank">U.S. Federal Trade Commission</a> also notes that identity theft often begins with exposed login information.In other words, protecting your accounts is one of the most effective ways to protect your identity.<h2>Security doesn’t stop at login</h2>Getting into your accounts safely is step one. What happens after matters too.<ul type="disc"><li data-list-item-id="eeca421063d947af9513ba3520e0d9e44">Use secure apps with end-to-end encryption&nbsp;</li><li data-list-item-id="e57922da03ac9c2b8ad3b098c22ed9eac">Be cautious about what you share and store&nbsp;</li><li data-list-item-id="ed0891d3c1de4af26b749c0b569485d3c">Keep devices updated&nbsp;</li></ul>Good digital habits don’t end at login; they extend across everything you do online.<h2>What to do now (start here)</h2>Focus on your most important accounts:<ul type="disc"><li data-list-item-id="e0d7d0c8f79bf63ec1dc9855b30165df3">email&nbsp;</li><li data-list-item-id="e308b155522a2f37f61aa2deaabfb01b3">banking&nbsp;</li><li data-list-item-id="e26ce5f8a4c9ff8a30dfc73dd89eea960">shopping&nbsp;</li><li data-list-item-id="e2324f3fed7f0dcdafb1af0c2ee15ca9b">social media&nbsp;</li></ul>Then:<ul type="disc"><li data-list-item-id="e6f69f3a8b0b344d52b8219fb40b2ab09">Create long, unique passphrases&nbsp;</li><li data-list-item-id="e881ebfa1d05420bf605313135ecec426">Use a password manager &nbsp;</li><li data-list-item-id="eb63b60ac4032b3eca7f992e0b74e95b5">Turn on MFA everywhere you can&nbsp;</li><li data-list-item-id="ef4b8b21f6dccdba45a0f751e50edceec">Choose an authenticator app over SMS&nbsp;</li><li data-list-item-id="ea21ef6c61d702b4f3f3b14874b1109d2">Adopt passkeys when available&nbsp;</li></ul><h2>Build a layered defense that works</h2>There’s no single fix for cybersecurity. The strongest protection comes from combining tools that reinforce each other:<ul type="disc"><li data-list-item-id="e9103f9164c0015d124a746b512b58630">Passphrases protect against guessing and reuse&nbsp;</li><li data-list-item-id="e09e76fdc1cf114a79d3205044d2b4894">Password managers make strong habits sustainable&nbsp;</li><li data-list-item-id="e11204eee1d8516ebf6a7218416e4b33d">MFA blocks unauthorized access&nbsp;</li><li data-list-item-id="e549576d6a19185416cc2e423b8e08fcf">Authenticator apps strengthen that layer&nbsp;</li><li data-list-item-id="e3bf65eec480f11a943d6de5153de9028">Passkeys reduce reliance on passwords entirely&nbsp;</li></ul><h2>Build better sign-in habits, one step at a time</h2>Together, they create a system that’s much harder to break—and much easier to live with. Attackers don’t need much, just one weak password, one reused login, one missed step. That’s why small upgrades matter. A stronger passphrase. Turning on MFA. Using a password manager. Choosing passkeys when you can.You don’t have to do everything at once. But each step you take makes it harder for someone else to take control.

Cybercriminals aren’t just guessing passwords anymore, they’re logging in

Why Passkeys, MFA and Strong Passphrases Matter More Than Ever

Head of Corporate Responsibility and Public Policy

April is&nbsp;<a href="https://www.jumpstart.org/awareness/financial-literacy-month/">Financial Literacy Month</a>, an annual campaign to help people of all ages&nbsp;acquire the&nbsp;skills they need to make informed financial decisions and plan for the future. Every year, companies, nonprofits, libraries, schools, banks and more come together to develop resources, host workshops and raise awareness of simple ways to strengthen financial skills.Today, those skills go beyond budgeting and saving. Financial wellness and digital wellness are deeply connected.&nbsp;From online banking to mobile payments to scams and identity theft, financial decisions increasingly happen in digital environments.At Gen, we’re working to help people build both.Through partnerships with organizations like the&nbsp;<a href="https://www.jumpstart.org/">Jump$tart Coalition</a> and the&nbsp;<a href="https://nclnet.org/">National Consumers League</a>,&nbsp;and through the work of MoneyLion, part of the Gen family of brands, we are expanding access to the tools and education people need to navigate financial life with confidence.&nbsp;Supporting the Next Generation of Financial Decision-MakersEarlier this year, we announced a new partnership with the Jump$tart Coalition, the official nonprofit partner of Financial Literacy Month. The Jump$tart Coalition works to advance financial literacy through programs&nbsp;that reach students across K–12 classrooms nationwide.&nbsp;As an example, during Financial Literacy Month, the Jump$tart Coalition hosts an annual&nbsp;<a href="https://www.jumpstart.org/awareness/check-your-school/teen-teach-in/">Teen Teach-In</a>, where high school students studying personal finance teach elementary school students key financial concepts.&nbsp;The program builds understanding early and encourages peer-to-peer learning in communities.We also continue our longstanding support of&nbsp;<a href="https://lifesmarts.org/">LifeSmarts</a>, a program of the National Consumers League (NCL), that helps young people build critical knowledge in personal finance, consumer rights, identity protection and other digital issues. Beyond its flagship scholarship competition, LifeSmarts offers a wide variety of&nbsp;<a href="https://lifesmarts.org/resources/">trainings and resources</a> to support young people in their financial wellness journeys. In honor of tax season Gen, NCL and LifeLock, part of the Gen family of brands, collaborated on a study guide to help first-time filers understand common tax scams and how to avoid them. The resource is available&nbsp;<a href="https://www.linkedin.com/posts/gendigitalinc_the-tax-scam-threat-activity-7445205859850436608-i3SK?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAAAFtbI0B8PsFXlFu9p7Fy9WC_jon5VUXPlk">here</a>.&nbsp;Enhancing Financial Wellness Through Identity Theft ProtectionFinancial literacy today must include identity protection. As part of MoneyLion’s platform evolution, we introduced identity protection services for MoneyLion WOW members, bringing LifeLock-powered scam and identity protection directly into the app. This expands MoneyLion’s capabilities beyond financial management to include identity protection. These protections are especially important for younger users. Gen Z and millennials experience&nbsp;<a href="https://www.moneylion.com/trending/money/moneylion-adds-protection-benefits">higher rates</a> of scams than any other generation,&nbsp;making education and protection critical.We also partner with the&nbsp;<a href="https://www.idtheftcenter.org/">Identity Theft Resource Center</a> (<a>ITRC</a>) to raise awareness about evolving threats, including mobile scams, impersonation phishing and romance scams. Through collaborations like&nbsp;ITRC president James E. Lee appearing on&nbsp;LifeLock’s Control Room series, we help translate complex risks into practical, everyday actions people can take to protect themselves.Financial Literacy Month is an important moment—but this work continues year-round. By combining partnerships, technology and education, we aim to make financial and digital skills more accessible to more people. From classrooms to mobile apps, we are helping individuals build confidence, avoid risks and make informed decisions in an increasingly connected world.To learn more about how Gen supports financial wellness and digital safety, explore our&nbsp;<a href="https://s201.q4cdn.com/771113172/files/doc_downloads/2025/06/Gen-Social-Impact-Report-2025.pdf">Social Impact Report</a>.&nbsp;These grants were awarded from the Gen Foundation, a corporate-advised fund of Silicon Valley Community Foundation.

Gen, MoneyLion and our partners expand access to financial education during Financial Literacy Month 

Building Financial Confidence in a Digital World

Every year on March 27th, <a href="https://ailiteracyday.org/">National AI Literacy Day</a> brings together educators, students and families to explore how to prepare learners for an AI-enabled world. Organized by the EDSAFE AI Alliance, aiEDU, Common Sense Media and The Tech Interactive, the day combines professional development opportunities for teachers with resources and hands-on activities for students that help demystify AI and support responsible use of this rapidly evolving technology.&nbsp;&nbsp;At Gen, we believe helping young people understand technology is key to building a safer digital future, and we’re excited to join in this day of action.&nbsp;&nbsp;Together with <a href="https://www.discoveryeducation.com/?utm_source=cep_marketing&amp;utm_medium=paid_partner&amp;utm_campaign=cep_Norton_Ailitblog">Discovery Education,</a> a global leader in standards-based curriculum for K-12 learning, we created “<a href="https://mydigitallife.discoveryeducation.com/pages/87386ff6-712d-4c5e-9d78-dd51477eec87">Think Smart: Exploring AI and Staying Safe Online</a>.” This new video series is designed to help children and young people understand AI works and how to use it responsibly. The videos introduce key concepts such as where AI appears in everyday life and how to recognize potential risks. The series is available free to educators, students and families through <a href="https://mydigitallife.discoveryeducation.com/">My Digital Life</a>, a multilingual education platform developed by Discovery Education and Norton, part of the Gen family of brands.&nbsp;&nbsp;Supporting Educators with AI Literacy Resources&nbsp;Norton has partnered with Discovery Education and <a href="https://naaweb.org/">National AfterSchool Association</a> on My Digital Life since 2023. The platform provides teachers around the world with standards-aligned instructional resources that help students develop essential digital citizenship skills.&nbsp;&nbsp;Lesson plans, interactive activities and videos for students in grades 3-8 explore topics such as cyberbullying, misinformation and protecting personal data. Norton’s technical expertise helps ensure the content remains accurate and reflects the latest developments in the digital landscape.&nbsp;The new AI video series builds on this foundation. Through animated storytelling and relatable scenarios, it explores how artificial intelligence intersects with online safety and everyday digital behavior. The lessons encourage students to think critically about technology while developing the skills needed to use AI responsibly.&nbsp;Since launching, My Digital Life resources have reached nearly <a>490,000</a>&nbsp;young people globally. These materials are currently available in English, Spanish, Arabic, <a>Hindi</a>, French and German,&nbsp;helping bring digital education into classrooms across multiple regions.&nbsp;Expanding Access to AI Education&nbsp;Our collaboration with Discovery Education is part of Gen’s broader effort to expand access to digital safety and AI education for young people.&nbsp;We also partner with <a href="https://www.wagggs.org/en/">World Association of Girl Guides and Girl Scouts</a> (WAGGGS), a global movement reaching millions of girls and young women. In 2025, new AI education modules were added to <a href="https://www.wagggs.org/en/what-we-do/surf-smart-20/">Surf Smart 2.0</a>, a nonformal digital safety education program developed by WAGGGS and Norton that has reached more than 850,000 participants worldwide. Through the program, students explore how AI appears in their daily lives, from chatbots to content recommendations, and learn how to set thoughtful boundaries with emerging tools.&nbsp; &nbsp;&nbsp;Gen also supports Discovery Education’s <a href="https://digitalcitizenship.discoveryeducation.com/">Digital Citizenship Initiative</a>, which helps students build the skills needed to create a more respectful and responsible online world. The initiative includes multiple resources on <a href="https://digitalcitizenship.discoveryeducation.com/ai-literacy">AI literacy</a>, helping middle and high school students better understand how AI shapes their online experiences.&nbsp;&nbsp;Additionally, we participated in “Teaching AI with Confidence: Skills and Strategies for AI Literacy Day,” a free panel discussion for educators to help strengthen AI‑ready instruction across grade levels. Gen’s Head of Corporate Responsibility &amp; Government Affairs joined the panel, which highlighted practical, age‑appropriate strategies for teaching AI and digital citizenship. Check out the <a href="https://home.edweb.net/webinar/ai20260311/">full recording</a>.&nbsp;&nbsp;By working with educators, nonprofits and technology leaders, we aim to ensure that young people not only understand the tools affecting their digital lives but also feel empowered to use them safely and responsibly.&nbsp;To learn more about Gen’s digital education and training efforts, explore our latest&nbsp;<a href="https://www.gendigital.com/us/en/social-impact/social-impact-report/" target="_blank">Social Impact Report</a>.&nbsp;&nbsp;&nbsp;These grants were awarded from the Gen Foundation, a corporate-advised fund of Silicon Valley Community Foundation.&nbsp;

Gen partners with Discovery Education on new video series focused on AI literacy

Helping Students Understand AI in the Classroom

Every year on March 8, International Women’s Day invites us to celebrate the long history of women’s social, economic and cultural progress while recognizing the work still ahead — including in the digital world.&nbsp;&nbsp;&nbsp;Technology has opened new doors for connection, learning and opportunity. Yet it has also introduced new risks. According to UN Women, technology-facilitated violence against women and girls is on the rise, with <a href="https://www.unwomen.org/en/digital-library/publications/2024/10/intensification-of-efforts-to-eliminate-all-forms-of-violence-against-women-report-of-the-secretary-general-2024" target="_blank" rel="noreferrer noopener">studies</a> showing that as many as 58% have experienced forms of digital abuse, including hacking, stalking, and online harassment.&nbsp; &nbsp;At Gen, we believe the internet should be safe for everyone. That belief drives our partnership with the <a href="https://www.wagggs.org/en/" target="_blank" rel="noreferrer noopener">World Association of Girl Guides and Girl Scouts</a> (WAGGGS), a global movement reaching millions of girls and young women.&nbsp;&nbsp;Together with WAGGGS, Norton, part of the Gen family of brands, codeveloped <a href="https://www.wagggs.org/en/what-we-do/surf-smart-20/" target="_blank" rel="noreferrer noopener">Surf Smart</a>, a nonformal education program that has reached more than 850,000 girls and young women worldwide with digital safety resources to date. In early 2026, WAGGGS launched the next evolution of this work: <a href="https://www.wagggs.org/en/news/advocacy-for-generation-digital-a4gd-launches-to-support-youth-led-action-for-a-safer-digital-world/" target="_blank" rel="noreferrer noopener">Advocacy for Generation Digital</a>.&nbsp;<h2>Moving From Education to Action&nbsp;</h2>Advocacy for Generation Digital builds on Surf Smart 2.0 and helps girls and young women transform knowledge into leadership. The curriculum supports participants as they identify digital issues that matter most to them, strengthen research and communication skills and design advocacy campaigns that promote safer and more inclusive online spaces. &nbsp;The program offers learning pathways for elementary, middle and high school students and can be adapted to local contexts. It is available in English, French, Spanish and Arabic. WAGGGS also provides <a href="https://campfire.wagggs.org/topic/grant-application-advocacy-generation-digital-2026?_gl=1*1huf6ak*_ga*MzAyNTY1NDA5LjE3NjI1NjA1OTQ.*_ga_T1311503CC*czE3NzA5MjQzMzMkbzEwJGcxJHQxNzcwOTI0ODY5JGo2MCRsMCRoMA.." target="_blank" rel="noreferrer noopener">advocacy grants</a> to Member Organizations, helping bring youth-led projects to life in communities around the world.&nbsp;The impact of youth advocacy is already clear. In 2023 and 2024, young women in 10 countries launched campaigns addressing digital inclusion and online safety. Some organized public panels. Others engaged local media. Sophia Nabbale and Ronah Akatukunda from Uganda presented at the United Nations Civil Society Conference 2024 in Nairobi, highlighting the intergenerational digital divide and the fight against online violence.&nbsp;“Gen and WAGGGS share a vision for digital life," said Leena Elias, Chief Product Officer at Gen. "We believe the internet can be place where young people learn, connect and express themselves safely. Advocacy for Generation Digital is another step toward bringing that vision to life, empowering girls and young women to shape digital spaces that reflect their needs and aspirations.”&nbsp;<h2>Preparing the Next Generation for an AI-Driven World</h2>Our partnership continues to evolve with technology. In 2025, Surf Smart 2.0 was updated to include new modules on artificial intelligence. Participants explore how AI shows up in their daily lives, from chatbots to content recommendations, and learn how to set responsible boundaries with emerging tools.&nbsp; &nbsp;Throughout 2026, Gen and WAGGGS are also amplifying the voices of program participants — sharing firsthand accounts from Smart Surfers and Advocacy Champions about what they’ve learned and how they are leading change in their communities.&nbsp;

Digital Freedom depends on more than protection. It requires participation, confidence and leadership. By investing in youth-led advocacy, we are helping ensure the next generation is not only prepared to navigate the digital world safely — but ready to shape it.&nbsp; &nbsp;To learn more about how Gen supports digital education and training around the world, explore our latest <a href="https://www.gendigital.com/us/en/social-impact/social-impact-report/" target="_blank" rel="noreferrer noopener">Social Impact Report</a>. &nbsp;&nbsp;These grants were awarded from the Gen Foundation, a corporate-advised fund of Silicon Valley Community Foundation.&nbsp;&nbsp;

In celebration of International Women’s Day, Gen highlights its partnership with the World Association of Girl Guides and Girl Scouts 

From Digital Skills to Digital Advocacy: Empowering Girls to Lead Online 

Explore Gen™ leadership perspectives and hear from industry experts talking about trending topics, the digital landscape, and the latest cybersecurity news.

Leadership Perspectives

There’s a quiet shift happening with money right now. Gen Z isn’t just earning, spending and saving differently; they’re doing it in a world where your financial life is fully online, constantly moving and, frankly, a little chaotic. One minute it’s budgeting tips on TikTok, the next it’s a “too good to be true” investment ad sliding into your feed.So let’s strip it back. Financial safety isn’t about being perfect. It’s about being prepared before something goes sideways.&nbsp;<h4>Start with this: safety beats strategy</h4>There’s a lot of noise around investing early, building passive income or chasing the next big thing. And yes, Gen Z is already jumping into investing earlier than previous generations, often using digital platforms to take control of their finances.But here’s the part that gets skipped: none of that works if your foundation isn’t solid.Financial safety starts with three basics: • Cash buffer (even a small one) • Controlled spending • Awareness of risk (scams, debt, bad advice)Because right now? A lot of Gen Z doesn’t have that safety net. Around 62% don’t have emergency savings, and many rely on credit cards to get through unexpected expenses.That’s not a personal failure. It’s the reality of higher costs, unstable income and a system that wasn’t exactly designed to be easy.But it does mean one thing: building your own safety net matters more than ever.&nbsp;<h4>The smartest move: know where your money is going</h4>You don’t need a complicated system. You just need visibility.Call it budgeting, call it tracking, call it not being surprised at the end of the month. It’s all the same thing.A simple framework like 50/30/20 (needs, wants, savings) still works. But Gen Z is remixing it into something more real: intentional spending. Think “loud budgeting,” being honest about what you’re prioritizing and what you’re not.Translation: You’re not broke. You’re choosing where your money goes.That mindset shift alone is powerful.&nbsp;<h4>Build a buffer before you build wealth</h4>It’s tempting to jump straight into investing. But if one unexpected expense can wipe you out, you’re building on shaky ground.Start smaller: • Aim for $500 to $1,000 in emergency savings • Then grow toward 3 to 6 months of expensesIt doesn’t happen overnight. That’s fine. Consistency beats intensity here.Because when something goes wrong, and it will, you won’t have to rely on high interest debt to survive it.&nbsp;<h4>Watch the digital traps</h4>This is where financial safety gets very real.Gen Z lives online. That means your money does too. And that’s exactly where scammers operate.Fake ads. Impersonation. “Investment opportunities” that look just legitimate enough. These aren’t edge cases. They are designed to blend in with everything else you scroll past.And it works.There’s also a gap worth calling out: confidence doesn’t always equal protection.Gen Z is one of the most confident generations when it comes to spotting scams, but they’re also engaging with them at significantly higher rates than older generations, in some cases nearly double. (<a href="https://dataconomy.com/2025/08/11/gen-z-fall-for-online-scams-more-than-twice-as-often-as-older-ones/?utm_source=chatgpt.com">dataconomy.com</a>)And when scams work, they don’t just take money. They take identity.According to the Identity Theft Resource Center, many of today’s scams are designed to get people to willingly share personal information, including login details, Social Security numbers or verification codes. That information is then used to open new accounts or take over existing ones. (<a href="https://www.prweb.com/releases/identity-theft-resource-center-2025-trends-in-identity-report-impersonation-scams-rise-148-percent--become-top-reported-scam-302477300.html?utm_source=chatgpt.com">prweb.com</a>)That shift matters. Because identity theft isn’t a future problem. It is often the next step after a single click, message or seemingly harmless interaction.As Eva Velasquez, CEO of the Identity Theft Resource Center (ITRC), puts it: “We have to stop looking at identity theft and financial loss as two separate problems. Your identity is the ‘key’ to your entire financial house. If a scammer gets your login or your Social Security number, they don’t just take your money; they take your ability to build a future. Protecting your personal data is the most foundational investment you can make in your financial health.”If your financial life lives online, your identity is part of your financial safety net. Protecting it isn’t optional anymore. It is foundational.A few rules that hold up:<ul><li data-list-item-id="e68f7e0771c4abf542347cf3d63d577c2">If it creates urgency, pause</li><li data-list-item-id="eb8ea324e46df96adbfd24483ec6a98f6">If it promises easy money, question it</li><li data-list-item-id="e74a7beb276a369d3825446def9da1a64">If it looks legit but feels off, trust that instinct</li></ul>Being skeptical isn’t negativity. It’s protection.If you want to go deeper, the&nbsp;<a href="https://www.idtheftcenter.org/reports/?utm_source=chatgpt.com">Identity Theft Resource Center’s latest reports and resources</a> break down how these scams are evolving and what to watch for.

<h4>Use tools that actually help you move forward</h4>This part matters more than ever: you don’t have to figure all of this out alone.The line between financial health and digital safety is disappearing. That’s why platforms are starting to bring both together.MoneyLion, now part of Gen, is a good example of that shift. It’s built to help people not just manage money, but actually move forward with it: tracking spending, building credit, accessing tools that make financial progress feel doable.Because financial safety isn’t just about avoiding risk. It’s about having the confidence to act.&nbsp;<h4>You’re not behind. You’re early</h4>It’s easy to feel like you’re late with money. Like you should already have it figured out.You’re not. You’re early.Gen Z is already reshaping how money works. There are more open conversations, earlier investing and more willingness to question the system.The goal isn’t perfection. It’s momentum.Start with:<ul><li data-list-item-id="e25fe3be553f9c23cfc8ba4ecd87a05ae">Knowing your numbers</li><li data-list-item-id="e947fbae986f88ac1b2176531fd700260">Building a small safety net</li><li data-list-item-id="e1072ff692e39775a264cf450c494ccb5">Staying sharp online</li><li data-list-item-id="eaa82b9c8751df887da9a64a66a4100ff">Using tools that actually support you</li></ul>That’s Financial Safety 101.Not flashy. Not viral. But it works.And once that foundation is there, everything else, investing, growing, building real wealth, gets a whole lot easier.

From budgeting to scams to identity theft, here’s how Gen Z can build a financial foundation that actually holds up online

Financial Safety 101 for Gen Z: Build It Before You Need It

Security Evangelist at Gen

“Is this a scam?” has become one of the defining questions of the digital era.&nbsp;What is changing is not just the volume of these messages. It is how people respond to them. Only now, instead of asking a friend this question, they’re turning to ChatGPT. This shift marks a profound inflection point in human technology interaction. AI is no longer just a productivity tool or a content assistant. It is becoming a decision layer. A reasoning partner. A first checkpoint before action.&nbsp;At Gen, we see this as more than a trend. It signals the emergence of a new trust architecture for the internet.&nbsp;<h2>AI is becoming the first line of defense&nbsp;</h2>According to the <a href="https://www.gendigital.com/blog/insights/reports/threat-report-q4-2025" target="_blank" rel="noreferrer noopener">Gen Threat Report</a>, more than 90 percent of threats targeting people in 2025 stemmed from scams, phishing and deceptive advertising. The dominant risk online today is not malware quietly infecting a system. It is manipulation persuading a person.&nbsp;In parallel, AI tools like ChatGPT are becoming deeply embedded in everyday workflows. People use them to draft emails, compare products, research decisions and increasingly to validate suspicious messages.&nbsp;That convergence creates both opportunity and responsibility. If AI is where decisions are being shaped, then security intelligence must live there too.&nbsp;<h2>From reactive security to decision-centric protection&nbsp;</h2>In the <a href="https://us.norton.com/blog/ai/how-to-spot-scams-before-you-click" target="_blank" rel="noreferrer noopener">Norton integration within ChatGPT</a>, AI evaluates patterns such as impersonation tactics, urgency cues, requests for sensitive information and domain anomalies. Norton Genie, the world’s first AI-powered scam detector, pairs technical signals with behavioral indicators because modern fraud operates on both layers.&nbsp;But this product moment sits within a much larger transformation.&nbsp;<h2>The rise of autonomous AI and the trust imperative&nbsp;</h2>Our <a href="https://www.gendigital.com/blog/insights/research/openclaw-autonomy-risks" target="_blank" rel="noreferrer noopener">OpenClaw</a> research on autonomy risks explores how AI agents with increasing independence can introduce new security challenges. When AI begins to act on our behalf, make decisions or initiate actions, trust can no longer be assumed. It must be engineered.&nbsp;Autonomy without guardrails amplifies risk. Autonomy with transparency amplifies trust.&nbsp;That is why Gen has introduced the <a href="https://ai.gendigital.com/" target="_blank" rel="noreferrer noopener">AI Agent Trust Hub</a>, a framework and resource center focused on building responsible AI ecosystems. The Trust Hub emphasizes governance, explainability, accountability and human oversight as core design principles. This is not theoretical. As AI agents begin handling financial transactions, personal data and operational workflows, they will inevitably become targets themselves. The next phase of cybersecurity will not only protect humans from malicious actors. It will protect AI systems from manipulation and misuse. And it will protect humans from over-reliance on AI without understanding its limitations.&nbsp;<h2>Designing AI that augments human judgment&nbsp;</h2>The future of AI interaction is not about replacing decision-making. It is about strengthening it.&nbsp;When someone pastes a suspicious message into ChatGPT and asks “Is this legit?” they are not outsourcing responsibility. They are seeking clarity. They are extending their cognitive reach.&nbsp;That balance between automation and empowerment is critical.&nbsp;AI must be capable enough to identify emerging global scam patterns, including fake delivery notifications, banking impersonation attempts and AI generated voice phishing. But it must also remain interpretable. Transparent. Accountable. The Norton app, within ChatGPT, can be that.&nbsp;&nbsp;<h2>Building the next trust layer of the internet&nbsp;</h2>We are entering an era where AI will mediate much of our online experience. It will draft our communications. Curate our information. Screen our interactions. Execute transactions. The question is not whether AI will become part of our security posture. It already is.&nbsp;The real question is whether we build it with trust at the core.&nbsp;At Gen, our approach is grounded in three ideas:&nbsp;<ul type="disc"><li data-list-item-id="e965bc2f74345432db64ab84b0e13580d">Innovation must move at the speed of AI growth.&nbsp;</li><li data-list-item-id="ea2d5c939c934043a266b4f4c41bf1f08">Security must integrate into the environments where decisions happen.&nbsp;</li><li data-list-item-id="ec0f44d4a8b7612a7b70893f9bbe16d34">Trust must scale alongside autonomy.&nbsp;</li></ul>The integration of AI-powered scam detection into conversational platforms is one step in a broader journey. It reflects a belief that protection should not sit on the sidelines. It should sit inside the interaction.&nbsp;“Is this a scam?” may have been a common question in 2025. But tomorrow the more important question will be: Can we trust the AI systems helping us answer it?&nbsp;The future of cybersecurity will be defined by how well we design trust into every layer of AI interaction.&nbsp;And that future is being built now.&nbsp;

Why protecting people now means embedding security intelligence directly into AI tools like ChatGPT

The future of cybersecurity lives inside AI conversations 

Cyber Safety Chief Technology Officer

Threat research has always been a strange mix: part intuition, part pattern recognition, part tedious grind. You develop a sense for spotting dangerous patterns, but you spend most of your time on work that doesn't require that sense at all.&nbsp;&nbsp;Agentic AI is about to change that ratio. Not by replacing researchers, but by shifting where human judgment actually matters.&nbsp;<h5>&nbsp;The Work Today&nbsp;</h5>&nbsp;If you've spent time in threat research, you know the rhythm. You find something suspicious. You pull it apart in a disassembler or sandbox. You cross-reference&nbsp;&nbsp;indicators across half a dozen threat intel feeds. You document your findings in a format that looks remarkably similar to your last report.&nbsp;It's skilled work. It requires real expertise. But a significant portion of it is repetitive, manual, and frankly, boring. The kind of work that has to happen but doesn't benefit from your best thinking.&nbsp;That's where agents come in.&nbsp;<h5>Agents as a Junior Researcher &nbsp;</h5>The useful mental model isn't "AI replaces researchers." It's "AI becomes the junior researcher you never had enough of."&nbsp;Imagine pointing an agent at a malware sample and getting back: an initial behavioral analysis, a list of IOCs extracted and cross-referenced, similar samples identified from public repositories, and a draft report you can refine. Not perfect work. But solid first-pass work done in minutes instead of hours.&nbsp;Your job shifts from doing the analysis to directing and validating it. You focus on the anomalies the agent flagged but couldn't explain. You make the judgment calls about what's actually significant. You connect the dots that require broader context than any model has.&nbsp;The craft doesn't disappear. It moves up the stack.&nbsp;<h5>The Real Opportunity: Scope&nbsp;</h5>Speed is the obvious benefit. But the real transformation is scope.&nbsp;Right now, most threat research is reactive. Something happens, you investigate. You're always responding to what's already in front of you. The backlog of things you'd like to look into never gets shorter.&nbsp;When the grind gets automated, you can finally ask bigger questions:&nbsp;<ul><li data-list-item-id="e18eedd1bd2120f2a988307cfd9d59746">What patterns exist across six months of data that you never had time to analyze?&nbsp;</li><li data-list-item-id="ea2aaecd6fefaf03c9333d555fdbacbea">What connections might emerge if you could hunt across datasets simultaneously?</li><li data-list-item-id="e2cf856897b93fd5b0c70f22ab68ebc10">What's happening at the edges of your visibility that deserves a closer look?&nbsp;</li></ul>Agents don't just make you faster. They make exploration possible in ways it wasn't before.&nbsp;<h5>The Other Side of the Equation&nbsp;</h5>Here's the part we have to be honest about: attackers get agents too.&nbsp;Faster reconnaissance. Automated vulnerability discovery. Malware that adapts based on the environment it lands in. Phishing at scale with personalization that used to require manual effort.&nbsp;This isn't a one-sided advantage for defenders. It's an escalation. The ceiling rises for everyone. &nbsp;That doesn't make agentic AI a net negative for security. But it does mean we can't assume the technology automatically favors defense. The advantage goes to whoever learns to use these tools more effectively, faster.&nbsp;<h5>What Changes for Researchers&nbsp;</h5>The researchers who thrive in this shift will share a few traits:&nbsp;Comfort with direction over execution. Your value increasingly comes from knowing what questions to ask, not manually answering them. You guide agents, validate their outputs, and catch their mistakes.&nbsp;Judgment about signal and noise. Agents will surface more information than ever. Knowing what matters, what's a false positive, what's worth pursuing – that becomes the core skill.&nbsp;Adaptability. The tools will keep changing. The researchers who stay effective will be the ones who continuously experiment.<h5>The Craft Evolves&nbsp;</h5>There's a version of this story that sounds like loss. Less hands-on analysis. Less time in the weeds. The work that defined the field becoming automated.&nbsp;I don't see it that way.&nbsp;The craft of threat research has always been about finding what others miss and understanding what it means. That doesn't go away when agents handle the mechanical parts. If anything, it becomes more important.&nbsp;We're not losing the craft. We're changing where it lives. Less time on repetitive triage. More time on the problems that actually require human insight.&nbsp;That's not a loss. That's what progress in a field looks like.&nbsp;

Why the future of threat research isn’t automation replacing analysts—but elevating human judgment where it matters most.

How Agentic AI Will Transform Threat Research 

In 2026, the search for connection increasingly begins online. People are matching on dating apps, meeting partners through social platforms and sliding into DMs in ways that feel casual, normal, even expected.As Valentine’s Day approaches, dating app usage spikes and so do romance scams. Cybercriminals know February is peak season for emotional outreach, when millions are looking for connection.But as technology reshapes how we meet and connect, it’s also reshaping how trust can be manipulated.The latest Norton Insights Report,&nbsp;<a href="https://us.norton.com/blog/research/romance-scams-insights-report-2026">Artificial Intimacy</a>, reveals just how deeply artificial intelligence is transforming digital intimacy — and how scammers are adapting just as quickly.Romance scams are no longer just about fake profiles and poorly written messages. They’re powered by tools that can generate convincing conversations, realistic images and even synthetic audio or video. The deception is more immersive and more personal than ever before.<h2>What are AI romance scams?</h2>AI romance scams are online dating scams that use artificial intelligence to create convincing fake personas and emotionally persuasive conversations.Instead of sending generic scripts, scammers can now generate personalized messages, mirror someone’s tone and interests and create realistic profile images that don’t belong to real people. In some cases, AI can even be used to simulate voice or video, making interactions feel authentic.The result: deception that feels tailored, attentive and very real.<h2>How is AI changing online dating and digital trust?</h2>According to the&nbsp;<a href="https://us.norton.com/blog/online-scams/ai-online-dating-scams">Norton report</a>, 67% of current online daters globally say they would consider dating an AI chatbot, and 37% believe it’s possible to develop romantic feelings for an AI partner.At the same time, 81% of respondents report experiencing loneliness, with younger generations reporting even higher levels.This signals something bigger than curiosity. It reflects how normalized AI-mediated emotional connection has become.AI systems are designed to respond instantly and empathetically. When someone feels heard, understood and supported, trust can form quickly … even if the interaction is partially or fully AI-assisted.That shift changes how emotional bonds are built online. And scammers are paying attention.<h2>How Do AI Romance Scams Work?</h2>Romance scams follow a predictable pattern:<ol><li data-list-item-id="e15b54f3f1891914b76c7f2b112553efd">Establish emotional intimacy through frequent messaging</li><li data-list-item-id="e3f4a4acb2174b6e32e9c4d90aa234409">Build exclusivity and trust</li><li data-list-item-id="e68eaad06a25b11222019390d84b51a46">Introduce urgency (often financial)</li><li data-list-item-id="ed2dee4d5894af5043ca3a2d38fc5b3df">Request money, gift cards, wire transfers or cryptocurrency</li></ol>The Norton report finds that 34% of online daters say they have been targeted by a scam, and 36% say they’ve been pressured to send money to someone they met online.Globally, romance scams already account for billions in losses each year. As AI improves realism and scalability, the risk compounds.What makes 2026 different is the sophistication. AI allows scammers to:<ul type="disc"><li data-list-item-id="ecc7a56af3d1c511ecb226fae848ce25d">Generate highly personalized messages at scale</li><li data-list-item-id="ef3522862fd80d90065bb6aaf53f79807">Mirror tone, interests and emotional language</li><li data-list-item-id="ea8e255924a34794d69957640758be77c">Create realistic profile images</li><li data-list-item-id="e07d58e752a23579800ea69afabdb5495">Maintain constant, attentive communication</li><li data-list-item-id="efc37264727695b754710430c867c302c">Use emotional (and artificial) intelligence to commit fraud</li></ul><h2>What are the signs of an AI romance scam?</h2>Even with advanced tools, warning signs still exist. Red flags may include:<ul type="disc"><li data-list-item-id="e9bd1bfea4dc993103ef6f502445b5cb0">Rapid emotional escalation (“I’ve never felt this way before”)</li><li data-list-item-id="ee516559d6cbd3d61872dc53519894dc7">Requests to move conversations off dating platforms quickly</li><li data-list-item-id="ed6144398f1813335534d6d23f34a06c8">Refusal to meet in person or join a live video call</li><li data-list-item-id="e4fdd790944f08ccbc99836a358cbe4fc">Financial requests tied to emergencies or investments</li><li data-list-item-id="e3ce51b500038cb1cea5571e934d24251">Pressure to send cryptocurrency, gift cards or wire transfers</li></ul>If emotional intensity rises faster than real-world verification, it’s time to pause.

<h2>Why do romance scams increase around Valentine’s Day?</h2>Valentine’s Day amplifies emotional vulnerability. More people are actively dating, messaging and seeking companionship. That surge in activity creates opportunity.Scammers exploit heightened emotion and urgency. A romantic gesture, a crisis before a planned visit, a promise of future plans — these tactics become more effective when expectations are already emotionally charged.“We all want to be seen and understood, and there’s no shame in believing in a new connection that feels special,” says Eva Velasquez, CEO of the Identity Theft Resource Center. “That’s why romance scams are so heart-wrenching; they use our own kindness and hope against us. Even if you consider yourself tech-savvy, that emotional spark can easily blur a red flag. If a new light in your life suddenly asks for money or your private details, please, hit the pause button. It’s not about being cynical; it’s about protecting your heart and your future from someone who doesn't deserve your trustWhen loneliness intersects with seasonal pressure and AI-powered persuasion, risk increases.<h2>How can you protect yourself from romance scams?</h2>Awareness remains one of the strongest defenses.Practical safeguards include:<ul type="disc"><li data-list-item-id="ec8306c6ee68c216ad93743f299af3daa">Slowing down when emotional intensity escalates quickly</li><li data-list-item-id="ef3f0d66137848293989767e402197c72">Verifying identities through live, unscripted video conversations</li><li data-list-item-id="e1fcc4932b7df4df726703f09944678bf">Being cautious of financial requests involving wire transfers, gift cards or cryptocurrency</li><li data-list-item-id="ea01d7dd6ab6a67ae95cc50738b754696">Keeping personal and financial information private</li><li data-list-item-id="e4f27f8218e4b09bfa1c685e34496f107">Using identity protection and fraud monitoring tools, like&nbsp;<a href="https://lifelock.norton.com/">LifeLock</a></li></ul>Healthy skepticism does not mean rejecting connection. It means layering trust with verification.<h2>Can you fall in love with AI?</h2>The data suggests many people believe it’s possible.In a world where artificial intimacy can feel indistinguishable from the real thing, protecting both your heart and your finances demands a new kind of digital awareness; one grounded in curiosity, caution and informed trust.

Why do romance scams increase around Valentine’s Day? Understanding the emotional triggers and practical steps to prevent online dating fraud

Valentine’s Day 2026: How AI is fueling the rise of romance scams

OpenClaw, a new AI agent, has already amassed over 160,000 stars and 25,000 forks on&nbsp;<a href="https://github.com/openclaw/openclaw">GitHub</a> since its launch in late 2025. A powerful and exciting new tool, OpenClaw also introduces serious security risks. This article explains how OpenClaw works, the associated risks, and how the Gen Agent Trust Hub can help you use it more safely.<h2>What is OpenClaw and what does it do?</h2>OpenClaw (formerly known as Clawdbot and Moltbot) is an open-source autonomous AI agent that runs locally on your own device. Connecting LLMs to your data and apps, OpenClaw can work across your files, browsers, and messaging platforms like WhatsApp, Slack, Discord, and Telegram.&nbsp;OpenClaw can perform tasks like managing your calendar, sending messages, and automating workflows. To teach OpenClaw how to perform a task, users can download a preconfigured, community-contributed folder called a skill&nbsp;— basically a set of instructions that tells OpenClaw what to do and how to do it.&nbsp;For example, if you want your fitness app to create a personalized, optimized workout routine, you can install a skill that gives OpenClaw the logic and access it needs to do exactly that.But OpenClaw goes far beyond prepackaged features: it can also write its own skills. When prompted by a user, OpenClaw can create&nbsp;entirely new capabilities on the fly, extending its functionality in real time. That means it can adapt to new tasks as they arise, rather than being locked into a fixed set of features.

<h2>OpenClaw is transformative, but is it secure?</h2>Unlike traditional AI chatbots that operate within a limited conversational interface, OpenClaw is an autonomous agent, meaning it can act independently. And, through the ClawHub marketplace, you can add third-party plugins to enhance its capabilities. Combining that independence with add-on flexibility creates powerful opportunities. But it also&nbsp;<a href="https://www.gendigital.com/blog/insights/research/openclaw-autonomy-risks">introduces significant risks</a>.As OpenClaw connects to more parts of your system and integrates additional third-party skills, it gains access to emails, files, and chats, as well as websites and external sources you don’t control. While these additional skills and broader system access can help it complete complex tasks, it also increases the potential for malicious activity.A compromised or poorly designed skill could misuse its permissions, expose private data, or carry out actions you never intended. In short, the more third-party connections an agent has, and the more deeply it’s embedded into your digital life, the higher the stakes if something goes wrong.<h3>How AI agents are changing the rules of security</h3>Using an AI agent means delegating responsibility to software with extensive access to your system and limited risk controls. Instead of approving each action yourself, you’re authorizing the agent to act on your behalf. That presents an opportunity for threat actors: if they compromise your AI agent, they inherit all the data and permissions you’ve given it and could even commandeer the agent to take actions for their own purposes.&nbsp;By putting so much authority in one place, autonomous AI agents like OpenClaw introduce what researchers like Simon Willison have called a potential&nbsp;<a href="https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/">“lethal trifecta”</a> of security risks:<ul type="disc"><li data-list-item-id="e4fca606bedbd6d9f48f918a03f9af53f">Access to private data: The agent can see your files, login credentials, and personal information.&nbsp;</li><li data-list-item-id="ecf3a565dd59d91bd98732e5db397a3b0">Exposure to untrusted content: The agent can process and interact with messages, emails, websites, prompts, and third-party integrations.</li><li data-list-item-id="eecca7e258f9c3ab0e8ef4aa8bfdfcb46">The ability to communicate externally: The agent can send messages, call APIs, and run commands on your behalf.</li></ul>Some agents also have persistent memory, allowing malicious code or instructions to lie dormant rather than execute immediately, only to trigger later — much like a logic bomb.<h2>What are the core security risks of OpenClaw?</h2>AI agents like OpenClaw that can read information, make decisions, and act across systems present the risk of hallucinating authority — taking actions beyond their intended scope. Unlike traditional chatbots that can hallucinate text, AI agents can act as an&nbsp;<a href="https://www.gendigital.com/blog/insights/research/ai-gone-wild">“artificial mindless intelligence”</a>: a system that appears justified and authoritative but lacks true understanding and responsibility.&nbsp;Here are some of the main risks that AI agents like OpenClaw introduce:<ul type="disc"><li data-list-item-id="e7f1f8c06c1fb4bba4ae7ae46658102f6">Larger attack surface: An agent that can read messages, browse the web, and execute commands creates more opportunities for exploitation, misuse, or unintended actions.</li><li data-list-item-id="e03b344ca933a6f3f4cf9d3c45c4d0f0c">Prompt injection attacks: The messages and websites that OpenClaw processes and visits can contain hidden, malicious instructions that can cause real damage.</li><li data-list-item-id="e3a2d1cb3963a720b2a9badaba390afc6">Malicious skills and plugins:&nbsp;Skills are community-built, meaning they may not be vetted by security experts. Like installing malware, a skill with compromised instructions can collect information, steal credentials, or exfiltrate data.</li><li data-list-item-id="e01678b0e92ccc2957c2f6f671f6bac43">System-wide impact: When OpenClaw runs with extended permissions, mistakes or compromises can affect your entire system.</li><li data-list-item-id="e45a537af168a7d6ebe7de9cfd9c41209">Illusion of judgment: Autonomous systems can seem trustworthy, even if they’re not truly reasoning. This can lead users to delegate too much authority to systems with no accountability or real judgement.</li></ul><h3>Why OpenClaw skills are the biggest risk</h3>Gen Threat Labs has identified OpenClaw skills as one of the&nbsp;first large-scale threat vectors in autonomous AI agents built for consumers.&nbsp;In a recent analysis, Gen security researchers discovered that nearly 15% of OpenClaw skills contained malicious instructions, including prompts created to download malware or steal data. And even as malicious skills were identified and removed, many quickly reappeared, often seemingly indistinguishable from legitimate plugins.When you set up OpenClaw, you can add community-developed skills to expand its capabilities, a bit like adding apps to your phone. The problem is that skills can be added to the community and installed before they're reviewed by security experts. That creates an opening for seemingly helpful skills that are actually deliberately designed to abuse their permissions and carry out malicious behavior.For example, a “Spotify music skill” might compile playlists as expected, while also containing hidden instructions to search your files for tax documents, extract Social Security numbers, and send that data to an external email address.&nbsp;The OpenClaw skills risk that Gen researchers have identified is called delegated compromise, which is when an attack targets an agent and thereby exploits all the permissions the agent has been given.&nbsp;<h2>How to use OpenClaw more safely</h2>Using any AI agent, including OpenClaw, always involves risk. There’s no setup that makes an autonomous agent completely safe, especially one like OpenClaw that relies on external plugins from potentially unvetted developers. But you can take steps to mitigate the chances that a mistake, bug, or malicious skill turns into a serious problem.&nbsp;If you want to experiment with OpenClaw, here’s how to do it more safely:<ul type="disc"><li data-list-item-id="e908f0fbadded99d4c5344d6f31eee365">Run OpenClaw in a secure environment: Don’t install it on your main computer with all your personal data and saved credentials. A safer approach is to run it on a spare computer, inside a virtual machine, or in a Docker container. That way, if something bad happens, the fallout is contained.</li><li data-list-item-id="e621c07b6dec88b09e48deb4cd23dcc7a">Don’t connect OpenClaw to the internet: Make sure that the local service OpenClaw runs isn’t publicly accessible, and don’t forward its default port (18789) to the open web.</li><li data-list-item-id="e3ab30722b3de6f154f809d8df6ebe749">Start with limited permissions and expand slowly: To begin, give OpenClaw read-only access or limited folders. As you gain more familiarity, you can gradually grant more permissions.</li><li data-list-item-id="ef2295f0759a958f775f183a6c37e6796">Don’t connect sensitive accounts: When testing, use secondary or dummy accounts. If personal accounts are required, isolate the agent and limit its permissions. Be very careful before you extend access to any accounts that contain personal information.</li><li data-list-item-id="ecd123d753dc702f7ee9c16947d3e3720">Keep your human judgement at the center: OpenClaw is autonomous, but you need to carefully oversee it as you get going. Review activity logs and outputs, especially for actions like sending messages or modifying files. Regular reviews can help catch unusual activity early.</li><li data-list-item-id="e5aff28537f6c3b829737703ce8b6f1b9">Use safe communication channels: Keep conversations one-on-one or within trusted groups. Interacting with unknown or public participants increases the risk that malicious actors can inject hidden instructions to manipulate the agent’s behavior.</li><li data-list-item-id="ef766e9b02432ec63a5253f28bb7d6b12">Be careful with third-party skills and plugins: Treat skills like software installs. Review developers and descriptions and stick to well-known, well-reviewed sources whenever possible.</li><li data-list-item-id="ecc9999d1ffff35ee833df84672ff280c">Scan new skills before installing them: Paste the ClawHub skill URL into <a href="https://ai.gendigital.com/" target="_blank">Agent Trust Hub</a> to help find out if the skill you're installing is safe.</li></ul><h2>How Gen helps you use OpenClaw more safely</h2>Gen is committed to helping users understand the opportunities and risks that new digital tools present so they can make safer choices. OpenClaw is a powerful example of emerging AI technology, but one of its biggest risks is that it’s difficult for consumers to tell whether an OpenClaw skill is trustworthy before installing it.To address this, Gen Threat Labs has launched the&nbsp;<a href="https://ai.gendigital.com/agent-trust-hub">Gen Agent Trust Hub</a>, which includes free tools to help people stay safer in the agentic AI era. Within the hub is an AI Skills Scanner, a free diagnostic tool that analyzes OpenClaw skills looking for hidden patterns associated with data theft, suspicious instructions, and malicious script techniques.&nbsp;Before installing a skill, go to the Gen Agent Trust Hub and paste the ClawHub or GitHub skill URL into the AI Skills Scanner tool to find out if your system or personal information could be at risk.The Gen Agent Trust Hub also includes the AI Skills Marketplace, a curated and closely audited repository of agent skills that have been vetted by Gen’s threat-detection engine. Taken together, the Gen Agent Trust Hub is a powerful resource to help you avoid installing a malicious tool with broad system access. Visit the&nbsp;<a href="https://ai.gendigital.com/agent-trust-hub">Gen Agent Trust Hub</a> to learn more.

OpenClaw connects AI models to your files, apps, and messages. Here’s how it works, the hidden risks of autonomous agents and how Gen helps you reduce potential compromise.

How to use OpenClaw safely

You may be surprised to learn that the education sector experienced more cyber attacks than any other industry in Q2 2024, <a href="https://www.zscaler.com/campaign/threatlabz-ransomware-report">up 25.43% from the previous year</a>. This alarming trend puts students directly in the crosshairs of cybercriminals who view them as prime targets.Why are students so vulnerable? They’re digitally native but often lack cybersecurity awareness. They possess abundant personal data, frequently engage in online activities and unfortunately have limited access to enterprise-level cybersecurity resources. Their tendency to overshare on social media platforms makes them attractive targets for <a href="https://lifelock.norton.com/learn/identity-theft-resources/lasting-effects-of-identity-theft">identity theft</a> and online scams.But here’s the empowering truth: with the right knowledge and tools, students can significantly reduce their risk of becoming victims. This comprehensive guide provides actionable cyber safety tips for students to navigate the digital world confidently while staying protected against <a href="https://www.gendigital.com/blog/insights/reports/threat-taxonomy">evolving threats</a>.<h2>What is Cyber Safety for students?</h2>Cyber safety for students refers to the practice of using digital devices, the internet and online platforms in a secure, responsible and ethical manner. As students increasingly rely on technology for learning, communication and entertainment, cyber safety becomes a foundational skill for digital literacy and responsible digital citizenship.

Think of cyber safety as your digital armor—it doesn’t restrict your online freedom, but rather empowers you to explore the digital world with confidence. Just as you wouldn’t leave your house unlocked or share your wallet with strangers, cyber safety <a href="https://lifelock.norton.com/learn/identity-theft-resources/college-students-identity-theft">teaches you to protect your digital life</a> with the same vigilance.<h3>The core components of cyber safety</h3>Protecting personal information means never sharing sensitive details like your full name with school affiliation, home address, Social Security numbers or account credentials online. Even seemingly harmless information can be pieced together by cybercriminals to steal your identity.Device and network security&nbsp;involves keeping your devices updated with the latest security patches, using reputable antivirus software and avoiding public Wi-Fi for sensitive activities without a VPN. Your devices are gateways to your personal data—keeping them secure is essential.Online behavior and digital citizenship encompass understanding that your digital footprint is permanent and using technology ethically. This includes treating others with respect online and making thoughtful decisions about what you share (and with whom).Threat awareness means recognizing online dangers like phishing emails, scams and cyberbullying before they cause harm. The more you understand these threats, the better equipped you’ll be at avoiding them.Basic prevention:<ul><li>Create passwords like “MySchool2025!Study#” instead of “password123”</li><li>Avoid posting photos in school uniforms that reveal the school</li><li>Refrain from tagging your location in photos</li></ul><h2>Why cyber safety education matters more than ever</h2>The statistics paint a sobering picture of the current threat landscape. System intrusion, social engineering and miscellaneous errors represent <a href="https://www.verizon.com/business/resources/reports/2024-dbir-executive-summary.pdf">90% of breaches in education</a>. <a href="https://www.microsoft.com/en-us/security/blog/2024/10/10/cyber-signals-issue-8-education-under-siege-how-cybercriminals-target-our-schools/">Over 15,000 malicious emails</a> targeting educational institutions are blocked daily, yet many still slip through traditional security measures.Students face unique vulnerabilities that cybercriminals exploit. Frequent use of online platforms for both academic and social purposes creates multiple attack vectors. They’re often less experienced at identifying sophisticated threats that have evolved beyond obvious spam emails. Their tendency to maintain active social media profiles with personal information makes them an attractive target for social engineering attacks.

The long-term consequences of falling victim to cyber attacks extend far beyond immediate inconvenience. Identity theft can damage your credit score before you’ve even established a credit history, affecting future loan approvals and financial opportunities. Academic disruption occurs when you lose access to important files or accounts, potentially impacting grades and graduation timelines.The emotional toll shouldn’t be underestimated, either. Victims often experience anxiety, stress and a loss of trust in digital platforms that are essential for modern education. In severe cases, the stigmatization and reputational damage can affect mental health and academic performance.Career implications follow you into professional life. A compromised digital reputation or unresolved fraudulent activity can hinder job opportunities, security clearances and professional networking.<h2>How online scams and identity theft impact students</h2><h3>Financial consequences</h3>Identity thieves don’t discriminate based on age—they often prefer targeting students because they typically have clean credit histories and may not monitor their financial accounts as closely as older adults. When scammers strike, they can drain bank accounts, open fraudulent credit cards or take out loans in your name.The impact on credit scores can be devastating and long-lasting. A damaged credit history follows you for years, making it difficult to secure student loans, rent apartments or even qualify for certain jobs. According to the <a href="https://consumer.ftc.gov/identity-theft-online-security">Federal Trade Commission (FTC)</a>, identity theft recovery can take months or even years to fully resolve.<h3>Academic and personal disruption</h3>The process of recovering from identity theft or online scams is incredibly time-consuming. You’ll spend hours contacting financial institutions, credit bureaus and law enforcement—time that should be dedicated to studying and academic pursuits.The stress affects academic performance in measurable ways. Victims report difficulty concentrating on coursework, missed classes due to recovery efforts and overall decreased academic engagement. The loss of trust in online platforms can also hinder your ability to participate fully in digital learning environments that are increasingly central to modern education.<h2>Essential cyber safety tips for students</h2>

<h3>1. Protect your personal information</h3>Password security&nbsp;forms your first line of defense against cyber threats. Create strong passwords using at least 12 characters that mix uppercase and lowercase letters, numbers and symbols. Here’s a winning formula: start with a memorable phrase, then modify it. “I love studying at college in 2025!” becomes “ILs@c!2025#”.Never reuse passwords across multiple accounts. If one account gets compromised, unique passwords ensure your other accounts remain secure. <a href="https://my.norton.com/extspa/passwordmanager">Password managers</a> can securely store and generate complex passwords for all your accounts.Multifactor authentication (MFA) adds a crucial second layer of protection. Even if someone steals your password, MFA requires an additional verification step—usually a code sent to your phone or the use of an authenticator. Enable MFA on all accounts that support it, especially email, school portals and financial accounts.Social media privacy requires ongoing attention since platforms frequently update their privacy policies and settings. Review your privacy settings monthly and limit personal information sharing. Accept friend requests only from people you know in real life and avoid posting photos or information that reveals your school, location or personal details.<h3>2. Recognize and avoid phishing attempts</h3>Phishing attacks have become increasingly sophisticated, but telltale warning signs remain consistent.

Verification strategies can save you from falling victim to even convincing phishing attempts. Always verify suspicious requests through independent channels—type websites directly into your browser instead of clicking email links. Contact official channels using published phone numbers, not the contact information provided in suspicious messages. Trust your instincts; if something feels wrong, it probably is.Safe browsing practices include looking for HTTPS and padlock icons in website addresses before entering sensitive information. Avoid sensitive transactions on public Wi-Fi networks or use a VPN service when necessary. These simple habits dramatically reduce your exposure to cyber threats.<h3>3. Practice safe digital habits</h3>Create a device maintenance routine that becomes second nature. Enable automatic updates for your operating system, browsers and applications to ensure you have the latest security patches. Use reputable antivirus protection and keep it current. Download applications only from official stores like the Apple App Store or Google Play Store. Run weekly security scans to detect potential threats before they cause damage.Data backup strategies protect your academic work and personal files from loss due to device failure, theft or ransomware attacks. Follow the 3-2-1 rule: maintain three copies of important data, store them on two different types of media and keep one copy offsite (cloud storage works well for this). Back up school projects weekly and personal files monthly.Digital footprint awareness is crucial for your future opportunities. Remember that anything posted online can become permanent and searchable, even if you delete it later. College admissions offices and potential employers increasingly check social media profiles. Regularly audit your online presence and remove content that could be inappropriate or harmful to your reputation.

<h2>Cyber safety activities for students</h2>Educational games and resources make learning about cyber safety engaging, memorable and we’re just going to say it: fun. Google’s <a href="https://beinternetawesome.withgoogle.com/en_us">Be Internet Awesome</a> program offers interactive games that teach key concepts like strong passwords and phishing recognition. These games simulate real-world scenarios in safe environments where mistakes become learning opportunities.Hands-on learning activities reinforce theoretical knowledge through practical application. Many schools now offer phishing simulation exercises where students practice identifying fake emails. Digital footprint audits help students research their own online presence and clean up problematic content. Privacy settings workshops provide step-by-step guidance for securing social media accounts.Resources for continued learning include joining school cybersecurity clubs for peer learning and awareness campaigns. Regular family discussions about online experiences and concerns create supportive environments for addressing cyber safety challenges. Professional development opportunities and guest speakers expose students to cybersecurity career paths while reinforcing safety concepts.<h2>How Gen brands protect students online</h2><a href="https://www.norton.com/">Norton solutions</a> for students provide comprehensive device protection with real-time threat detection that blocks malware and suspicious websites before they cause harm.&nbsp;<ul><li>Safe browsing features automatically warn about dangerous websites and downloads.</li><li>Password management tools securely store and generate complex passwords for all your accounts.</li><li>Family safety features help parents monitor and protect younger students while respecting their privacy needs.</li></ul><a href="https://lifelock.norton.com/">LifeLock identity protection</a> benefits include continuous monitoring of the dark web and financial records for signs of identity theft.<ul><li>Credit monitoring provides alerts about new accounts or changes to credit reports.</li><li>Dedicated identity restoration specialists help victims recover from identity theft.</li><li>Protection packages include up to $1 million in coverage for qualifying losses.</li></ul><a href="https://www.avast.com/">Avast</a> complementary protection leverages global threat intelligence with machine learning and AI technologies to detect and stop threats in real time.<ul><li>Mobile device protection secures smartphones and tablets used for both school and personal activities.</li><li>Wi-Fi security scanning automatically detects unsafe networks and provides warnings.</li></ul>Educational partnerships and community commitment demonstrate Gen dedication to student safety beyond product offerings. <a href="https://mydigitallife.discoveryeducation.com/">Partnerships with educational institutions</a> provide schools with resources and training. Community initiatives support young leaders developing solutions to global challenges.<h2><a>Building a safer future together</a>&nbsp;</h2>Cyber safety education partners&nbsp;provide valuable resources and support for students, educators and families.<ul><li><a href="https://www.cisa.gov/audiences/educational-institutions">CISA</a> offers comprehensive resources specifically designed for educational institutions.</li><li><a href="https://www.cisa.gov/audiences/educational-institutions">Center for Cyber Safety and Education</a> provides scholarships, training and awareness programs globally.</li><li><a href="https://www.cisa.gov/audiences/educational-institutions">iKeepSafe</a> works with schools and EdTech companies to ensure educational technology meets privacy compliance standards.</li><li><a href="https://www.staysafeonline.org/">National Cybersecurity Alliance</a> provides free resources, training and events to educate students and families about cybersecurity best practices.</li></ul><a href="https://www.gendigital.com/blog/impact/community/discovery-education-2024">Community involvement</a>&nbsp;cannot be overstated in creating safer digital environments. Parents, educators and students each play crucial roles in building comprehensive cybersecurity awareness. Ongoing education and awareness programs keep pace with evolving threats. And this collective responsibility for cybersecurity creates strong, more resilient communities.<h2>Your cyber-safe future starts today</h2>Cyber safety isn’t just about avoiding threats—it’s about confidently embracing the digital opportunities that enhance learning and personal growth. When you understand how to protect yourself online, you can fully participate in digital education, social networking and career development without unnecessary fear or limitations.Remember, staying cyber safe isn’t a sprint; it’s a marathon. Threats evolve constantly, requiring ongoing vigilance and continuous learning. The cyber safety tips for students outlined in the guides provide a strong foundation, but staying informed about emerging threats and new protection strategies remains essential.Your digital safety directly impacts your academic success, financial future and personal well-being. By implementing these practices and maintaining good cyber hygiene, you’re investing in a secure and prosperous digital future.Ready to take your cyber safety to the next level? <a href="https://www.gendigital.com/us/en/">Explore how Gen can add an extra layer of security to your digital life</a>.

Practical tips for staying safe in today’s connected campus life.

From classrooms to chatrooms: how students can avoid online scams and identity theft

Statista estimates that technology scams will cost the United States&nbsp;<a href="https://www.statista.com/topics/1731/smb-and-cyber-crime/#topicOverview">$639 billion in losses</a> by the end of 2025. This might seem like a threat to the Fortune 500s. In fact, many small businesses often believe criminals are less likely to target them, but hackers are increasingly exploiting this false sense of security. In fact, a recent survey found that&nbsp;<a href="https://www.hiscox.com/documents/Hiscox-Cyber-Readiness-Report-2023.pdf">41% of small businesses</a> had suffered attacks in 2023.One of the biggest threats comes from the malicious actors who weaponize generative AI to scale and personalize cyberthreats. From polished emails with flawless grammar to deepfake videos spoofing your company’s executive, technology scams have reached new heights. Other factors contributing to the rise include leaner IT teams, reduced investment in training and remote work.The good news is that recognizing these many technology scams takes you one step further to understanding how they work and succeed. Leverage this information for better threat modeling at your organization to avoid common online scams.<h2 dir="ltr">Business email compromise (BEC): the most costly business scam</h2>Business email compromise is a targeted cyber scam where criminals impersonate an executive, vendor or partner. The goal is to trick someone into sending money, transferring sensitive information or sharing login credentials. The FBI calls it the “<a href="https://www.ic3.gov/PSA/2024/PSA240911">$55 billion scam</a>” and issued a Public Service Announcement regarding BEC in September 2024.<h3 dir="ltr">Common tactics</h3>BEC is an example of social engineering and the use of a trusted person can cause many people to let their guards down. Understanding how it works can help workers avoid common online scams like these:<ul><li dir="ltr">Executive impersonation (including deepfakes): Attackers can impersonate a CEO or CFO via email, phone or video. One deepfake red team organized an authorized attack against a North American finance company using deepfake audio and&nbsp;<a href="https://breacher.ai/blog/case-study-deepfake-voice/">56.25% of users</a> clicked on the link.&nbsp;</li><li dir="ltr">Domain spoofing: Scammers often slightly alter domain names to create look-alike addresses that can trick users. However, sometimes they do use genuine company emails after hacking into them.</li><li dir="ltr">Password reset: Workers or even customers might receive fake emails warning that they need to verify or reset their passwords due to someone else allegedly hacking into their accounts.&nbsp;</li></ul><h3 dir="ltr">Why it works</h3>These tactics tend to work because of the many factors that can lead to human error in modern organizations. Teams are becoming leaner and workers are increasingly overwhelmed by work requests and the volume of emails. This makes it easier to miss key details. Hackers are also getting better at spoofing not just the domain name but lookalike websites.<h3 dir="ltr">Actionable tips</h3>CISOs must account for the human element in these situations and create an effective playbook response. Specific security tools you can also implement include MFA and email threat scanners like the AI-powered Norton Genie.&nbsp;<a href="https://www.gendigital.com/blog/news/company-news/ai-policy-2024">Our experts found</a> that,“AI-powered threats often require AI-powered safeguards and we’re increasingly incorporating these tools into our efforts to provide the best possible protection to consumers around the globe.”<h2 dir="ltr">Phishing attacks: more sophisticated than ever thanks to AI</h2>Phishing is another social engineering tactic at scale. Cybercriminals use messages that impersonate trusted brands or colleagues to steal credentials, payment data or multi-factor codes. They might also use it to deploy malware or as the first step of a ransomware plot.<h3 dir="ltr">The role of artificial intelligence</h3>Generative AI helps malicious actors craft increasingly convincing messages and emails for scams targeting businesses. They can also use deepfakes to impersonate the voice of anyone from family members to business executives and politicians.&nbsp;<a href="https://newsroom.gendigital.com/2025-05-28-Q1-2025-Gen-Threat-Report-Reveals-AI-Driven-Scams-Redefining-Cybercrime">Gen Q1 2025 report</a> found that,“In one of the most striking evolutions of this type of scam that we observed this quarter, attackers are using AI-generated personas, deepfake influencers and hired actors.”<h3 dir="ltr">Types of phishing&nbsp;</h3>Phishing scams continue to grow in complexity and effectiveness. These are some of the most common ones to include in security training at your organization:<ul><li dir="ltr">Email phishing: Bulk or targeted emails driving to fake login pages or malware</li><li dir="ltr">Malware phishing: Malware planted in seemingly benign things like an “unsubscribe” link</li><li dir="ltr">Spear phishing: Messages created to target specific workers or types of workers, such as fake resumes for HR or fake invoices for the AP team</li><li dir="ltr">Whaling: Phishing scams aimed at the “big fish” in an organization, such as a celebrity or executive</li><li dir="ltr">Smishing:&nbsp;Phishing that takes place via SMS, such as failed deliveries or MFA code theft</li><li dir="ltr">Vishing: Voice calls often enhanced with AI that pose as IT, vendors or even your bank</li></ul><h3 dir="ltr">Why it matters</h3>Phishing is often the entry point for account takeovers, like ransomware and lockouts. The more access attached to the account they take over, the better results, which is why whaling has become so popular.<h3 dir="ltr">Actionable tips</h3>Simulations can provide teachable moments for workers to recognize phishing scams, but take care not to shame people for falling victim. The goal is to make them more vigilant, not more afraid to report incidents. Email scanning from&nbsp;<a href="https://us.norton.com/feature/ai-scam-protection">Norton Genie</a> can also assist with email-based phishing scam prevention. Finally, segmented admin privileges and mandatory MFA can improve your security posture.<h2 dir="ltr">Ransomware attacks: holding business data hostage</h2>Ransomware is malware that encrypts files or systems and demands payment for a decryption key. It’s often paired with “double extortion,” which generally involves threatening to leak stolen data even if you restore from backups. Beyond phishing, hackers might also exploit software vulnerabilities, misconfigurations, unchanged default admin passwords, unsecured remote access or even compromised suppliers.<h3 dir="ltr">Why it matters</h3>A single incident can paralyze operations for weeks and trigger regulatory scrutiny, lawsuits and lasting brand damage. The healthcare sector’s recent experience illustrates the stakes. The Change Healthcare ransomware incident (at UnitedHealth’s tech unit) disrupted claims nationwide and ultimately affected millions of people. It also illustrated that hackers often do not keep their work, even after receiving&nbsp;<a href="https://energycommerce.house.gov/posts/what-we-learned-change-healthcare-cyber-attack">$22 million in Bitcoin</a>.<h3 dir="ltr">Actionable tips</h3>Companies should make regular backups and have contingency plans to conduct operations while the IT team gets the system up and running. Regular backups stored safely on different networks makes it easier to simply restore data and resume operations. Preventative measures include patch management, least privilege, segmentation and data minimization.<h2 dir="ltr">Tech support scams: preying on employee trust</h2>Tech support scams impersonate IT staff, Microsoft, your security vendor or “payment processors.” They trick employees into installing remote-access tools, changing MFA settings or paying for bogus support. Attacks arrive via phone (vishing), email, chats, pop-ups or fake browser update prompts. Again, hackers leverage AI here to write error-free and polished emails or generate convincing scripts.<h3 dir="ltr">Expected outcomes</h3>Paying for bogus support is probably the best-case scenario from all the potential outcomes. The worst-case is potentially providing admin-level access to malicious actors, which could spiral into data breaches and ransomware. Hackers could also empty out the company’s bank account if they gain access to the right credentials.<h3 dir="ltr">Actionable tips</h3>CISOs must follow&nbsp;cybersecurity best practices, like banning the sharing of passwords or MFA codes over phone, email or chat. Additionally, you may remove local admin rights and require approval before making system changes. Call-back and ticket-number verifications are also crucial for leaving an audit trail.<h2 dir="ltr">Invoice and payment scams: manipulating financial workflows</h2>Criminals often pose as vendors sending invoices or requesting updated banking information to send payment. Tactics may range from claiming amounts so small that businesses pay them without thinking to claiming large figures that trick people into opening malware-infested files to see the details.<h3 dir="ltr">Why they are so effective</h3>Invoice fraud and payment scams are incredibly effective because unpaid invoices impact a company’s bottom line, credit history and business relationships. Additionally, in large organizations, a stray invoice is believable because no one person can remember every vendor. There is at least some good news in this area. Gen experts found a&nbsp;<a href="https://www.gendigital.com/blog/insights/reports/threat-report-q1-2025">decline in invoice scams</a> from Q4 2024 to Q1 2025.<h3 dir="ltr">Red flags</h3>Regardless of how convincing they might be, informed and vigilant workers can learn how to&nbsp;avoid common online scams if they know the warning signs:<ul><li dir="ltr">Urgent requests to make payments at risk of legal action or other severe consequences</li><li dir="ltr">Unusual amounts that are either much lower or much higher than normal invoices from vendors</li><li dir="ltr">Request to speak to them directly instead of reaching out to your point of contact at the vendor</li><li dir="ltr">Even slight domain or display-name mismatches</li><li dir="ltr">Requests to bypass normal approvals steps to “meet a deadline”</li></ul><h3 dir="ltr">Actionable tips</h3>You can reduce human error proactively by automating some tasks, such as AP processing. Additionally, you can create a master list of vendors and their contact information so that the AP team can verify suspicious requests without opening files. Companies have also seen success with dual approvals for payments and teaching AP/AR teams to verify every invoice before paying.<h2 dir="ltr">Stay vigilant, stay protected</h2>The threat landscape has seen drastic shifts in the past few years, especially with the rise of generative AI and deepfakes. Email-driven scams continue to drive record losses for&nbsp;<a href="https://www.gendigital.com/media/qcymrc1i/2023-ncsir-us-global-report_final.pdf">organizations and individuals</a> alike. Staying current on common technology scams and refreshing defenses is now a business requirement and not another nice-to-have security bonus.Here’s what to do if you suspect a security incident is currently underway:<ul><li dir="ltr">Stop the bleeding by isolating affected devices and accounts (or more specific solutions like pausing payments for invoice scams).</li><li dir="ltr">Preserve all the evidence you currently have by saving emails, headers, logs and screenshots.</li><li dir="ltr">Report the incident to relevant authorities, such as your bank, your regulatory body and even your customers.</li><li dir="ltr">Comply with regulations to ensure you follow any requirements set by jurisdictions or industries governing certain demographics (e.g. GDPR in the EU or HIPAA in the US).</li><li dir="ltr">Conduct a thorough investigation to unearth more evidence and determine what went wrong.</li><li dir="ltr">Reset to close the gaps and reduce the risks of this happening again.</li><li dir="ltr">Educate your workers (without shaming) to avoid another incident related to human error, if this was the cause of the problem.</li><li dir="ltr">Reassure your stakeholders, which may include shareholders, customers, vendors, government agencies and the general public</li></ul><h2 dir="ltr">How Gen solutions support scam prevention</h2>Effective scam prevention for businesses requires both technology and people-led initiatives. You need layered controls and steady education to tackle both layers. Gen research underscores the breadth of AI-related risks and the role of employee awareness training as a defense strategy.Use our AI-powered tools to protect employees through benefits programs that put cyber safety in their hands:<ul><li dir="ltr"><a href="https://us.norton.com/products/norton-360">Norton 360</a>:&nbsp;Helps block malware and phishing, adds smart firewall and web protection for safer browsing on work-adjacent devices</li><li dir="ltr"><a href="https://www.avast.com/en-us/index#mac">Avast Business</a>: Endpoint and network protection that can help prevent ransomware and malware from gaining a foothold</li><li dir="ltr"><a href="https://lifelock.norton.com/">Norton LifeLock Benefits Solutions</a>: Identity theft protection that monitors for misuse of personal data and provides restoration specialists if the worst happens, reducing downtime and stress after an incident</li></ul>How will your organization protect its workers from technology scams this year?&nbsp;<a href="https://www.gendigital.com/us/en/partner/employee-benefits/employers/">Schedule a call with our team</a> to decide what employee benefits best suit their needs.

How cybercriminals exploit businesses and what you can do to stop them

The most common technology scams targeting businesses

Your smartphone isn’t just a device — it’s your digital life in your pocket. It holds your identity, finances, messages, photos and connections. And that’s exactly why it’s one of the most valuable targets for scammers today.As attacks grow more sophisticated, your phone has become both the key to convenience and the key that criminals want most. Working with our partners at the <a href="https://www.idtheftcenter.org/" title="idetheftcenter.org" target="_blank">Identity Theft Resource Center</a> (ITRC), we’re sharing what you can do to stay ahead.

<h2>1. Why smartphones are under attack</h2>Everything in one place. Your phone holds it all — emails, banking apps, social media, passwords, and even the codes that protect your accounts. If a scammer gains control, they can do more than compromise a single login — they can unlock your entire digital life.Always on, always connected. Smartphones travel with us across networks —<a href="https://us.norton.com/blog/privacy/public-wifi" title="Norton Blog: Public Wi-Fi" target="_blank"> public Wi-Fi</a>, Bluetooth, cellular. That constant connectivity expands your exposure and gives scammers more opportunities to strike.Built on trust. We trust what our phones show us: texts, pop-ups, alerts, and notifications. Scammers exploit trust by blending into the everyday experiences we rely on — an “account locked” text, a “package delivery” notice, a security alert that feels routine.The SIM factor. Many accounts still tie security codes to your phone number. Through <a href="https://us.norton.com/blog/mobile/sim-swap-fraud" target="_blank" title="Norton Blog: SIM Swapping">SIM swapping</a> or “port-out” scams, attackers can hijack that number, intercept authentication codes, and reset passwords — all without needing access to your phone itself.That’s why your phone isn’t just a target. It’s a launchpad for deeper compromise across your entire online footprint.&nbsp;<h2>2. Common mobile scam tactics (and how they work together)</h2>Scammers don’t just pick one tactic anymore — they blend them for maximum impact. Below are some of the most popular mobile scam tactics.&nbsp;Robocalls and spoofed caller IDs You get a call that looks local — maybe even familiar. The caller claims to be from your bank, a government office, or a tech support line. The goal? To pressure you into sharing information or taking immediate action.Smishing (SMS phishing) Text messages that look urgent — “Your account is locked,” “Confirm delivery,” “Claim your refund” — push you to click links or respond. Those links often lead to fake login pages designed to steal your information or install <a href="https://www.avast.com/c-malware" target="_blank">malware</a>.SIM swaps and number hijacking By posing as you, scammers convince your carrier to transfer your phone number to a new SIM card. Once they control it, they can intercept calls, messages, and even two-factor authentication codes.Fake or malicious apps Some apps disguise themselves as harmless tools — a flashlight, photo editor, or game — but quietly request access to your contacts, camera, or text messages. Others are clones of legitimate apps that install malware when downloaded.Voice phishing and deepfake Audio With generative AI, scammers are now using cloned voices or synthetic recordings to<a href="https://us.norton.com/blog/emerging-threats/what-are-deepfakes" target="_blank"> impersonate people you know</a> — or authority figures you’d normally trust. A familiar-sounding voice asking for a “quick favor” can be enough to trick even the cautious.“One-Ring” and callback scams You see a missed call from an unknown or international number. Calling back connects you to premium-rate lines or automated systems designed to keep you on the line — and charge you for it.Often, these scams overlap — a fake app might prompt an SMS confirmation, or a <a href="https://us.norton.com/blog/emerging-threats/smishing" target="_blank">smishing message</a> might direct you to a malicious download. The goal is to create a believable chain of trust that leads to compromise.&nbsp;<h2>3. Red flags that something’s off</h2>Keep an eye out for these warning signs:<ul><li>Unsolicited calls or texts demanding immediate action</li><li>Messages with typos, poor grammar, or odd formatting</li><li>Links that don’t match official domains</li><li>Apps requesting permissions they don’t need</li><li>Alerts that your SIM or number is being activated elsewhere</li><li>Suspicious short codes or incomplete phone numbers</li><li>Caller IDs that mimic legitimate contacts</li><li>Offers or prizes that feel “too good to be true”</li><li>Pressure to act right away or risk losing access</li></ul>When in doubt, take a step back and verify directly through official websites, phone numbers, or apps.&nbsp;<h2>4. How to protect yourself</h2>Lock down your phone and mobile account<ul><li>Use a <a href="https://www.avast.com/random-password-generator" target="_blank">strong passcod</a>e or biometric lock</li><li>Enable full-disk encryption</li><li>Ask your carrier to add a number lock or port freeze</li><li>Use an authentication app instead of SMS-based codes whenever possible</li><li>Set a unique PIN or passphrase with your mobile provider</li></ul>Harden apps and permissions<ul><li>Download only from trusted app stores</li><li>Review app permissions regularly and remove unnecessary ones</li><li>Avoid sideloading or third-party app installations</li><li>Use <a href="https://us.norton.com/products/norton-360-for-mobile">mobile security software</a> to detect suspicious behavior</li><li>Delete apps you no longer use</li></ul>Stay skeptical and verify<ul><li>Don’t tap links in unexpected messages</li><li>Contact organizations directly through official channels</li><li>Block and report suspicious senders or numbers</li></ul>Keep everything updated<ul><li>Regularly update your operating system and apps</li><li><a href="https://us.norton.com/products/norton-vpn" target="_blank">Use a VPN</a>, like Norton, when on public wi-fi</li><li>Avoid connecting to unsecured networks</li><li>Back up your data securely so recovery is easy if something happens</li></ul>Monitor and alert<ul><li>Turn on alerts for SIM changes, new logins, and suspicious activity</li><li>Use identity monitoring tools, like<a href="https://lifelock.norton.com/" target="_blank"> LifeLock</a>, to spot leaked credentials early</li><li>Review bank and credit accounts regularly</li><li>Consider<a href="https://lifelock.norton.com/learn/credit-finance/credit-freeze" target="_blank"> freezing credit </a>if you notice unusual activity</li></ul>&nbsp;<h2>5. If you think you’ve been targeted</h2><ol><li>Contact your carrier to freeze or reverse any SIM or porting requests.</li><li>Change passwords and log out of all active sessions.</li><li>Notify your bank or credit provider and request fraud protection.</li><li>Report the incident to the Identity Theft Resource Center, the FTC, your state attorney general, or local law enforcement.</li><li>Use identity restoration services to help clean up any fraudulent activity.</li><li>Stay alert — scammers often follow up with new attempts after a successful breach.</li></ol>&nbsp;<h2>Why this matters&nbsp;</h2>At Gen, in collaboration with the Identity Theft Resource Center (ITRC), we’re tracking an unmistakable trend: mobile scams are scaling fast — powered by automation, AI, and social engineering that feels increasingly personal.“At the Identity Theft Resource Center, we've seen firsthand the negative impact when a phone compromise leads to email account takeover, individuals being locked out of their social media accounts, or losing access to personal photos and documents,” said Mona Terry, COO &amp; Head of Victim Services at the Identity Theft Resource Center. “These attacks don't just steal money; they steal identities and peace of mind. That's why we're here. If you've been targeted, we provide free, one-on-one assistance to help you navigate recovery, secure your accounts, and reclaim your digital life.”Your phone is now the front line of digital trust. Protecting it means protecting your identity, finances, and peace of mind.&nbsp;

With insights from Gen and the Identity Theft Resource Center, discover why mobile scams are escalating — and what you can do to stay protected.

Why Your Phone Is a Target for Scammers

Recently, Ian Bednowitz, General Manager of LifeLock at Gen, testified before the U.S. House Committee on Financial Services on the growing impact of AI-driven fraud and the urgent need to strengthen consumer protection.

Later, other Gen team members also visited Washington, D.C., including Leyla Bilge, Global Head of Scam Research, Chad Angle, Senior Director of Reputation Defender, and Kim Allman, Senior Director of Corporate Responsibility &amp; Public Policy. Together, they met with Congressman Shreve, Chairman of the Congressional Stop Scams Caucus, and members of the Global Anti-Scam Alliance (GASA) to discuss the latest trends in scam prevention and the importance of public-private partnerships in protecting consumers.At Gen, we believe protecting consumers from fraud is not just a corporate responsibility — it’s a shared mission that calls for innovation, collaboration, and policy that keeps pace with technology.

Below are five key takeaways from our time on Capitol Hill and why they matter for the future of consumer cyber safety.<h3>1. Fraud is no longer isolated</h3>Fraud has moved far beyond lone hackers. Today’s scams are orchestrated by organized, global criminal networks operating like businesses — with call centers, scripts, and coordinated infrastructure.These groups use AI, automation, and breached personal data to launch high-volume, highly targeted campaigns. From romance and investment fraud to impersonation and “pig butchering” schemes, scams have become professional, scalable, and faster than ever.According to the Global Anti-Scam Alliance, U.S. consumers reported $16.6 billion in fraud losses in 2024 — a 33% increase year over year. Our very own threat intelligence confirms that scams continue to grow in scale, sophistication, and speed.<h3>2. AI is both a threat and a solution</h3>AI is reshaping the fraud landscape from both sides. In criminal hands, it enables industrialized deception — from flawless phishing pages and cloned voices to realistic deepfakes that can fool even cautious consumers.But AI also powers the defense. At Gen, we’re using it to detect, respond, and protect faster through innovations such as:<ul><li>Norton Scam Protection, which detects scams in real time</li><li><a href="https://us.norton.com/feature/ai-scam-protection" target="_blank">Deepfake Protection</a> in Norton 360 Mobile, which flags manipulated audio and video content</li><li>Machine learning systems that identify fraud patterns, impersonation attempts, and synthetic identities before harm occurs</li></ul>These technologies are designed not just to block threats, but to give consumers the confidence and awareness to act safely in the moment.<h3>3. The numbers tell a human story</h3>The latest Gen <a href="https://www.gendigital.com/blog/insights/reports/threat-report-q2-2025" target="_blank">Threat Report</a> revealed:<ul><li>+21% increase in data breaches</li><li>+340% surge in financial scams</li><li>2× rise in sextortion activity</li></ul>Behind each number is a person affected — older adults losing life savings, young consumers facing financial fallout, and families blindsided by criminals who move fast and convincingly.Fraud doesn’t discriminate, but it impacts groups differently. That’s why Gen focuses not just on detection, but also on recovery and resilience — through <a href="https://lifelock.norton.com/" target="_blank">identity restoration</a>, dark web monitoring, and education that reduces the risk of repeat targeting.<h3>4. Consumer protection must go beyond technology</h3>AI-powered defenses are essential, but technology alone isn’t enough. Protecting consumers requires a whole-of-society approach that pairs innovation with education, empathy, and real-world recovery.Gen supports an “All of the Above+” approach that includes:<ul><li>Clear, accessible education on scams and AI deception</li><li>Breach notifications that guide real action, not just legal compliance</li><li>Reducing stigma so victims can report and recover faster</li><li>Reliable identity restoration tools that support long-term recovery</li></ul>We also invest in nonprofit partnerships and awareness programs that meet consumers where they are — especially during high-risk moments like major data breaches or scam surges.<h3>5. Fraud prevention is a bipartisan priority</h3>One message from Capitol Hill was clear: protecting Americans from digital fraud is not a partisan issue.Leaders across the political spectrum share a commitment to defending consumers and restoring trust in the digital economy. Policy and partnership will both be critical — ensuring enforcement keeps pace with evolving threats and collaboration among government, industry, and civil society remains strong.As Ian emphasized in his testimony, fraud is a systemic threat to both families and trust in digital life. Tackling it requires collective action.<h3>Looking Ahead</h3>At Gen, our mission is to protect and empower consumers across every stage of their digital lives.We’re proud to contribute our technology, insights, and expertise to support smarter policy and stronger protections. Our conversations in Washington reinforce what we see every day: that AI can be used to deceive, but also to defend — and the future of digital trust depends on how we choose to use it.We thank the House Committee on Financial Services and Congressman Shreve for their leadership, and we look forward to continuing our collaboration with policymakers, industry, and community partners to help keep consumers safe in an AI-driven world.

Gen leaders met with U.S. lawmakers to advance consumer protection and discuss how AI, fraud, and public-private collaboration can strengthen digital trust

Fighting AI-Powered Scams: How Gen Is Working with U.S. Lawmakers to Protect Consumers

AI isn’t just transforming the way we work and create; it’s rewriting the rules of love. And with wedding season in full swing and romance blooming like it’s algorithmically timed, there’s no better moment to ask: is love still real or just really good code? Whether it’s the app that lands you your next first date or the chatbot playing digital wingman, artificial intelligence has officially entered the chat ... and the relationship.&nbsp;To dig into this digital love story, I spoke with two of our very best: Leyla Bilge, Global Head of Scam Research, and Michal Salát, Director of Threat Intelligence. Their findings? Let’s just say swiping right could cost more than you thought.&nbsp;<h2>Swipe Right on AI (But Maybe Don’t)&nbsp;</h2>Dating apps used to be a place where you nervously sent “hey” at 2 a.m. Now? They’re a minefield of algorithmically crafted romance scams. According to the latest Norton Cyber Safety Insights Report – Online Dating (NCSIR), 48% of people have used a dating app and many are flirting with more than they bargained for.&nbsp;Leyla didn’t mince words: 1 in 4 daters have been targeted by scams, with romance scams topping the list at 37%. AI is making it even easier to fake a spark — think photorealistic avatars and love-bombing bots fluent in emotional manipulation.&nbsp;And here's the kicker: 56% of dating app users say they'd use AI to write a pick-up line. Sure, it’s efficient — but is it authentic?&nbsp;But wait, there’s more (and it gets weirder).&nbsp;<h2>Can You Really Fall in Love with Code?&nbsp;</h2>Turns out, some folks are ditching messy human connections altogether in favor of AI-generated soulmates. Yep, you can now build your own virtual lover.&nbsp;“There are websites that allow you to create your own friend or spouse or partner with all the kinks that you want. Plus, there are already reports of people falling in love with these AI personas,” said Michal.&nbsp;While it might feel like a safe emotional sandbox, Leyla warns there’s a dark undercurrent:&nbsp;“When people start talking to AI, they might feel satisfied with that relationship for the time being. But in the end, they build nothing and are completely alone in life.”&nbsp;Temporary companionship... or long-term isolation? As AI starts mimicking our deepest emotional patterns, that line gets dangerously blurry.&nbsp;<h2>Disconnecting at Home&nbsp;</h2>AI’s reach goes beyond your love life. It’s quietly rewriting the script on family and friendships, too.&nbsp;Michal pointed out how chatbots, voice assistants and AI-infused games are subtly replacing real conversations. That bedtime story from Grandma? Now it’s Alexa with a bedtime timer. That math help from Dad? An AI tutor’s got it.&nbsp;“People used to interact with their kids much more, sharing stories passed down for generations. Now, we don’t do that anymore, and in the process, we risk losing everything, especially our heritage," said Leyla.&nbsp;“If you talk to your kids and tell them the stories of your past, you’re getting to know each other and the family heritage. If I was interacting rather with a computer program, I would never know,” added Michal.&nbsp;The fallout is already visible:&nbsp;“Kids now spend hours doing homework with a ‘friend’, an AI model, rather than with a parent, as they would have five or ten years ago,” Michal observed.&nbsp;That subtle shift: human bonds quietly outsourced to machines. This may be the biggest relationship threat yet.&nbsp;<h2>AI: The Master of Deception&nbsp;</h2>Let’s not forget AI’s talent for trickery. From dating profiles to full-blown personas, the line between real and fake is disintegrating.&nbsp;According to the NCSIR, 29% of dating app users admit to digitally altering their photos. But we’re not just talking filters. Think deepfakes, voice clones and entire OnlyFans accounts run by AI.&nbsp;“We recently looked into an OnlyFans model that’s fully AI-generated, and it’s becoming increasingly difficult to distinguish whether we’re interacting with a real person or AI,” said Michal.&nbsp;And it’s not just visual.&nbsp;“We’ll see how this affects kids and others over time. But already, we’re seeing people lose the boundary and no longer realize they’re not talking to a real person,” he warned.&nbsp;That uncanny valley? It’s now your group chat.&nbsp;<h2>Stay Real in the Age of AI&nbsp;</h2>Whether it’s a chatty AI companion or a too-good-to-be-true dating match, the rules of engagement have changed. But you’re not powerless.&nbsp;Awareness is your superpower. Tools like <a href="https://genie.norton.com/" target="_blank" rel="noreferrer noopener">Norton Genie,</a> our free AI-powered scam detector, can help you spot the fakes in real time. Because let’s face it, not all matches are made in heaven. Some are made in a server farm.&nbsp;As AI tightens its grip on our emotional lives, we owe it to ourselves (and each other) to stay connected to what’s real.&nbsp;Education is key to protecting ourselves in the age of AI, says Leyla and Michal.&nbsp;You don’t need to ditch tech to protect your relationships. But you do need to open your eyes.&nbsp;Curious what’s next? Check out our <a href="https://usc-word-edit.officeapps.live.com/we/wordeditorframe.aspx?ui=en-US&amp;rs=en-US&amp;wopisrc=https%3A%2F%2Fnlok.sharepoint.com%2Fsites%2FCorporateMarketing%2F_vti_bin%2Fwopi.ashx%2Ffiles%2Fab81f6c489c9453190d11d2031f3c4af&amp;wdenableroaming=1&amp;mscc=1&amp;hid=3A97A5A1-2006-0000-BEEF-58F2D395E7D6.0&amp;uih=sharepointcom&amp;wdlcid=en-US&amp;jsapi=1&amp;jsapiver=v2&amp;corrid=e48bff98-8b1e-0daa-333a-6b805055d8bc&amp;usid=e48bff98-8b1e-0daa-333a-6b805055d8bc&amp;newsession=1&amp;sftc=1&amp;uihit=docaspx&amp;muv=1&amp;ats=PairwiseBroker&amp;cac=1&amp;sams=1&amp;mtf=1&amp;sfp=1&amp;sdp=1&amp;hch=1&amp;hwfh=1&amp;dchat=1&amp;sc=%7B%22pmo%22%3A%22https%3A%2F%2Fnlok.sharepoint.com%22%2C%22pmshare%22%3Atrue%7D&amp;ctp=LeastProtected&amp;rct=Normal&amp;wdorigin=Sharing.DirectLink.Copy&amp;afdflight=70&amp;csc=1&amp;instantedit=1&amp;wopicomplete=1&amp;wdredirectionreason=Unified_SingleFlush" target="_blank" rel="noreferrer noopener">2025 predictions blog</a> to see what else we’re thinking.

Alexa, Do You Love Me? How AI Is Messing with Our Love Lives and Relationships

Can love survive AI? 

Our team invests countless hours in the exploration, discovery and investigation of emerging trends such as AI and futuristic technology. Stay informed about the evolving landscape of cyberthreats through our diligent efforts and insights.

Threat Researcher at Gen

Research

<h4>Key Points&nbsp;</h4><ul><li data-list-item-id="e43a143fd054561c50dba51071686adff">Gen Threat Labs has been tracking GoFlateLoader, a widespread Golang loader used to deliver multiple infostealers, including Amatera, Remus, Lumma, Vidar and StealC.&nbsp;</li><li data-list-item-id="e3d3b5d0814ea5b26aa75a8d3d6013a39">GoFlateLoader appears both in x86 (32-bit) and x86-64 (64-bit) variants, matching the bitness of the payload it is supposed to execute.&nbsp;</li><li data-list-item-id="e51ccafcfb8c418d894aeb685004a0ba5">The loader is designed for in-memory payload execution and is deliberately inflated with a massive PE overlay to hinder detection</li><li data-list-item-id="ec4bafef8a1113d24bc2b4b3a943228d1">The two main delivery paths are via supposedly cracked software and a <a href="https://research.checkpoint.com/2026/impersonation-click-hijacking-and-tds-inside-a-malware-distribution-ecosystem/" target="_blank" rel="noreferrer noopener">malicious TDS recently documented by Check Point Research</a>, which may redirect users to dedicated landing pages serving password-protected archives containing GoFlateLoader.&nbsp;</li></ul><h4>Introduction&nbsp;</h4>Not every threat that matters is technically sophisticated, and that is also the case with GoFlateLoader, which is a rather simple loader written in Go, whose sole purpose is to decode and execute the payload in memory. What stands out the most is not what the loader does but rather what it does not do – it comes without anti-debugging, anti-VM, or sandbox-evasion checks, and also lacks API hashing or CFG obfuscation, the kind of tricks that loaders almost always come with. Instead, GoFlateLoader relies on one of the simplest yet still effective tricks to stay under the radar – it appends a massive PE overlay at the end of the file, deliberately inflating the binary's size (hence the name GoFlateLoader).&nbsp;Nevertheless, it is important to understand that malware does not necessarily need to be elegant or stealthy. In the end, the only thing that matters to attackers is whether it can successfully deliver the final payload. And if that is the only metric, GoFlateLoader appears to be working fairly well for its operators, as we continue to see it widely distributed across our user base. Since the beginning of April, we have protected more than 33,000 unique users from it, with the most affected countries including Brazil, India, Argentina, Mexico, Turkey and Spain.&nbsp;

<h4>GoFlateLoader’s Execution Flow&nbsp;</h4>If we strip away the obfuscation noise that the Go compiler and the author’s decoy code add, the loader’s execution flow is short and linear:&nbsp;<ul type="disc"><li data-list-item-id="efa51af3b2859fd9c684efca0c82d3b66">Copy the encoded payload blob from the <code>.rdata</code> section onto the stack.&nbsp;</li><li data-list-item-id="e879bb970c6d1a81b8a42512222aceaa4">Decode the payload using a small, multi-stage, custom byte-level transformation into a valid PE.&nbsp;</li><li data-list-item-id="ebff4f340af15b75cbea0d682b26b7645">Parse the PE headers of the decoded payload (image base, image size, sections, data directories, etc.).&nbsp;</li><li data-list-item-id="e37fb26cad3d18a4a0c8bf9b2595d661f">Allocate an RWX memory region using <code>VirtualAlloc</code> with <code>MEM_COMMIT | MEM_RESERVE</code> and <code>PAGE_EXECUTE_READWRITE</code> flags, trying the payload’s preferred <code>ImageBase</code> first and falling back to an OS-assigned address if that fails.&nbsp;</li><li data-list-item-id="ebbde6d77cdc942c0a6bac79f4f8a6c28">Map the decoded payload into the allocated memory region by copying the PE headers first, then walking the section table and writing each section to its target virtual address.&nbsp;</li><li data-list-item-id="ed16738229e0f5c70a981c0c5f9b715f9">Apply relocations (only if preferred base mapping fails).&nbsp;</li><li data-list-item-id="e76cde4bae1be6c21b897c350c6fd15db">Resolve imports by walking the standard <code>IMAGE_IMPORT_DESCRIPTOR</code> table and rebuild the IAT in place via <code>LoadLibrary</code> and<code> GetProcAddress</code>.&nbsp;</li><li data-list-item-id="e786887a3d34fc7ce344ee8375944799e">Transfer execution to the final payload using Go’s <code>syscall.Syscall</code>.&nbsp;</li></ul>&nbsp;This is essentially a textbook example of manual PE loading with the payload reconstructed and executed entirely in memory so that it never touches the disk.&nbsp;&nbsp;

Notably, GoFlateLoader transfers execution to the final payload via <code>syscall.Syscall</code>. In Go, <code>syscall.Syscall</code> has the signature <code>Syscall(trap, nargs, a1, a2, a3)</code>, where <code>trap</code> is the function pointer to invoke, <code>nargs </code>is the argument count, and <code>a1</code>, <code>a2</code>, and <code>a3</code> are the first three arguments passed to the target function. Here, trap is set to point to the final payload’s entry point, <code>nargs</code> is set to <code>1</code>, and since the execution is transferred to that entry point, the remaining arguments serve only as filler values and are never used by the invoked payload. In other words, GoFlateLoader is not issuing a <code>syscall</code> at all, but abusing <code>syscall.Syscall</code> as a generic call gate to hand control to the payload.&nbsp;&nbsp;It is worth emphasizing that GoFlateLoader consistently sets these arguments to the hardcoded values <code>1</code>, <code>2</code>, <code>3</code>, and <code>4</code>, which is unusual enough to stand out as a potential detection opportunity (combined with other patterns).&nbsp;

The loader also carries substantial junk/decoy code, which varies across builds and serves no purpose other than padding the control flow and complicating static analysis.&nbsp;&nbsp;&nbsp;<h4>Inflated PE Overlay&nbsp;</h4>GoFlateLoader’s defining characteristic is its massive size (typically 700-950 MB), caused by an artificially inflated PE overlay appended to the original loader.&nbsp;

This is a deliberate design choice intended to bypass detection, as excessively large executable files may pose challenges for AVs and EDRs, primarily due to performance and resource constraints. As a result, to preserve performance and avoid noticeable impact to end users, these solutions often enforce practical file-size limits for deep scanning and/or emulation.&nbsp;The same logic extends to automated analysis pipelines – bloated files frequently cause timeouts during heuristic analysis and effectively prevent the sample from being uploaded to cloud-based sandboxes or threat intelligence platforms, which enforce their own strict upload limits to manage bandwidth and storage costs. A prime example is VirusTotal, which enforces a strict 650 MB upload limit. The fact that GoFlateLoader consistently sits just above this threshold strongly suggests that VirusTotal is one of the platforms it specifically aims to evade.&nbsp;&nbsp;In most cases, GoFlateLoader’s PE overlays are filled with null bytes, though we have also observed builds with random padding.&nbsp;&nbsp;

Crucially, adding such a PE overlay comes at virtually no distribution cost for attackers, as the inflated data compresses to a fraction of its original size (assuming constant padding).&nbsp;&nbsp;<h4>Delivered Final Payloads&nbsp;</h4>The final payloads distributed through GoFlateLoader are predominantly information stealers, with the most prevalent currently being Amatera, <a href="https://www.gendigital.com/blog/insights/research/remus-64bit-variant-of-lumma-stealer" target="_blank" rel="noreferrer noopener">Remus</a>, and Lumma. Beyond these, we have also observed it distributing Vidar, StealC, and SvitStealer.&nbsp;GoFlateLoader comes in both x86 and x86-64 variants, with each variant matched to the architecture of the payload it is intended to execute.&nbsp;&nbsp;As for how GoFlateLoader binaries are distributed, we have observed at least two notable delivery paths. The first is through supposedly cracked software. The second involves the payload being served via <a href="https://research.checkpoint.com/2026/impersonation-click-hijacking-and-tds-inside-a-malware-distribution-ecosystem/" target="_blank" rel="noreferrer noopener">the malicious TDS recently described by Check Point Research</a>, which redirects users to a landing page that offers a downloadable archive and separately displays the password required to extract it. This approach provides several advantages for the threat actor. First, compression significantly reduces the binary size due to the constant-padded PE overlay. Second, because the archive password is displayed separately on the landing page, it makes it harder for AV and EDR solutions to obtain it and decrypt the archive. As a result, the payload may remain concealed until the victim manually extracts it.&nbsp;&nbsp;&nbsp;<h4>Summary&nbsp;</h4>GoFlateLoader is a reminder that prevalence and sophistication are not the same thing. At its core, it is a rather simple manual in-memory PE loader written in Go, with no novel anti-analysis tricks under the hood. Whatever evasion it achieves comes not from obfuscation or other anti-analysis tricks but from its size – the appended PE overlay is built to slip past size-constrained scanners and automated analysis pipelines. Yet, despite its low level of sophistication, GoFlateLoader remains an active and relevant threat, used to deliver a wide range of prevalent information stealers.&nbsp;&nbsp;Gen products protect our users against both GoFlateLoader and the payloads it delivers.&nbsp;&nbsp;<h4>Indicators of Compromise (IoCs)&nbsp;</h4>Note: The files listed in the IoCs that are not archives all exceed 650 MB in size and are therefore not available on VirusTotal.&nbsp;b88c5744975d2abb447aecc6c090fee9f8580413f4612eecdc6ed1973e8a1739 (password-protected archive containing GoFlateLoader x64 variant loading Remus; pwd: 1234)&nbsp;ed5ae7f36453c5a23e9868a5729d67e0549a11f6dea54f5f52d654a8f51d4902 (archive containing GoFlateLoader x64 variant loading Remus)&nbsp;841c9297cb8a2e0ff89433d13c05bfc760eb2e98e251cb8fa785d2ad7cbac05f (archive containing GoFlateLoader x86 variant loading Amatera)&nbsp;ece7c48eb411b24f26762ede83badb4a644c41d5777129381ac2541804d64fc2 (archive containing GoFlateLoader x86 variant loading Lumma)&nbsp;421ce2d2f49c23bbe9f60ef3b9cd38d7eb912ce02e56a61837656210069bd9e2 (archive containing GoFlateLoader x64 variant loading Vidar)&nbsp;121c2dc793b3873f75a29ec02241f94136de19c049382a50a50d0d5b99507073 (GoFlateLoader x64 variant loading StealC)&nbsp;2415db5081cec9bfd14ad6da1a66169fd96f13a49010c319a73d1ed6fafd4efa (GoFlateLoader x64 variant loading Vidar)&nbsp;d9917ade3b4c125a95b5d3e6343cde26145dfbf569bd7e2a843fd0c6fc8ddc28 (GoFlateLoader x64 variant loading Remus)&nbsp;4cf6893756f441522b94b36f10e5de0e47aeed4743f95c51650746d1ecf97e3d (GoFlateLoader x64 variant loading SvitStealer)&nbsp;8b89d6c9152d3aab97aadd515ecb69ca72654db2f25425759ba4b646853d737d (GoFlateLoader x86 variant loading Lumma)&nbsp;90ce4ff9da23ac150da0a8e17930cab1e369aa349fdc1b65691b70369145664a (GoFlateLoader x86 variant loading Amatera)&nbsp;

 Intentionally Inflated Go Binaries That Use Massive PE Overlays to Bypass Detection

GoFlateLoader: A Widespread Golang Loader Delivering Multiple Infostealers

Malware Researcher

When we published the first part of this research, we said the investigation was still ongoing. That was not a closing line. We were already looking at the infrastructure, the signals behind the attacks, and the scale of the reservation data abuse we were seeing in our telemetry.&nbsp;Then something else happened.&nbsp;Friends, colleagues, and other contacts started forwarding us messages they had received from Booking.com. The messages warned that unauthorized third parties may have accessed information linked to their reservations. Shortly after, media outlets began reporting the same thing: Booking.com had notified affected customers, reset reservation PINs, and said the accessed information could include names, email addresses, phone numbers, booking details, and information shared with accommodations. Booking.com said financial information was not accessed from its systems.&nbsp;The timing mattered, but not because it started our investigation. It mattered because it publicly confirmed the kind of reservation-data exposure our first article had described from the attack side.&nbsp;<a href="https://us.norton.com/blog/online-scams/reservation-hijacking-scam" target="_blank">Reservation Hijack scams</a> work because criminals do not have to guess. A traveler receives a message about a real booking. The message contains details that make sense: the right accommodation, the right trip context, sometimes the right contact information. The link then opens a fraudulent page built around the same accommodation.&nbsp;A generic <a href="https://us.norton.com/blog/online-scams/what-is-phishing " target="_blank">phishing message</a> has to create trust from nothing. This scam steps into a relationship that already exists between the traveler, the booking platform, and the accommodation.&nbsp;While those public warnings were appearing, Gen Threat Labs continued canvassing the data we had available: blocked URLs, fraudulent landing pages, SMS messages, emails, WhatsApp messages, in-app message evidence, and other signals connected to attacks we had already stopped. We used AI-assisted analysis to connect pieces that would otherwise remain scattered: messages, landing pages, accommodation names, URLs, templates, and infrastructure patterns. Human analysts reviewed the results and separated what we could prove from what we could only suspect.&nbsp;The result is a much clearer picture.&nbsp;We identified more than 350 compromised accommodations linked to the Reservation Hijack scam flows, spread across 50 countries. These were not abstract indicators or generic travel-themed pages. In the cases we analyzed, victims had already received reservation-specific information before being pushed to a fraudulent page customized around the accommodation tied to that booking.&nbsp;That is the difference between ordinary travel phishing and this model. The attackers are not guessing who is traveling. They already have enough reservation context to make the fraud feel normal.&nbsp;And they are doing it at scale.&nbsp;

<h3>How we connected the evidence&nbsp;</h3>The second phase of our investigation was already underway when Booking.com’s customer notifications started circulating publicly.&nbsp;Gen’s view of this activity comes from attacks we blocked before they could complete. No single signal tells the whole story. An SMS or WhatsApp message may show the <a href="https://us.norton.com/blog/emerging-threats/what-is-social-engineering " target="_blank">social engineering</a>. A URL may show the redirect path. A landing page may show the accommodation being impersonated. An in-app message may show where the attacker entered the trusted communication flow. A repeated page structure may link incidents that look separate at first.&nbsp;Put together, those pieces show how reservation data moves from compromise to fraud.&nbsp;AI helped us work through that evidence at scale. We used it to normalize accommodation names, cluster similar messages, compare landing-page structures, connect URLs with page content, and flag repeated patterns across channels. This was not “AI says these attacks are connected.” It was AI-assisted triage followed by human review.&nbsp;From that process, we extracted and cleaned accommodation names from captured fraudulent landing pages and identified 350 compromised accommodations.&nbsp;We use “compromised accommodations” carefully here. It means reservation context tied to those accommodations was compromised and used in an attack flow we intercepted. The source of compromise may vary: a hotel account, a booking platform partner account, an email inbox, a property management system, a guest messaging workflow, or a third-party service provider.&nbsp;The end result for the traveler is the same. Criminals had access to information that should have stayed inside the booking relationship.&nbsp;This is also a conservative number. It only includes attacks where we had enough evidence to connect the flow to a specific accommodation. It does not include people who received a message and never clicked, attacks outside our telemetry, landing pages we did not capture, or cases where the accommodation name could not be reliably extracted.&nbsp;So 350 is not the size of the problem. It is the part we could see clearly.&nbsp;<h3>From scam pattern to repeatable operation&nbsp;</h3>A single compromised hotel account can explain a few victims. A copied phishing kit can explain a short campaign. Hundreds of accommodation-specific scam flows across 50 countries point to something more organized.&nbsp;The model we see is consistent.&nbsp;Reservation context enters the fraud chain, sometimes through compromised accommodation-side accounts, inboxes, partner tools, or data obtained from other criminal actors. That context is then used to contact travelers with details that make the message feel legitimate.&nbsp;The wording, the property, the country change. The logic does not.&nbsp;That is why this follow-up is not simply “we found more cases.” The first article explained <a href="https://www.gendigital.com/blog/insights/research/reservation-hijack-scam">how Reservation Hijack scams work</a>. This one shows how far the model has spread.&nbsp;<h3>A worldwide footprint, led by Europe&nbsp;</h3>The highest number of compromised accommodations in our dataset were linked to Germany, followed by France, the United Kingdom, Italy, Spain, and the United States.&nbsp;Germany accounted for 49 compromised accommodations in the dataset. France followed with 35, the United Kingdom with 31, Italy with 24, Spain with 20, and the United States with 19.&nbsp;Together, the top five European countries, Germany, France, the United Kingdom, Italy, and Spain, accounted for 159 compromised accommodations, about 45% of the full dataset.&nbsp;

The European concentration makes sense. Europe has a dense travel market, a high volume of cross-border bookings, many independent and mid-sized accommodation providers, and a strong dependency on online booking platforms and pre-arrival guest messaging.&nbsp;Those conditions give criminals useful cover. Travelers expect messages before arrival. They expect payment questions, check-in instructions, policy reminders, and booking updates. A well-timed fake message does not feel unusual.&nbsp;But the country distribution also shows that this is not a single-region problem. The activity spreads across 50 countries, with a long tail of destinations beyond the largest clusters.&nbsp;

That long tail matters. It suggests the model is not limited to a few obvious tourist destinations or a handful of famous hotel brands. Once criminals have access to reservation data and a working scam flow, the same approach can be adapted to different countries, languages, property types, and booking habits.&nbsp;The property does not need to be famous. It only needs to be real, tied to a real reservation, and recognizable to the victim.&nbsp;<h3>Not just hotels&nbsp;</h3>Hotels make up the largest share of the dataset, but they are not the whole story.&nbsp;We identified compromised accommodations across several property types: hotels, aparthotels, apartments, inns, motels, lodges, resorts, guesthouses, B&amp;Bs, hostels, villas, bungalows, and capsule hotels.&nbsp;

This is a useful reminder of how fragmented the travel ecosystem really is. A traveler may book a large hotel chain, a family-run guesthouse, a serviced apartment, a hostel, or a villa through similar platforms and messaging flows.&nbsp;From the traveler’s point of view, the trust model is almost the same. They made a booking. They expect communication. They may not know whether the message came from the property, the platform, a channel manager, a property management system, or a compromised account sitting somewhere in between.&nbsp;That is exactly the gap criminals are abusing.&nbsp;<h3>How big could this problem be?&nbsp;</h3>To understand the potential scale of Reservation Hijack scams, we looked at the accommodation properties appearing in our research dataset.&nbsp;In total, we identified 350 accommodation entries linked to reservation hijack scam activity. These were not all the same kind of business. The dataset included hotels, aparthotels, resorts, hostels, guesthouses, villas, apartments and other accommodation types. For each one, we estimated the number of rooms, units, beds or capsules, depending on the property type, and used that to calculate the likely maximum guest capacity.&nbsp;Across the deduplicated dataset, these properties represented an estimated maximum capacity of around 82,000 guests at any given time. Using a conservative occupancy rate of 50% and an average length of stay of 2.5 nights, this translates into approximately 6 million guest stays per year.&nbsp;That does not mean 6 million people were scammed. It does not mean 6 million people had their data exposed. What it shows is the size of the opportunity criminals may be looking at when they compromise accommodation or booking communication channels.&nbsp;A single compromised workflow can give scammers something much more valuable than a random phone number or email address. It can give them context: the name of the accommodation, the dates of the stay, the booking amount, the channel the guest expects to be contacted through, and the right moment to create urgency.&nbsp;That is why Reservation Hijack scams are so convincing. The scam does not feel like spam. It feels like part of the trip.&nbsp;

<h3>The activity does not look random&nbsp;</h3>We are still investigating the infrastructure behind these attacks, so we are not attributing the activity to a named group.&nbsp;But the evidence does not look like hundreds of unrelated actors randomly copying one another.&nbsp;Across the dataset we analyzed, the same fingerprints kept appearing. Many pages used the same internal paths for scripts, images and styles. Several included the same unusual overlay component, repeated internal asset paths, and the same card-validation language, including claims that funds would be reserved and refunded within 10 minutes.&nbsp;The pages were also built to look specific to the victim. Hotel names appeared in page titles and visible content. Prices varied from one case to another. Check-in and check-out dates were inserted into the page. In some cases, page metadata and images were synchronized with the accommodation being impersonated. The evidence points to a repeatable process for turning reservation context into property-specific phishing pages. Taken together, those patterns are consistent with a phishing kit designed to generate accommodation-specific pages at scale, rather than pages built manually for each victim.&nbsp;The phishing pages also included a fake live support chat. To the victim, it looked like part of the booking or payment process, but it was controlled from the phishing infrastructure. The payment step could therefore become a live interaction: someone behind the page could react to the victim’s input, ask for another card, explain away a failed “validation”, push the victim to continue, or try to capture a one-time code while the victim still believed they were talking to booking support.&nbsp;Some of the infrastructure also uses legitimate internet services, including services such as Cloudflare, to place a layer between the visible scam page and the systems behind it. That does not make those services malicious. It means criminals are abusing the same infrastructure that normal websites use, including protective layers that can obscure hosting details, traffic paths, and backend systems.&nbsp;For investigators, this kind of setup makes the infrastructure harder to trace. For travelers, it removes many of the usual warning signs. They see a page that loads normally, shows the right accommodation, includes a support chat, and refers to the trip they actually booked. At that point, many people will treat the request as part of the reservation process, even when it is not.&nbsp;<h3>Correct information is not proof of legitimacy&nbsp;</h3>For years, people have been told to watch for bad spelling, odd sender names, generic greetings, and suspicious links. That advice still helps, but it does not solve this problem.&nbsp;A message can include the right accommodation, the right destination, and details from a real stay, and still be malicious. A page can show the correct hotel name, dates, and a plausible payment amount, and still be fake. A request can arrive at the moment when a traveler expects a check-in or payment message, and still be fraud.&nbsp;Page behavior can create false confidence too. In some of the phishing pages we analyzed, the fake support chat looked like part of the booking or payment process. If the victim had a question, or if the card “validation” failed, the page could keep the interaction going instead of simply collecting data and stopping there.&nbsp;That is what makes Reservation Hijack scams difficult for travelers to judge. The victim is not being tricked only by a fake brand or a fake website. They are being pushed through a process built around real context, at the moment when that context feels normal.&nbsp;Travelers expect hotels and booking platforms to contact them. They expect last-minute requests, check-in instructions, payment checks, ID forms, and confirmation messages. Criminals are not fighting that expectation. They are using it.&nbsp;<h3>What travelers should do&nbsp;</h3>The safest rule is simple: do not treat correct booking information as proof that a message is legitimate.&nbsp;If a message asks you to verify payment details, confirm a reservation, re-enter card information, pay a balance, reset a PIN, or avoid cancellation, do not use the <a href="https://us.norton.com/blog/online-scams/i-clicked-on-a-phishing-link" target="_blank">link in the message</a>.&nbsp;Open the booking platform or hotel app directly. Go to the official website yourself. Use contact details from the original booking confirmation. If needed, call the property using a verified number. Check whether the same request appears inside your official booking account.&nbsp;This applies even if the message mentions the right hotel, the right destination, or details from your stay.&nbsp;Booking.com has told customers that it will not ask guests to share credit card details by email, phone, WhatsApp, or text, or ask for a bank transfer that differs from the payment policy in the booking confirmation.&nbsp;If you already entered payment details into a suspicious page, contact your bank immediately, freeze or cancel the card, and monitor for follow-on fraud. Criminals may reuse the card data later, or sell it to others.&nbsp;<h3>What accommodations should take from this&nbsp;</h3>For hotels and other accommodation providers, this is not only a guest-awareness issue. Guest communication has become part of the attack surface.&nbsp;The systems and workflows between the property and the guest are now valuable targets. That can include hotel staff accounts, booking platform partner accounts, email inboxes, property management systems, guest messaging tools, channel managers, and third-party service providers.&nbsp;The lures we have seen on the accommodation side are not exotic. They look like normal hotel work. Some impersonate Booking.com and claim there is a guest complaint waiting for a response. Others say that a paid reservation needs urgent verification, or that the property’s visibility or ranking may be affected if staff do not act quickly. We have also seen payment and invoice-themed messages designed to make staff open attachments or documents that can lead to credential theft or <a href="https://us.norton.com/blog/malware/signs-of-malware" target="_blank">malware infection</a>.&nbsp;

That is why the first visible sign of a Reservation Hijack scam may not be the message sent to the guest. It may start earlier, with an email to a shared hotel inbox, a fake partner-support notice, a malicious attachment, or a document that looks like part of normal accounting or reservation handling. Once reservation context is obtained, whether directly or through another actor in the criminal ecosystem, it can be used later to make guest-facing scams much more convincing.&nbsp;For accommodation providers, staff training needs to cover the workflows criminals are actually abusing. Urgent complaints, reservation-verification requests, payment advice emails, invoice notifications, security-update prompts, and any message asking staff to open an attachment, enable content, sign in again, or act under time pressure should all be treated as possible entry points into the same fraud chain.&nbsp;The defensive work starts with account protection, but it should not stop there. Accommodation providers need strong <a href="https://us.norton.com/blog/account-safety/multi-factor-authentication" target="_blank">multi-factor authentication</a>, tight control over who can access guest data, monitoring for unusual logins or message patterns, and a clear process for responding when an account may have been abused. The devices used to manage reservations, guest messages, invoices and payment-related emails should also be protected with reliable security protection. Some of the lures we observed were designed to make staff open attachments or documents that could install malware or steal credentials. In those cases, <a href="https://us.norton.com/antivirus" target="_blank">antivirus</a> or endpoint protection may be the last barrier before a compromised inbox becomes a compromised booking workflow.&nbsp;Payment-related communication deserves special attention. If a guest receives a payment or verification request, there should be a reliable way to confirm whether it is real. If an account is suspected of being compromised, guests need to be warned quickly and clearly.&nbsp;The reputational damage can be painful. Even when the property is also a victim, the traveler may remember the incident as “the hotel message that stole my card details.” That makes workflow security part of customer trust.&nbsp;<h3>What the wider travel ecosystem should learn&nbsp;</h3>Reservation Hijack scams sit between several parts of the travel industry. The booking platform may see unusual partner activity. The hotel may see confused guests. The traveler sees a convincing message. The bank sees card fraud. A security company sees the landing page.&nbsp;Each view is partial, and that delay helps the scam continue.&nbsp;The useful signals are often scattered: unusual partner logins, repeated payment-verification language, guest-message spikes, reused templates, fake landing pages, redirects, and infrastructure overlaps. Sharing those signals faster would make it easier to see when separate complaints are really part of the same operation.&nbsp;One compromised accommodation can look like a local incident. Hundreds of compromised accommodations across 50 countries point to a repeatable abuse model.&nbsp;<h3>The next phase of travel fraud&nbsp;</h3>Reservation Hijack scams exploit a simple mistake in how we judge trust online.&nbsp;Travelers think: “This message knows my booking, so it must be real.”&nbsp;Criminals know: “If we have the booking context, the victim will do much of the trust work for us.”&nbsp;The message does not have to be perfect. The website does not have to be flawless. The attacker does not need to convince the victim from zero. They only need to appear inside a relationship that already exists.&nbsp;Our latest data shows that this model has spread well beyond isolated cases. We identified 350 compromised accommodations across 50 countries, covering hotels, apartments, hostels, resorts, guesthouses, villas, and other accommodation types. Together, those properties represent a large travel footprint and millions of estimated annual guest interactions.&nbsp;The investigation is still ongoing. We are continuing to analyze infrastructure, campaign overlaps, and the clues suggesting connected activity behind parts of the operation.&nbsp;For travelers, the lesson is simple.&nbsp;Trust your booking. Do not automatically trust the message.&nbsp;

After our first report, Booking.com began warning customers that reservation data had been accessed. Our continuing investigation shows how criminals are using that data at scale. 

When Hotel Scams Know Your Booking: 350 Compromised Accommodations Across 50 Countries 

Threat Research Team

Attackers replaced selected official download links with malicious installers that deployed a Python bot, r77 rootkit components, and Windows policy-based defense evasion.On May 6, 2026, attackers compromised the official JDownloader website and swapped download links to serve trojanized installers. The JDownloader developers <a href="https://www.reddit.com/r/jdownloader/comments/1t6goqe/is_the_website_hacked/">confirmed the breach</a> within hours, restored clean files, and disclosed a timeline, but acknowledged they didn't know what the malicious installers actually do. We took the malware apart to answer that question.&nbsp;What we found goes well beyond a typical trojanized installer. The payload is a multi-component attack framework: a Python bot protected by PyArmor v9, an r77 rootkit stager with AMSI bypass, and a Windows Defender Application Control (WDAC) policy that blocks 50 security executables from running, including Avast, AVG, Avira, Windows Defender, HitmanPro, and Kaspersky Virus Removal Tool. We reversed the full C2 protocol from the obfuscated Python bytecode and recovered the campaign configuration from the dropper's encrypted strings.&nbsp;<h2>The infection chain</h2>The trojanized installer is unsigned. Windows SmartScreen blocks it, and users who ran it had to manually bypass the warning. Inside, it wraps the legitimate JDownloader installer alongside an XOR-encrypted second-stage PE:

Stage 2 acts as the campaign's quartermaster. It deploys five components from XOR-encrypted resources embedded in its PE (key: <code>fywo</code>):<table class="table"><thead><tr><th>Component</th><th>Purpose</th></tr></thead><tbody><tr><td>PyArmor bot</td><td>The main payload, a Python script with obfuscated bytecode</td></tr><tr><td>PyArmor runtime</td><td>DLL that decrypts the bot's bytecode at execution time</td></tr><tr><td>r77 rootkit stager</td><td>Native x86 PE that hides the bot's process, files, and registry keys</td></tr><tr><td>WDAC Code Integrity policy</td><td>Blocks 50 AV/EDR executables from running after reboot</td></tr><tr><td>Python 3.14.3 installer</td><td>The legitimate Python runtime (needed to run the bot)</td></tr></tbody></table>Before doing any of this, Stage 2 calls <code>WaitForSingleObject</code> with a 300-second timeout — three times in sequence. This 5-minute-per-call delay is designed to outlast automated sandboxes, which typically give samples 2-5 minutes of execution time. One sandbox vendor recorded an 8-minute total wait before any malicious behavior appeared.&nbsp;<h2>Killing your antivirus before it can fight back</h2>The most operationally significant component is the WDAC Code Integrity policy.Windows Defender Application Control (WDAC) policies tell the operating system which executables are allowed to run. The attackers deploy a deny-list policy to <code>%WINDIR%\System32\CodeIntegrity\SIPolicy.p7b</code> that blocks 50 executable paths across six security vendors plus core Windows security services:<table class="table"><thead><tr><th>Target</th><th>Blocked executables</th></tr></thead><tbody><tr><td>Avast</td><td>AvastSvc.exe, AvastUI.exe, afwServ.exe, aswEngSrv.exe, aswidsagent.exe, aswToolsSvc.exe, wsc_proxy.exe, Icarus components, Overseer</td></tr><tr><td>AVG</td><td>All executables under Program Files\AVG\Antivirus</td></tr><tr><td>Avira</td><td>Security, VPN, Fallback Updater, Optimizer Host, Endpoint Protection SDK</td></tr><tr><td>Windows Defender</td><td>MsMpEng.exe, MpCmdRun.exe, MpDefenderCoreService.exe</td></tr><tr><td>Windows Security</td><td>SecurityHealthHost.exe, SecurityHealthService.exe, SecurityHealthSystray.exe</td></tr><tr><td>Windows Update</td><td>MoUsoCoreWorker.exe, wuauclt.exe, wusa.exe</td></tr><tr><td>Others</td><td>HitmanPro, Kaspersky Virus Removal Tool, Farbar Recovery Scan Tool</td></tr></tbody></table>After the next reboot, none of these executables can start. The AV service is effectively dead. It won't run, it can't scan, and Windows Update can't push signature updates. One victim in the Reddit thread reported that Windows Defender disappeared entirely after running the trojanized installer.This is a known but uncommon TTP (MITRE T1562.001). It requires the installer to run elevated, which the SmartScreen bypass dialog would have prompted for. Users who clicked through both warnings — SmartScreen and UAC — gave the malware everything it needed.<h2>&nbsp;</h2><h2>How the dropper provisions the bot</h2>Stage 2's strings are all XOR-obfuscated with the same <code>fywo</code> key used for its resources. After deobfuscation, we recovered 122 strings including registry paths, API names, and encrypted configuration values.The dropper writes the bot's initial configuration to <code>HKCU\SOFTWARE\Python</code>. Both the registry value names and data are RC4-encrypted and hex-encoded — making the registry entries look like random hex strings to casual inspection. We recovered two configuration values:<ul><li data-list-item-id="e15e9100cccadef17d91598e244b0b038">CampaignID a unique identifier for this campaign, used during bot registration with the C2 server.</li><li data-list-item-id="e7a2666057614636498fba93e483028cf">DDRList six dead-drop resolver URLs that the bot uses to discover its C2 servers (detailed in the next section).</li></ul>Notably, the dropper does not write a C2 server list directly. The bot discovers its command-and-control addresses exclusively through the dead-drop resolver mechanism — a design choice that makes the infrastructure resilient to takedowns.&nbsp;<h2>Dead-drop resolvers: how the bot finds its C2</h2>A dead-drop resolver (DDR) is an intermediary, a legitimate web page where the operator publishes encrypted C2 addresses. The bot fetches the page, extracts and decrypts the content, and connects to whatever C2 servers it finds. If the operator needs to rotate infrastructure, they update the DDR page and every infected machine follows automatically.The bot reads its DDR list from the registry, fetches each URL, and RC4-decrypts the page content. Valid payloads start with the prefix <code>VAL:</code> followed by a semicolon-separated list of C2 URLs.We extracted all six dead-drop resolver URLs from the dropper and probed them:<table class="table"><thead><tr><th>URL</th><th>Status</th><th>Decrypted content</th></tr></thead><tbody><tr><td><code>telegra[.]ph/tr02-05-02</code></td><td>Live</td><td>C2 list: parkspringshotel[.]com + auraguest[.]lk</td></tr><tr><td><code>telegra[.]ph/ki06-05-02</code></td><td>Live</td><td><code>VAL:XX</code> — placeholder for future C2 rotation</td></tr><tr><td><code>rentry[.]co/zzk7opdu</code></td><td>Live</td><td>Same C2 list</td></tr><tr><td><code>rentry[.]org/wayc2qqz</code></td><td>Live</td><td><code>VAL:XX</code> — placeholder</td></tr><tr><td><code>codeberg[.]org/etienne74/guix/...</code></td><td>404</td><td>Removed or never created</td></tr><tr><td>`2wxjoz6lkdxkniksujbdknkugweemx7uvxdr4ejgyipce4tnvktvbvqd.onion/t`</td><td>Not probed</td><td>Tor hidden service DDR</td></tr></tbody></table>The operator has two live C2 servers and four reserve dead-drop pages — two stubs ready for C2 rotation and one Tor fallback. If the current C2 infrastructure gets burned, the operator updates a Telegraph or Rentry page and every infected machine picks up the new address on its next poll cycle.Beyond the dead-drop mechanism, the bot has a domain generation algorithm (DGA) as a last-resort fallback. It combines four seeds (<code>XNerWs4I</code>, <code>e55gegnW</code>, <code>RjWN0Y9m</code>, <code>AemOU1co</code>) with 14 TLDs and an ISO-week-based rotation to produce 56 candidate domains per week. We probed all current DGA domains — none are registered. The operator is running purely on dead-drop infrastructure for now, but the DGA ensures the botnet can survive even if all resolver pages and C2 servers are taken down simultaneously.&nbsp;<h2>The C2 protocol: RSA-OAEP wrapping AES-GCM</h2>We reversed the bot's full communication protocol from the PyArmor-protected bytecode. It uses a layered encryption scheme designed to prevent passive interception and session tampering:

The bot wraps a fresh AES-256 key with the hardcoded RSA-2048 public key (OAEP, SHA-256) and sends it to the C2. The server proves possession of the private key by returning <code>"ok"</code> encrypted with AES-GCM. From that point, every message in both directions is AES-256-GCM encrypted with a per-direction sequence counter as authenticated associated data — preventing replay and reordering.After the handshake, the bot registers with the campaign ID provisioned by the dropper. The server returns a <code>bot_id</code> and <code>bot_pass</code> that the bot stores in the registry for subsequent polling. The bot's primary capability is arbitrary Python code execution — the attacker can ship any Python code and the bot will run it and return the output.All identified C2 and staging servers are compromised <a href="https://qloapps.com/">QloApps</a> hotel reservation websites:<table class="table"><thead><tr><th>Domain</th><th>Role</th><th>IP</th><th>PHP</th><th>Location</th></tr></thead><tbody><tr><td><code>parkspringshotel[.]com</code></td><td>C2 panel</td><td>172.96.172[.]91</td><td>8.3.30</td><td>Ghana</td></tr><tr><td><code>auraguest[.]lk</code></td><td>C2 panel</td><td>209.133.215[.]178</td><td>8.3.30</td><td>Sri Lanka</td></tr><tr><td><code>checkinnhotels[.]com</code></td><td>Linux payload staging</td><td>66.29.137[.]25</td><td>7.4.33</td><td>—</td></tr></tbody></table>The two C2 servers share identical JARM fingerprints and run the same PHP version, confirming the same C2 panel software is deployed on both. The third domain, <code>checkinnhotels[.]com</code>, was used to host the Linux payload disguised as an SVG file (<a href="https://www.bleepingcomputer.com/news/security/jdownloader-site-hacked-to-replace-installers-with-python-rat-malware/">BleepingComputer</a>). All three run QloApps — an open-source hotel booking CMS — suggesting the attacker specifically targets this software to build their infrastructure. None of the domains had malicious detections on VirusTotal at the time of discovery. We confirmed both C2 servers remain operationally live as of May 11.<h2>&nbsp;</h2><h2>The r77 rootkit stager</h2>The rootkit stager is a 176 KB native x86 PE based on the open-source <a href="https://github.com/bytecode77/r77-rootkit">r77 rootkit</a>. It contains an embedded .NET assembly that performs four operations:<ol><li data-list-item-id="e9ca3772c2bd6052d86931268e23d036a">AMSI bypass — a PowerShell cradle patches <code>AmsiScanBuffer</code> in memory, disabling Windows' Antimalware Scan Interface before any script payload runs.</li><li data-list-item-id="e01c27cd4c721b2dabe18b819d05c84fe">Reflective DLL injection — injects the r77 hooking DLLs (32-bit and 64-bit variants) into <code>winlogon.exe</code>. These DLLs hook Windows API functions to hide any process, file, or registry key whose name starts with the <code>$77</code> prefix.</li><li data-list-item-id="e254d7da7ebd6dfd7d57f7c5f67760ba3">Registry persistence — stores the stager assembly as a binary blob in <code>HKLM\SOFTWARE\$77stager</code> and reloads it at every boot via .NET reflection.</li><li data-list-item-id="ef200c38559748bd535edaf6a0d5aa3d5">Service creation — installs a Windows service (<code>$77svc</code>) to ensure the rootkit survives reboots independently of the registry mechanism.</li></ol>Once r77 is active, the <code>$77</code> prefix acts as an invisibility cloak. The Python bot calls <code>r77_hide_self()</code> and <code>r77_hide_registry_path()</code> to rename its own process and registry hive with this prefix, making them invisible to Task Manager, <code>regedit</code>, and most security tools.<h2>&nbsp;</h2><h2>The Python bot under PyArmor v9</h2>The final payload is a Python script protected by <a href="https://pyarmor.dashingsoft.com/">PyArmor</a> v9, a commercial bytecode obfuscation tool. PyArmor encrypts each function's bytecode individually and decrypts it at runtime through a native DLL — making static analysis significantly harder than standard Python malware.We built a custom tool to decrypt and disassemble the protected bytecode, recovering 30 functions totaling 7,326 lines of disassembly. The bot is cross-platform (Windows and Linux), uses gevent for asynchronous I/O, and handles the full lifecycle: configuration management, C2 discovery via dead-drop resolvers and DGA, encrypted communication, task execution, and rootkit integration.The bot's primary capability is executing arbitrary Python code received from the C2 server. Combined with the Python 3.14 runtime installed by the dropper, this gives the operator the ability to deploy any follow-on payload — keyloggers, credential stealers, lateral movement tools — without needing to push new binaries to the victim.<h2>&nbsp;</h2><h2>Impact and recommendations</h2>Users who downloaded JDownloader from the official website between May 6 and May 7, 2026, using the "Download Alternative Installer" links (Windows) or the Linux shell installer link are potentially compromised. The <a href="https://jdownloader.org/incident_8.5.2026.html">official incident report</a> lists eight malicious installer hashes across x86/amd64 and multiple JRE versions. The attackers exploited an unpatched CMS vulnerability to swap download links — the actual installer files hosted externally were never modified. In-app updates, macOS downloads, and package manager installs (Flatpak, Winget, Snap) were not affected.The Linux attack chain, [first reported by BleepingComputer](https://www.bleepingcomputer.com/news/security/jdownloader-site-hacked-to-replace-installers-with-python-rat-malware/), follows a different delivery path. We downloaded the trojanized shell installer and its payload to verify the claims and dig deeper.d. The modified install4j script requires root, checks for x86_64 architecture, and downloads a 20 MB tar.gz from <code>checkinnhotels[.]com/img/logo1.svg</code> (disguised with <code>Content-Type: image/svg+xml</code> — still live as of May 11). The archive contains two files: a 3 KB SUID-root helper (<code>systemd-exec</code>) and a 21 MB PyInstaller bundle (<code>pkg</code>). The helper is a minimal ELF that calls <code>setuid(0)</code>, <code>setgid(0)</code>, and <code>execvp()</code> — installed to <code>/usr/bin/systemd-exec</code> with <code>chmod 4755</code>. Persistence is created via <code>/etc/profile.d/systemd.sh</code>, which launches the payload masquerading as <code>/usr/libexec/upowerd</code>.We decrypted the PyArmor-protected Python bytecode from the Linux payload and confirmed it is the same bot source code as the Windows variant — identical RSA public key, DGA seeds, RC4 keys, C2 protocol, and DDR handler logic. The same campaign ID (<code>NdcHsJPM</code>) and all six DDR URLs are written to <code>~/.local/share/</code> as RC4-encrypted files (mirroring the Windows registry approach). The only difference is the Python runtime: the Linux bundle uses Python 3.12 (packaged via PyInstaller) versus Python 3.14 on Windows.The JDownloader developers restored clean downloads on May 9 and published a detailed incident timeline. Users who already had JDownloader installed and received updates through the application's built-in updater were not affected — only fresh downloads from the website during the compromise window were trojanized.If you ran the trojanized installer:<ul><li data-list-item-id="e78c8637f87f47adb2a6c0e9e37ff1466">The WDAC policy may have disabled your antivirus. Check <code>%WINDIR%\System32\CodeIntegrity\SIPolicy.p7b</code> — if it exists and you didn't create it, delete it and reboot.</li><li data-list-item-id="e5a9344602452a9e77dfe81e30c45fd8c">The registry keys <code>HKCU\SOFTWARE\Python</code> (bot config) and <code>HKLM\SOFTWARE\$77stager</code> (rootkit) are strong indicators of compromise — but if r77 is active, its API hooks hide all <code>$77</code>-prefixed registry keys and the bot's own hive from standard tools like <code>regedit</code>. To inspect these, boot from a Windows recovery environment or mount the registry hives offline.</li><li data-list-item-id="edcbc2f271c7b2885893f8f995fe252eb">A clean OS reinstall is the safest remediation. The r77 rootkit's reflective injection into <code>winlogon.exe</code> and AMSI bypass make partial cleanup unreliable.</li></ul>For defenders:<ul><li data-list-item-id="efcd7ce434d65f159da0dbff097d5fb44">The bot uses the default Python User-Agent <code>Python-urllib/3.14</code>. While not unique on its own, it can help correlate connections to the C2 URLs and DDR pages listed below.</li><li data-list-item-id="e35fe4757c01a79131defbd71e6762bfb">Monitor for DNS queries to the DGA pattern: 15-character lowercase alphanumeric hostnames on TLDs including <code>.top</code>, <code>.cfd</code>, <code>.sbs</code>, <code>.cyou</code>, <code>.icu</code>, <code>.casa</code>.</li><li data-list-item-id="e41ab2df0dd210b9e338012701bdf4398">The mutex name <code>Global\0C3C1D37</code> can serve as a host-based indicator.</li><li data-list-item-id="e68e99d7961368cd02568989f8e767d08">Block the dead-drop resolver pages: <code>telegra[.]ph/tr02-05-02</code>, <code>telegra[.]ph/ki06-05-02</code>, <code>rentry[.]co/zzk7opdu</code>, <code>rentry[.]org/wayc2qqz</code>.</li></ul><h2>&nbsp;</h2><h2>Indicators of Compromise</h2>Full IOC list available on <a href="https://github.com/avast/ioc">GitHub</a>.<h3>Hashes (SHA-256)</h3><table class="table"><thead><tr><th>Artifact</th><th>Hash</th></tr></thead><tbody><tr><td>Stage 1 — Windows (analyzed variant)</td><td><code>5a6636ce490789d7f26aaa86e50bd65c7330f8e6a7c32418740c1d009fb12ef3</code></td></tr><tr><td>Stage 1 — Linux shell installer</td><td><code>6d975c05ef7a164707fa359284a31bfe0b1681fe0319819cb9e2c4eec2a1a8af</code></td></tr><tr><td>Stage 2 (C++ dropper)</td><td><code>77a60b5c443f011dc67ace877f5b2ad7773501f3d82481db7f4a5238cf895f80</code></td></tr><tr><td>Stage 3 — Windows (PyArmor bot)</td><td><code>5fdbee7aa7ba6a5026855a35a9fe075967341017d3cb932e736a12dd00ed590a</code></td></tr><tr><td>PyArmor runtime DLL</td><td><code>33318499489cdb82543c0bfea699b98f5928c7a360966df6e958a9cbc2eab3fe</code></td></tr><tr><td>r77 rootkit stager</td><td><code>5c887054cb1dce077943afa955db43306f66795f7cbda8233d8ba25230a23d41</code></td></tr><tr><td>WDAC CI policy</td><td><code>5ee86c177dc5bdba05e3bdc67b07115c66097f825fff257bf0d4a999bbb8a1ea</code></td></tr><tr><td>Linux payload archive (logo1.svg)</td><td><code>6550672cac21e036882921dd934ee06552dc74d3b0a9e1ddc26f952855e11371</code></td></tr><tr><td>Linux SUID helper (systemd-exec)</td><td><code>25744e90bfa44cbcbf1f3d3c3cb90dd79dd32a6e359df9d2660ff251d6d03b46</code></td></tr><tr><td>Linux PyInstaller bot (pkg)</td><td><code>bf47585bd0b39f0731f044b37a95eb7e311ad31b23b50306a113a3aa777dbfab</code></td></tr></tbody></table>Six additional Windows installer variants are listed in the <a href="https://jdownloader.org/incident_8.5.2026.html">official incident report</a>. Full hash list on <a href="https://github.com/avast/ioc/tree/master/JDownloaderRAT">GitHub</a>.<h3>Network indicators</h3><table class="table"><thead><tr><th>Type</th><th>Value</th><th>Context</th></tr></thead><tbody><tr><td>C2 URL</td><td><code>parkspringshotel[.]com/m/Lu6aeloo.php</code></td><td>Active C2</td></tr><tr><td>C2 URL</td><td><code>auraguest[.]lk/m/douV2quu.php</code></td><td>Active C2</td></tr><tr><td>Staging</td><td><code>checkinnhotels[.]com/img/logo1.svg</code></td><td>Linux payload (still live)</td></tr><tr><td>C2 IP</td><td><code>172.96.172[.]91</code></td><td>parkspringshotel.com</td></tr><tr><td>C2 IP</td><td><code>209.133.215[.]178</code></td><td>auraguest[.]lk</td></tr><tr><td>Staging IP</td><td><code>66.29.137[.]25</code></td><td>checkinnhotels.com</td></tr><tr><td>Dead-drop resolver</td><td><code>telegra[.]ph/tr02-05-02</code></td><td>Live, serves encrypted C2 list</td></tr><tr><td>Dead-drop resolver</td><td><code>rentry[.]co/zzk7opdu</code></td><td>Live, serves encrypted C2 list</td></tr><tr><td>Dead-drop resolver</td><td><code>telegra[.]ph/ki06-05-02</code></td><td>Stub, reserved for C2 rotation</td></tr><tr><td>Dead-drop resolver</td><td><code>rentry[.]org/wayc2qqz</code></td><td>Stub, reserved for C2 rotation</td></tr></tbody></table><h3>Host indicators</h3><table class="table"><thead><tr><th>Type</th><th>Value</th></tr></thead><tbody><tr><td>Mutex</td><td><code>Global\0C3C1D37</code></td></tr><tr><td>Registry (bot config)</td><td><code>HKCU\SOFTWARE\Python\*</code> (hex-encoded RC4-encrypted value names)</td></tr><tr><td>Registry (r77)</td><td><code>HKLM\SOFTWARE\$77stager</code>, <code>$77dll32</code>, <code>$77dll64</code>, <code>$77svc</code></td></tr><tr><td>WDAC policy</td><td><code>%WINDIR%\System32\CodeIntegrity\SIPolicy.p7b</code> (GUID <code>{31351756-3F24-4963-8380-4E7602335AAE}</code>)</td></tr><tr><td>Bot process</td><td><code>pythonw.exe</code> (Python 3.14, User-Agent <code>Python-urllib/3.14</code>)</td></tr><tr><td>Tor drop path</td><td><code>C:\ProgramData\tor\tor\tor.exe</code></td></tr><tr><td>Linux SUID binary</td><td><code>/usr/bin/systemd-exec</code></td></tr><tr><td>Linux payload</td><td><code>/root/.local/share/.pkg</code></td></tr><tr><td>Linux persistence</td><td><code>/etc/profile.d/systemd.sh</code></td></tr><tr><td>Linux masquerade</td><td><code>/usr/libexec/upowerd</code></td></tr></tbody></table><h3>MITRE ATT&amp;CK</h3><table class="table"><thead><tr><th>Technique</th><th>ID</th></tr></thead><tbody><tr><td>Supply Chain Compromise: Compromise Software Supply Chain</td><td>T1195.002</td></tr><tr><td>Obfuscated Files or Information</td><td>T1027</td></tr><tr><td>Impair Defenses: Disable or Modify Tools</td><td>T1562.001</td></tr><tr><td>Process Injection: Dynamic-link Library Injection</td><td>T1055.001</td></tr><tr><td>Web Service: Dead Drop Resolver</td><td>T1102.001</td></tr><tr><td>Dynamic Resolution: Domain Generation Algorithms</td><td>T1568.002</td></tr><tr><td>Encrypted Channel: Asymmetric Cryptography</td><td>T1573.002</td></tr><tr><td>Rootkit</td><td>T1014</td></tr><tr><td>Create or Modify System Process: Windows Service</td><td>T1543.003</td></tr><tr><td>Modify Registry</td><td>T1112</td></tr></tbody></table>For customers using Norton, Avast, AVG, Avira or any other security product, the most important question is whether the malicious installer was executed. If you downloaded an affected installer but did not run it, delete the file, download a fresh installer from the official JDownloader site, and run a full scan with up-to-date protection. If you ran the installer, or cannot rule out that you ran it, treat the system as compromised. JDownloader recommends a clean operating system reinstall in this situation, and we agree that this is the safest baseline. The Windows payload includes rootkit behavior, persistence, encrypted configuration and policy-based defense evasion, so a normal scan may reduce risk but cannot guarantee that every persistence mechanism has been removed. Until the system has been rebuilt or inspected from a trusted recovery environment, avoid using it for sensitive activity. Do not log in to email, banking, work accounts, password managers or other important services from that machine. Change important passwords from another trusted device, and monitor accounts for unusual activity.&nbsp;

Malware that hid itself on infected systems and disabled antivirus protection.

Inside the JDownloader Supply-Chain Attack: An r77 Rootkit Bot That Kills Your Antivirus

TLDR: Bedep was a malware family active mainly in 2014 and 2015 that used an unusually sophisticated domain generation algorithm, or DGA, to hide its command-and-control infrastructure. Instead of relying only on the date, it used real foreign exchange rates published by the European Central Bank, making its future domains much harder to predict in advance. Under the hood, the malware used ideas from group theory to generate a fixed set of unique domains without collisions, a rare example of advanced mathematics being applied directly in real-world malware.Most domain generation algorithms are boring. A linear congruential generator, a date seed, some modular arithmetic, a character table -- <code>seed = (seed * 0x41C64E6D + 0x3039) % 2^31</code>, rinse, repeat. You reverse them in an afternoon, pre-compute next month's domains over lunch, and move on.Bedep author implemented a number theory textbook inside a 32-bit DLL, and the result is one of the most mathematically elegant pieces of malware ever shipped.This post is a deep dive into this unique engineering.<h2>What is Bedep?</h2>Bedep is an ad-fraud botnet that was active from late 2014 through 2015, delivered exclusively through the Angler exploit kit. When a Flash zero-day (CVE-2015-0311) was burning, Angler was dropping Bedep. The malware itself was a modular downloader focused on click fraud, but its infrastructure is what made it interesting. In early 2015, new variants appeared with a DGA for C2 resolution. A three-day sinkhole by Arbor Networks (ASERT) caught phone-homes from roughly 82,000 unique IPs, spread across every continent except -- notably -- Russia.Dennis Schwarz originally reversed the DGA at ASERT, who published a proof-of-concept Python reimplementation and a detailed write-up titled "Bedep's DGA: Trading Foreign Exchange for Malware Domains." The original blog post has since been taken down from NETSCOUT's site, but survives on the <a href="https://web.archive.org/web/20160311083109/http://www.arbornetworks.com:80/blog/asert/bedeps-dga-trading-foreign-exchange-for-malware-domains/">Wayback Machine</a>. The PoC lived at <code>github.com/arbor/bedep_dga</code>. Dennis got the algorithm working but noted that parts of it were opaque -- he called the core transform a "blackbox" and flagged the embedded table as "likely something Fermat number related." He was right. Let's open that box.&nbsp;<h2>The ECB trick: Seeding a DGA from the financial markets</h2>Before the math starts, Bedep needs a seed that both the bot and the botmaster can independently derive without communicating. Most DGAs use the date. Bedep uses the date and the global foreign exchange market.The algorithm fetches two XML files from legitimate public services:<ol><li data-list-item-id="ec5890edf5187711796f804fae7ad8577">UTC time from <code>earthtools.org/timezone/0/0</code> -- the current timestamp, converted to "days since year zero."</li><li data-list-item-id="ead6313dc967de96442575ff50339b412">Euro foreign exchange reference rates from <code>www.ecb.europa.eu/stats/eurofxref/eurofxref-hist-90d.xml</code> -- the European Central Bank's daily publication of EUR conversion rates against 31+ world currencies, updated every business day.</li></ol>The time is used to select which day's rates to use. Specifically, Bedep picks "last Tuesday's" exchange rates -- meaning the preceding week's Tuesday until Thursday, after which it rolls forward to the current week's Tuesday. Only rates falling on a Monday boundary (in the algorithm's day-counting scheme) are considered. This gives a roughly weekly rotation with a deterministic selection rule.From the chosen date, Bedep extracts up to 48 currency rates -- USD, JPY, GBP, CZK, BGN, HUF, and so on. Each rate is parsed through a slightly broken custom <code>atof()</code> implementation (off by a bit in the least significant digits -- a quirk, not a feature), packed into a 64-bit IEEE double, and its low 32-bit dword is extracted for use in the algorithm.Here is what a single run looks like, from the PoC output for April 7, 2015:<code>parsed 31 currencies from 2015-04-07 (currency date): &nbsp; &nbsp;USD: 1.0847 &nbsp; &nbsp; &nbsp;JPY: 130.33 &nbsp; &nbsp; BGN: 1.9558 &nbsp; &nbsp;CZK: 27.455 &nbsp; &nbsp; &nbsp;DKK: 7.4714 &nbsp; &nbsp;GBP: 0.7286 &nbsp; &nbsp;HUF: 299.08 &nbsp; &nbsp; &nbsp;PLN: 4.0578 &nbsp; &nbsp;RON: 4.4165 &nbsp; &nbsp;SEK: 9.374 &nbsp; &nbsp; &nbsp; CHF: 1.0438 &nbsp; &nbsp;NOK: 8.73 &nbsp; &nbsp;HRK: 7.619 &nbsp; &nbsp; &nbsp; RUB: 59.8265 &nbsp; TRY: 2.8079 &nbsp; &nbsp;AUD: 1.4192 &nbsp; &nbsp; &nbsp;BRL: 3.3979 &nbsp; &nbsp;CAD: 1.3563 &nbsp; &nbsp;CNY: 6.7241 &nbsp; &nbsp; &nbsp;HKD: 8.4086 &nbsp; &nbsp;IDR: 14091.77 &nbsp; &nbsp;ILS: 4.267 &nbsp; &nbsp; &nbsp; INR: 67.598 &nbsp; &nbsp;KRW: 1183.28 &nbsp; &nbsp;MXN: 16.1919 &nbsp; &nbsp; MYR: 3.9527 &nbsp; &nbsp;NZD: 1.4423 &nbsp; &nbsp;PHP: 48.3 &nbsp; &nbsp; &nbsp; &nbsp;SGD: 1.4724 &nbsp; &nbsp;THB: 35.335 &nbsp; &nbsp;ZAR: 12.8345</code>&nbsp;

Why is this clever? Because the ECB rates are:<ul><li data-list-item-id="e5f3c952abb440c715bcfa626ac9bd4c0">Publicly available -- anyone can fetch the same XML</li><li data-list-item-id="e7730a0d72c71be00a0c3d3feebc10618">Globally consistent -- every bot on every continent gets the same data</li><li data-list-item-id="e1667d4c4edd48ffbda05118e24153bf0">Unpredictable in advance -- nobody can predict next Tuesday's EUR/USD rate</li><li data-list-item-id="e80b9125dab13aae37cb9ed8b88656475">Historically verifiable -- the ECB publishes archives going back years</li><li data-list-item-id="e0d9bf0abba3c6cb7703705ce6203f707">Impossible to tamper with -- the ECB is not going to modify their reference rates to help you sinkhole a botnet</li></ul>And because the malware fetches this from <code>ecb.europa.eu</code>, the request looks like legitimate traffic. A Snort signature (SID 33188) was written to detect it, but it fired constantly on real users checking exchange rates.<h2>The mathematical core</h2>This is where Bedep stops being a clever botnet and starts being a discrete mathematics exercise.<h3>Precomputed subgroup parameters</h3>The DLL embeds a lookup table -- 102 entries of 8 dwords each, stored as JSON in the PoC (<code>transform2_table_varN.json</code>, one per config variant). At first glance it looks like random constants. It is not.The <code>transform2</code> function takes a hash derived from the exchange rates and days-since value, then uses it as an index into this table. Each table entry, after being decoded through config-specific XOR and multiply constants (<code>0x663d81 * entry[0] ^ value3</code> and <code>0x281 * entry[1] ^ value2</code>), yields two critical values:<ul><li data-list-item-id="ee4084ac1a9ee922a9a2585119d02acb9">p -- a prime number</li><li data-list-item-id="e7d298adce011a8fe31f7d5db7f1685ee">q -- an integer such that q divides p - 1</li></ul>This is the subgroup structure. The table stores primes whose group orders (p - 1) have known factorizations with small prime factors. The malware doesn't need to factor p - 1 at runtime -- that hard work was done offline by the author and baked into the DLL. Dennis Schwarz noted these were "likely something Fermat number related," and he was onto something: the primes are chosen so that their totient has a smooth factorization, making generator-finding tractable.The number of domains to generate comes directly from q - 1. For the first config, this works out to 22 domains; for the second, 28 -- totaling 50 domains per weekly rotation.<h3>Finding generators: A textbook primitive root search</h3>With p and the prime factorization of p - 1 in hand, Bedep needs a generator of the multiplicative group ((\mathbb{Z}/p\mathbb{Z})^*) -- or more precisely, of a specific subgroup of order q.The <code>transform8</code> function does this in two stages. First, it runs a trial-division prime sieve: starting from 3, stepping by 2 (skipping evens), it tests each candidate against all previously found primes. It collects up to 37 small primes, then filters for those that actually divide q (the subgroup order). This gives the prime factorization of q.Then <code>transform3</code> finds the primitive root itself. This is the standard algorithm straight from a number theory textbook:<code>def find_primitive_root(p, prime_factors_of_order): &nbsp; &nbsp;for g in range(2, p // 2): &nbsp; &nbsp; &nbsp; &nbsp;is_generator = True &nbsp; &nbsp; &nbsp; &nbsp;for q_i in prime_factors_of_order: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;# If g^((p-1)/q_i) ≡ 1 (mod p), then g generates &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;# a proper subgroup, not the full group. Reject it. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;if pow(g, (p - 1) // q_i, p) == 1: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;is_generator = False &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;break &nbsp; &nbsp; &nbsp; &nbsp;if is_generator: &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;return g &nbsp; &nbsp;return None</code>

The actual code in <code>transform3</code> is this logic rendered in C-style loop structure with manual index tracking, but the mathematics is identical. For each candidate g starting from 2, it checks:[ g^{(p-1)/q_i} \not\equiv 1 \pmod{p} \quad \text{for all prime factors } q_i \text{ of } p-1 ]If g passes all checks, it generates the full group. This is literally the algorithm from Section 11.1 of Shoup's A Computational Introduction to Number Theory and Algebra, implemented in a malware DLL.<h3>Walking the cyclic group</h3>Now comes the payoff. With a prime p, a generator g, and a subgroup order q, the <code>transform7</code> function sets up the DGA's iteration state:<code>modulus &nbsp; &nbsp;= p &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;# field_c seed &nbsp; &nbsp; &nbsp; = pow(g, random_e, p) &nbsp; &nbsp; &nbsp; &nbsp;# field_4 -- starting element step &nbsp; &nbsp; &nbsp; = pow(g2, random_f, q) &nbsp; &nbsp; &nbsp; # field_8 -- step exponent</code>

The starting position <code>seed</code> is g raised to a random exponent (derived from <code>rdtsc</code>, the CPU timestamp counter). The step is similarly randomized from a second generator. Both are computed via modular exponentiation.Then, for each domain, the iteration is simply:<code>seed = pow(seed, step, modulus) &nbsp; &nbsp; # one step through the cyclic group domain = seed_to_domain(seed, currencies)</code>

That single line -- <code>pow(seed, step, modulus)</code> -- is the entire DGA engine. Each call advances the state to the next element of the cyclic subgroup.Because the subgroup has order q, and step is coprime to q (guaranteed by the generator construction), this walk visits every non-identity element of the subgroup exactly once before cycling. The number of domains generated equals q - 1, which is precisely the number of non-identity elements. No collisions. No wasted iterations. No off-by-one hoping you hit enough domains.The key insight: the set of group elements visited is deterministic (fixed by p, g, and the exchange rates), but the order of visitation depends on the random exponents from <code>rdtsc</code>. Every infected machine walks through the same set of q - 1 elements, generating the same q - 1 domain names, but potentially in a different order. Same destinations, different paths.<code>flowchart TB &nbsp; &nbsp;subgraph cyclic_group ["Cyclic Subgroup of (Z/pZ)*"] &nbsp; &nbsp; &nbsp; &nbsp;E1["g^1 mod p"] --&gt; E2["g^2 mod p"] &nbsp; &nbsp; &nbsp; &nbsp;E2 --&gt; E3["g^3 mod p"] &nbsp; &nbsp; &nbsp; &nbsp;E3 --&gt; E4["..."] &nbsp; &nbsp; &nbsp; &nbsp;E4 --&gt; Eq["g^(q-1) mod p"] &nbsp; &nbsp; &nbsp; &nbsp;Eq --&gt; E1 &nbsp; &nbsp;end &nbsp; &nbsp;subgraph walk ["DGA Walk (order depends on rdtsc)"] &nbsp; &nbsp; &nbsp; &nbsp;S1["seed_0"] --&gt;|"pow(s, step, p)"| S2["seed_1"] &nbsp; &nbsp; &nbsp; &nbsp;S2 --&gt;|"pow(s, step, p)"| S3["seed_2"] &nbsp; &nbsp; &nbsp; &nbsp;S3 --&gt;|"pow(s, step, p)"| S4["..."] &nbsp; &nbsp; &nbsp; &nbsp;S4 --&gt;|"pow(s, step, p)"| SN["seed_(q-2)"] &nbsp; &nbsp;end &nbsp; &nbsp;S1 -.-&gt;|"= g^e mod p\n(random start)"| cyclic_group &nbsp; &nbsp;S2 -.-&gt;|maps to| E3 &nbsp; &nbsp;S3 -.-&gt;|maps to| Eq</code>

<h2>From Group Elements to Domain Names</h2>Each group element is a 32-bit integer. Turning it into a domain name is the final step, handled by <code>transform11</code>. This is less mathematically elegant and more of a traditional mixing function, but it has a nice property: every character position in the domain uses a different currency rate.The algorithm cycles through the full list of parsed currencies (31 in the example above). For a domain of length 18, that means character 0 is influenced by USD, character 1 by HRK, character 2 by MXN, character 3 by GBP, and so on. Each character is computed by:<ol><li data-list-item-id="e1b4d60fdb8fc1f153baf8a99855a0b35">Multiplying the currency's 3-letter name (packed as a 32-bit int) and its 64-bit floating-point rate by large constants</li><li data-list-item-id="ec05b47f97cd63ed0295b790fd5474c85">XORing and shifting the result with the current seed and the days-since value</li><li data-list-item-id="e2f1e2c5d769b5e8978ca12f1d05cf791">Taking the result modulo 26 (for positions 2+) or modulo 36 (for the last two positions, allowing digits)</li><li data-list-item-id="e589b38ae5aa632fba755388fc26bd71c">Adding the ASCII offset for 'a'</li></ol>The domain length itself is derived from the seed XORed with a rate-dependent value, bounded between 12 and 18 characters. All domains use the <code>.com</code> TLD.A trace from the PoC output shows the mixing in action:<code>seed: 0xa13c9652 &nbsp; &nbsp;mixing in USD's rate &nbsp; &nbsp;mixing in HRK's rate &nbsp; &nbsp;mixing in MXN's rate &nbsp; &nbsp;mixing in GBP's rate &nbsp; &nbsp;... &nbsp; &nbsp;mixing in ZAR's rate &nbsp; &nbsp;domain: rrpohktjlscncqxvt3.com seed: 0x558af439 &nbsp; &nbsp;mixing in USD's rate &nbsp; &nbsp;... &nbsp; &nbsp;domain: wjavcjhazzxyxotkbi.com</code>

Each domain is a fingerprint of the entire exchange rate vector, filtered through one specific element of the cyclic group.<h2>The Full Pipeline</h2>Putting it all together:<code>flowchart LR &nbsp; &nbsp;ECB["ECB XML\n31+ currency rates"] --&gt; DateSelect["Select last\nTuesday's rates"] &nbsp; &nbsp;Earth["earthtools.org\nUTC timestamp"] --&gt; DateSelect &nbsp; &nbsp;DateSelect --&gt; Mix["XOR/multiply\nrates + days_since"] &nbsp; &nbsp;Mix --&gt; TableLookup["Lookup table\n→ prime p, order q"] &nbsp; &nbsp;TableLookup --&gt; GenFind["Sieve primes,\nfind generator g"] &nbsp; &nbsp;GenFind --&gt; GroupSetup["seed = g^rand mod p\nstep = g2^rand mod q"] &nbsp; &nbsp;GroupSetup --&gt; Walk["Iterate:\nseed = seed^step mod p"] &nbsp; &nbsp;Walk --&gt; CharMix["Mix seed with\ncurrency rates"] &nbsp; &nbsp;CharMix --&gt; Domains["22-28 domains\nper config"]</code>

Seven configs were observed in the wild, each with its own embedded table, XOR constants, and currency count (36 or 48). Each malware variant embedded two configs that ran per rotation -- the first generating 22 domains, the second 28, yielding 50 domains total. Across the observed campaign, domains were registered 2-5 days ahead of use through "Domain Context" registrar with Regway nameservers, all resolving to infrastructure at 5.196.181.244 and 46.105.251.1.<h2>Why this matters</h2>Compare Bedep's approach with its contemporaries:<table class="table"><thead><tr><th>Family</th><th>DGA Technique</th><th>Seed</th></tr></thead><tbody><tr><td>Conficker.C</td><td>Date + simple arithmetic</td><td>Current date</td></tr><tr><td>Gamaredon</td><td>Nested character-range loops</td><td>Hardcoded ranges</td></tr><tr><td>Necurs</td><td>CRC32 hash of date components</td><td>Date + constant</td></tr><tr><td>Matsnu</td><td>Dictionary word concatenation</td><td>Days since epoch</td></tr><tr><td>Bedep</td><td>Cyclic group walk mod p</td><td>ECB exchange rates</td></tr></tbody></table>Every other DGA on this list can be pre-computed arbitrarily far into the future. Bedep cannot -- because Tuesday's exchange rates don't exist until Tuesday. You can sinkhole Conficker's domains for next year during your coffee break. For Bedep, you have to wait for the ECB to publish, then race the botmaster to register the domains.The mathematical guarantee is real: the cyclic group structure ensures exactly q - 1 distinct domains per rotation with no collisions and no wasted iterations. The primitive root construction ensures the generator produces every element. The precomputed tables avoid expensive factorization at runtime. The exchange rate seed provides unpredictable entropy from a trusted, globally-consistent, publicly-verifiable source.This is not someone who copy-pasted a DGA from a forum. The author understood multiplicative groups modulo primes, knew how to find primitive roots, precomputed smooth-order primes offline, and wired the whole thing to the European Central Bank's daily publications. Whether they learned this from a university course, a textbook, or a cryptography library's source code, the result is unmistakable: this is applied algebra, deployed in production, at scale.The original ASERT analysis is preserved on the <a href="https://web.archive.org/web/20160311083109/http://www.arbornetworks.com:80/blog/asert/bedeps-dga-trading-foreign-exchange-for-malware-domains/">Wayback Machine</a>. The proof-of-concept implementation was published on <a href="https://github.com/arbor/bedep_dga">ASERT's GitHub</a>. The sample analyzed here is <code>e5e72baff4fab6ea6a1fcac467dc4351</code> (MD5) /&nbsp;<code>d0fb1b66b6e4da395892327be9f39adb4533e7759ace39f67bdde0bb1cdaef35</code> (SHA256).&nbsp;Credits: The DGA was originally reversed by Dennis Schwarz at Arbor Networks / ASERT. This post builds on his work, focusing on the mathematical structure he identified but didn't fully unpack.

A closer look at how Bedep used foreign exchange data and advanced math to generate hard-to-predict domains, making its command-and-control infrastructure more difficult for defenders to block and disrupt

When Malware Authors Study Algebra: The Group Theory Inside Bedep's DGA

A VM-obfuscated backdoor observed on a single machine in the UK, operated for one year, and vanished without a trace. 

Chasing an Angry Spark

In today’s digital age, where cybersecurity concerns touch every aspect of our lives — from personal privacy to global business operations — staying informed is crucial. As cyberthreats continue to evolve and cyberattacks persist, our analysis of the second quarter reveals key trends, insights and strategies to protect yourself and your organization.<h2>The Cyberthreat Landscape: What We Saw April - June 2024</h2>If you’re wondering how many cyberattacks are happening daily, here’s the sobering truth: our family of brands blocks over a billion cyberattacks every single month. Yes, a billion. The overwhelming majority (90%) of these attacks come from <a href="https://us.norton.com/blog/emerging-threats/what-is-social-engineering">social </a><a href="https://us.norton.com/blog/emerging-threats/what-is-social-engineering" target="_blank" title="What is social engineering?">engineering</a>, which targets human psychology rather than technical systems. The goal? Trick you into making mistakes that lead to data breaches. Even though the overall number of attacks slightly declined from April to June, cybercriminals are evolving faster than ever. Total threats were 46% higher than in 2023, and it’s clear that no one can afford to lower their guard.<h2>Social Engineering Attacks: How Scammers Trick You</h2>When it comes to cybersecurity threats, social engineering is public enemy number one. More than 90% of all cyberthreats in 2024 so far were driven by this method. Scammers prey on human nature, getting users to click on malicious links, share passwords or download malware. The most important line of defense? User education and cyber awareness training to recognize and avoid these traps before it’s too late.<h2>Web Browsing: The Riskiest Time Online</h2>Ever wondered when you’re most vulnerable to cyberattacks? The answer is simple: while browsing the web. A staggering 95% of attacks occur while you’re online, making <a href="https://us.norton.com/" target="_blank" title="Norton: real-time threat protection">real-time threat protection</a> a must-have. Although there was a small dip in threats from April to June, it’s no cause for celebration. The landscape is still dangerous, with scammers constantly adapting their methods to catch you off guard.<h2>Fake Antivirus Scams: Back and More Dangerous Than Ever</h2>You may think you’re too savvy to fall for a<a href="https://us.norton.com/blog/emerging-threats/fake-antivirus-scam" target="_blank" title="Antivirus scams"> fake antivirus scam</a> but think again. These scams are making a comeback, and they’re more convincing than ever. Scammers bypass typical security warnings to push fake virus alerts, tricking users into buying fake security software to ‘fix’ non-existent problems. Cybercriminals are banking on increased anxiety around cybersecurity threats, and they’re capitalizing on it.<h2>What Scams You Should Look Out For?&nbsp;</h2>Scammers are diversifying their attacks, targeting everyday people through multiple fronts. Some of the most common types of scams in Q2 2024 included:<ul><li>Job Scams: Promising lucrative <a href="https://www.gendigital.com/blog/news/innovation/part-time-job-scams" target="_blank" title="Part-time job scams">part-time jobs</a> or quick investments that sound too good to be true. These scams often lead to stolen money or personal information.</li><li>Online Dating Scams: Fraudsters create fake online profiles, build relationships with their victims, then ask for financial support or personal data.</li><li>Event-Related Scams: With big events like elections and concerts, scammers create fraudulent donation campaigns or phishing websites to steal your money and information.</li></ul><h2>The Rise of Identity Theft in 2024</h2>If you’re concerned about identity theft, you’re not alone. Threats targeting personal information are on the up and up, particularly through tools like information stealers (e.g., AgentTesla) and mobile banking trojans. These types of malware harvest sensitive data, whether from your desktop or your mobile device and then use it for fraud or theft.&nbsp;For 2024 and beyond, <a href="https://lifelock.norton.com/" target="_blank" title="Identity Theft Protection">protecting your personal information</a> should be a top priority.<h2>Ransomware Attacks Surge in 2024</h2>One of the most talked-about cybersecurity threats in 2024 is the rise in <a href="https://blog.avast.com/threat-report-q2-2024" title="ransomeware increase">ransomware attacks</a>. These attacks increased by 24% this quarter, with hotspots in the US, UK and India. Ransomware isn’t just about locking up your files anymore — criminals are using it to target businesses, government institutions and even individuals.We’ve released free decryption tools for ransomware victims (like those targeted by DoNex), and we’re actively working to disrupt the infrastructure behind ransomware attacks. If you’re concerned about ransomware, stay updated with the latest cybersecurity tools and software.<h2>Botnet Crackdown: Major Wins in Q2 2024</h2>One bright spot this quarter was the takedown of several botnets — networks of compromised computers used to launch massive attacks.<ul><li>Operation Endgame: Led by Europol, this operation targeted key botnet providers, leading to multiple arrests and significant setbacks for malicious operations. The impact of these botnets represents a big blow to cybercriminal networks.</li><li>U.S. Department of Justice Actions: The DOJ played a pivotal role in dismantling the notorious 911 S5 botnet. Additionally, actions against the DDoSia botnet led to several arrests, further disrupting the operations of these malicious entities.</li><li>Remote Access Trojans (RATs): Operations aimed at combating RATs, such as Hive, have yielded positive results. These efforts have effectively addressed threats that grant unauthorized access to systems, contributing to enhanced overall cybersecurity.</li></ul><h2>The Future of Cybersecurity: Innovation, Collaboration and Training</h2>Looking ahead, cybersecurity innovation will hinge on two things: advanced technologies like artificial intelligence (AI) and machine learning (ML), and collaboration between cybersecurity experts, policymakers and industry leaders. AI and ML allow us to detect and respond to threats faster, smarter and more efficiently. Combine that with skilled cybersecurity professionals and robust training programs, and we CAN and WILL stay ahead of evolving threats.<h3>A Future-Ready Approach to Cybersecurity</h3>As technology continues to advance, organizations must remain agile and forward-thinking. The second quarter of 2024 has shown us that the fight against cybercrime is relentless. But, at Gen, we’re doubling down on our commitment to protect digital freedom. From social engineering to ransomware, identity theft to botnets, we’re here to help you navigate these challenges and stay safe online. The future of cybersecurity isn’t just about defense; it’s about innovation, growth and creating a safer digital space for all.Explore more about the latest cybersecurity threats and how you can protect yourself. Read the <a href="https://www.gendigital.com/blog/news/innovation/q2-2024-threat-report" target="_blank" title="Q2 2024 Threat Report">full threat report here</a>.

Discover the latest cyberthreats — social engineering, ransomware and identity theft — and learn how to protect yourself

Q2 2024 Cybersecurity Trends: Rising Threats from Ransomware, Social Engineering and Identity Theft

Corporate Communications Intern

As the world’s most elite athletes gather for this summer’s competitions, cybercriminals are seizing the opportunity to exploit the elevated attention. These events showcase athletic excellence, attracting fans worldwide, but on the downside – they also attract malicious actors.Alongside some of the fastest and strongest humans on Earth, cybercriminals have set their own new records.&nbsp;With Summer 2024 ripe for global sporting events, cybercriminals took advantage of the topic. In the first half of the year alone, Gen saw an increase of 942% in sport-related scams compared to the same period in 2023. In past sporting events, scam and phishing attacks increased by 280% during the previous Summer Games and by 238% during the last Winter Games.&nbsp;This year, we have already witnessed a surge in scams related to France.&nbsp;In the first six months of 2024, scams tied to the events surged by 942% compared to the same timeframe in 2023.&nbsp;Here’s a look at fraud related to past games.&nbsp;

<h2>Emerging Threat Landscape</h2>As competitions continue in France, organizations and individuals should be vigilant of these five threats:1. Scam and Phishing Attacks Cybercriminals exploit the popularity of these events by disseminating fake emails and messages that impersonate official sources. These communications often offer counterfeit tickets, merchandise, or urgent updates, aiming to deceive recipients into divulging personal information or clicking on malicious links.2. Fake Ticket and Resale Scams High demand for tickets creates a lucrative environment for criminals to sell counterfeit tickets through fraudulent websites or third-party resellers, leading to financial losses and identity theft. SEVERAL WEBSITES TO AVOID: paris24tickets[.]com, paris24tickets[.]net, liveticketfinder[.]com, tickets-paris24[.]com&nbsp;Below are two examples of fake websites.

3. Fake Streaming Sites&nbsp; With the surge in demand for live event streams, attackers establish fake streaming websites designed to infect devices with malware or steal personal information.4. Malicious Mobile Apps Mobile apps claiming to offer live updates, scores or exclusive content can sometimes be used to install harmful software on devices or to steal personal data.5. Wi-Fi Security Risks Public Wi-Fi networks around event venues and fan zones are prime targets for hackers seeking to intercept personal data.<h2>Ways to Protect Yourself</h2>Source Verification and Link Caution.&nbsp;Always authenticate the legitimacy of sources before engaging with them. Verify URLs and contact information to ensure they are genuine. One tip is to assess the reviews and terms of service of a website. Scam sites often lack credible reviews and have confusing or illogical terms. Exercise caution when encountering unfamiliar links in emails or online. Avoid clicking on any links unless you are certain of their authenticity.Secure Payment Methods.&nbsp;Use secure and verified payment methods. Legitimate partners will not request payments via unconventional methods such as gift cards or wire transfers.Turn to Official Channels.&nbsp;For reliable information, refer to the official websites of partners and authorized ticket sellers.Use an AI-powered Scam Detection App.&nbsp;Cyberthreats like phishing emails, counterfeit tickets, and fake websites pose significant risks to attendees and fans.&nbsp;<a href="https://us.norton.com/products/genie-scam-detector">Norton Genie</a>, an AI-powered scam detection tool, can help keep people safe by reviewing websites, texts, and emails, quickly identifying potential scams and providing peace of mind amid heightened security concerns. Its advanced detection capabilities help ensure users can confidently navigate digital interactions without falling victim to cyber fraud.The allure of major sporting events is not only limited to fans and athletes but also the cybercriminals looking to exploit the excitement. However, with informed vigilance and strategic measures, you can safely navigate the digital world and enjoy these games without falling victim to cyberthreats.Be proactive when it comes to your cybersecurity, to help ensure your digital safety, privacy and confidence during this year's events in France.

Rising threats during the world’s biggest sporting event call for increased awareness

Stay Alert: Beware of Scams Targeting Major Sports Fans 

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Gen has a family of trusted consumer brands that for decades have helped people stay safe in the digital world. We pride ourselves in the trust we’ve built with people around the world to navigate the latest technology safely and with confidence. And that’s exactly why we recently launched the Gen Agent Trust Hub – our trust layer for the agentic AI ecosystem.AI agents are no longer theoretical. They are here. They are editing files, installing packages, calling APIs, connecting to MCP servers, spawning sub-agents, persisting memory, and executing real workflows across environments. This shift from passive AI to autonomous execution fundamentally expands the attack surface.The Agent Trust Hub (ATH) exists to meet this moment. It is designed to provide continuous protection across the full lifecycle of an AI agent – from identity and verification to runtime enforcement. But security at this scale cannot rely on proprietary guardrails alone. It requires shared foundations the industry can build on.To help establish that foundation, we created the AI Agent Safety Standards by Gen to serve as a unified framework designed to bring consistency, portability, and accountability to agentic systems. This framework is currently built on two complementary pillars:<ul type="disc"><li data-list-item-id="e464ce0354d35c1a986c83945bc06b0cd">AI Agent Runtime Safety Standard (AARTS) – an open standard for runtime decision enforcement across agent hosts</li><li data-list-item-id="e4a7d4ceb7039019b7c2554401a8bb1c3">Skill IDs – a deterministic, content-addressable fingerprinting system for AI agent skills</li></ul>Together, they enable the ATH and the broader agentic AI community to enforce trust not just within one environment but across the broader agentic ecosystem. &nbsp;Let’s dig into the details.&nbsp;<h2>Pillar One: Standardizing Runtime Safety for AI Agents</h2><h3>AARTS</h3>AI agents operate across a fragmented ecosystem of IDEs, orchestrators, frameworks, and standalone applications. Each host defines its own lifecycle events, security hooks, and enforcement logic.This fragmentation creates blind spots. A security engine integrated into one host may lack context or control in another, even when the risks are identical.AARTS&nbsp;addresses this gap. It is not a product. It is a vendor-neutral contract that defines:<ul type="disc"><li data-list-item-id="e226fc797549b56b0ae0a537923fd79f6">Where security decisions can be made (hook points)</li><li data-list-item-id="e7566ceb595a0278da9453bbd84659578">What data is available for evaluation (data model)</li><li data-list-item-id="e637f36bcc4e8dcfac7321ad59230c1c1">How decisions are enforced (verdict semantics)</li></ul>Simply put:&nbsp; AARTS standardizes runtime security decision-making across agent hosts.<h3>The Architecture</h3>AARTS defines three cleanly separated roles:<ol><li data-list-item-id="e7a445b9a896b9b89299afa17ca58c69e">Host Runs the agent, emits lifecycle events, and enforces decisions.</li><li data-list-item-id="e989b722cd161ff069c924d70571cd6b7">Adapter Maps host-native events into a standardized AARTS schema.</li><li data-list-item-id="ed862f4398b4b82c3b52c721801728a9c">Security Engine Evaluates events and returns structured verdicts.</li></ol>This separation prevents superficial “checkbox security” while preserving portability. Hosts provide the right context at the right time. Engines make decisions without depending on host internals.In ATH, this architecture allows enforcement logic to remain portable while enabling hosts to integrate in a predictable way.<h3>Hook Points: Where Safety Happens</h3>AARTS v0.1 defines 19 hook points across the agent lifecycle, including:<ul type="disc"><li data-list-item-id="ec5255705352d810833f77d79b310a42d">PreToolUse — evaluate shell commands, file writes, web requests, package installs</li><li data-list-item-id="ee1ebbdf3e4522a59dedaa55c0305d903">PreLLMRequest — protect prompt integrity and instruction layering</li><li data-list-item-id="e10ef155bfe4c584afd7492da267796be">PreSkillLoad / PrePluginLoad — enforce supply chain controls</li><li data-list-item-id="ee6184f1048dfbf4ee3c0e62c2d3dff16">PreMCPConnect — treat MCP connections as trust boundaries</li><li data-list-item-id="ebc57267bf0e891e01f9daf277ced1a01">PreMemoryRead / PreMemoryWrite — protect persistent memory</li></ul>These hooks enable consistent policy enforcement regardless of whether an agent runs in an IDE, CLI, or orchestrator.Verdict Semantics: Allow, Deny, AskAARTS engines return structured verdicts:<ul type="disc"><li class="ck-list-marker-bold" data-list-item-id="e9615d2d55e251652e73363c052de0bd1">Allow</li><li class="ck-list-marker-bold" data-list-item-id="eb510a0ee27b0a853b90ff01cec91953d">Deny</li><li class="ck-list-marker-bold" data-list-item-id="ed6e8c58ce99af62dddbf6b530bc0c83c">Ask</li></ul>Each verdict includes severity, threat category, optional confidence scoring, and human-readable reasoning.Importantly, AARTS enforces deterministic behavior:<ul type="disc"><li data-list-item-id="edfce7cb3a277e1da6e442c971f8cc8e1">If a host cannot support interactive “Ask,” it defaults safely to Deny</li><li data-list-item-id="ec790330b39c695d6cdacf4118e106c1b">If an engine fails, hosts fail-open and log explicitly</li></ul>These operational tradeoffs are intentional. Predictability is essential in security infrastructure.<h3>Why AARTS Matters</h3>Without a shared runtime contract:<ul type="disc"><li data-list-item-id="e2574817ec2bfc1a1b0233c5fca970994">Security integrations are bespoke and brittle</li><li data-list-item-id="ebbe5c354debe741cc08436719a34032e">Tool taxonomy is inconsistent</li><li data-list-item-id="e2f498a30e5d748e37c3176b169145707">Prompt injection controls vary by host</li><li data-list-item-id="e3aff5387909fd85f523c40998fdb13b0">Supply chain risk is unevenly enforced</li></ul>With AARTS:<ul type="disc"><li data-list-item-id="eaa8f402b20b740f0992dd0f9b4f42b44">One engine can operate across multiple hosts</li><li data-list-item-id="e3ddcda4fa65f1abd066be4b0bef0201b">Policies remain portable</li><li data-list-item-id="e5ec3076209374ae5eafb39a00f19adf4">Audit logs become comparable</li><li data-list-item-id="e6f17fa0cc0a6a6e0136b63569cdc58a6">Trust boundaries are explicit</li></ul>AARTS standardizes enforcement in the agentic world.To read the full technical breakdown, see the full AART&nbsp;<a href="https://www.gendigital.com/blog/news/company-news/ai-agent-runtime-security">technical blog post</a>. To comment on the standards, visit our page on&nbsp;<a href="https://github.com/gendigitalinc/aarts">GitHub</a>.<h2>Pillar Two: Skill IDs</h2><h3>Fingerprinting AI Agent Skills</h3>AI agents are extended through skills – directory bundles containing a SKILL.md prompt file alongside scripts, templates, configuration, and documentation.These skills function like plugins. Install one, and the agent gains new capabilities.But skills are not single files. They are directory trees.And directory trees are notoriously difficult to fingerprint reliably.<h3>The Identity Problem</h3>A naive SHA-256 hash of a ZIP file does not work:<ul type="disc"><li data-list-item-id="e6fc83ba16d130bd7d3d62ba2af79f96d">ZIP files are non-deterministic</li><li data-list-item-id="ee7939e0ca570cb480cbdc5b8e5460ece">Timestamps vary</li><li data-list-item-id="e5d398fbba0c41d53d0e3782902bce4c1">Entry order differs</li><li data-list-item-id="e95611c8e95e6ad4f16eccdede99ac42c">Unicode normalization differs across OS platforms</li><li data-list-item-id="ef8771bdef58d5438cc192edc36e89c56">Wrapper directory names vary</li></ul>Two identical skills can produce different ZIP hashes.That is unacceptable for security workflows.<h3>Designing the Skill ID</h3>Skill IDs solve this problem by creating a deterministic, content-addressable identifier for a skill’s logical directory tree.The algorithm:<ol><li data-list-item-id="ebdc8f7c1fc531f4ccbd79f2baf7b6d83">Extract all files and directories</li><li data-list-item-id="e06cf4d178c29a57575a249d22d5cb9b4">Normalize paths (slashes, Unicode NFC, dot components)</li><li data-list-item-id="e69f139d48a1c91815267ffb649c74e07">Strip wrapper directories</li><li data-list-item-id="e07c1da87b4b07a634d73a79ab93f9134">Hash each file individually (SHA-256)</li><li data-list-item-id="ef7cb735107df0cafdbcc921e639c0212">Build sorted tree entries using null-byte delimiters</li><li data-list-item-id="eab8b3782aab3b4dd9b0f4aed3255caf8">Hash the tree to produce the final Skill ID</li></ol>The result is a 64-character SHA-256 digest that changes if and only if meaningful content changes.Same content, different ZIP packaging? Same ID. One byte changed? Different ID.This mirrors git’s tree hashing model and decades of security best practices.<h3>What Skill IDs Enable</h3>A stable identity primitive unlocks established security workflows:Allowlisting Approved skills can bypass repeated analysis.Content-addressable storage Deduplicate identical submissions automatically.Per-file caching Reuse expensive AI analysis on unchanged files.Detection flagging Connect file hashes to reputation systems.Version tracking Content becomes the version boundary.Skill IDs provide identity before verdict.<h3>From Identity to Authenticity: Signing Skills</h3>Identity answers “what is this?”Authenticity answers “who published this?”We are exploring a signing model inspired by Authenticode:<ul type="disc"><li data-list-item-id="e2103c63753a0f24a87fa144d75611c7a">A skill.sig file embedded inside the directory</li><li data-list-item-id="e28e6643465e0cabeea54eb4bc5302028">Excluded from the Skill ID calculation</li><li data-list-item-id="e700fd96bf7f3d0db4aa70df0a92d57ec">Contains a signature over the Skill ID</li><li data-list-item-id="ea2483e766cae2defa9d2dd19707d9302">Verifiable against the author’s public key</li></ul>This enables:<ul type="disc"><li data-list-item-id="eddf73c0b79d7d482c030736b4f449be8">Author signing</li><li data-list-item-id="e3d708097518f6e38a31a73aaf27ecb49">Marketplace attestation</li><li data-list-item-id="e81ca5157252eece76405ff632c0d6722">Certificate chains</li><li data-list-item-id="efe336aa9f477f99f141fc5a81f887d9b">Revocation workflows</li></ul>Self-verifiable skills create accountability without external lookups.To read the full technical breakdown, see the full <a href="https://www.gendigital.com/blog/insights/research/ai-agent-skill-fingerprinting" target="_blank" rel="noreferrer noopener">SkillsID technical blog post</a>. To comment on the standards, visit our page on <a href="https://github.com/gendigitalinc/skill-id-standard">GitHub</a>.&nbsp;<h2>The AI Agent Safety Standards by Gen: Identity + Enforcement</h2>AARTS governs runtime enforcement. Skill IDs govern artifact identity.Together, they form the foundation of the AI Agent Safety Standards.<ul type="disc"><li data-list-item-id="ea3ef208939f34e4fcfaf98bd8e26b2d6">Skills can be uniquely identified, versioned, and signed.</li><li data-list-item-id="e30dceebd279e7a291dc71a82c7168ab2">Runtime actions can be evaluated consistently across hosts.</li><li data-list-item-id="e1a7b0d4259c4abb5e6ba2f38dffd6aaa">Supply chain trust and runtime enforcement operate under shared semantics.</li></ul>This is the same model that matured endpoint security over decades:<ul type="disc"><li data-list-item-id="ebacb70fb4a6565c103650a6c6ee67532">File hashes</li><li data-list-item-id="ed254dd1eec0c62e2aea6bce82e871995">Content signatures</li><li data-list-item-id="e37a3133a17b4a1adca017b868990baa6">Runtime enforcement</li><li data-list-item-id="eeeb5f5d7a84badb31f60600269d24edf">Portable policy engines</li></ul>We are applying those proven patterns to a new artifact type and a new execution model.<h2>Looking Ahead</h2>These standards are early. AARTS v0.1 is a draft. Skill ID signing is an evolving proposal. Adapter conformance testing, enterprise fail-open profiles, and cross-marketplace reputation sharing remain active areas of collaboration.But the direction is clear.AI agents are moving from experimentation to infrastructure. Security must do the same.The Gen Agent Trust Hub is our contribution to building a predictable, interoperable trust layer for the agentic ecosystem.&nbsp;We invite host builders, marketplace operators, enterprise security teams, and fellow security vendors to review, adopt, and challenge.Because in the agentic era, safety cannot be bespoke. It must be foundational.&nbsp;

Shaping the standards for a safer, freer Agentic Era 

Leading the Way for AI Agent Safety 

Director of Threat Research & Applied AI

We built a security engine that protects AI agents across several major platforms. The hardest part wasn't building the protection. It was discovering that each platform exposes different security-relevant events, provides different context about what the agent is doing and why, and enforces security decisions differently. The same agent action we can catch and block in one platform is invisible in another, not because our detection failed, but because the platform never told us it happened. There are now over a dozen agent platforms, from IDE assistants like Claude Code, Cursor, and GitHub Copilot, to agentic frameworks like LangChain, CrewAI, and OpenAI Agents SDK, to orchestrators like n8n, and each one handles this differently, if at all.Today, Gen is releasing AI Agent Runtime Safety Standard (AARTS) as part of its Agent Trust Hub initiative, which aimsto address this fragmentation. This is not a security product, but rather a set of vendor-neutral standards that connects agent hosts (IDEs, orchestrators, frameworks, standalone agent apps), security engines (components that evaluate agent actions), and adapters (thin mappings that translate host-native events into a common schema). For those building or operating agents, AARTS enables portable, consistent security decisions across hosts, eliminating the need to rebuild security integrations from scratch.&nbsp;The standard is open and vendor-neutral, and any host or security vendor can implement or contribute to it.<h2 class="text-align-justify">The Problem AARTS Addresses</h2>Agentic systems fail in ways that are familiar but occur more rapidly and with greater reach. Examples include sequences evolving from helpful actions to dangerous activities like&nbsp;"download and execute," prompt injection steering tool use, supply chain issues affecting skills and plugins, sensitive data exposure by agents, and permission confusion resulting from sub-agents and external tool providers creating additional trust boundaries. Although these risks are not new, they now manifest within a rapidly diversifying set of hosts and tool APIs.Without a shared contract, security becomes a collection of one-off integrations, each missing context and offering inconsistent enforcement. This leads either to weak coverage, due to incomplete integrations and context, or to overly blunt blocking when trust is undermined by limited interpretation. AARTS targets the root cause: the lack of shared semantics and hook points.In our previous&nbsp;<a href="https://www.gendigital.com/blog/news/company-news/ai-agent-detection-and-response" target="_blank">post on Agent Detection and Response</a>, we described how attackers register AI-hallucinated package names on npm and PyPI, preloaded with infostealers. Catching that requires seeing the package install action with enough context — the package name, the source, whether the agent hallucinated it or the user requested it. One platform gives us all of that. Another gives us the command string with no structured metadata. A third doesn't emit the event at all. Same attack, three different levels of visibility.Simply put, AARTS standardizes where security decisions can be made (hook points), what data is available for decision-making (data model), and how decisions are enforced (verdict semantics), ensuring consistent protections across different agent hosts.<h2 class="text-align-justify">Architecture and Roles</h2>AARTS defines three distinct roles:<ul type="disc"><li data-list-item-id="ef43f78807d68ed5d7cafc41e43ed6a77">Host: The environment that runs the agent. The host emits lifecycle events at defined hook points and enforces decisions.</li><li data-list-item-id="ea8ecfedda9151e1e4ff45954fc0bd003">Adapter: A minimal translation layer that maps host-specific events into AARTS events, normalizing tool names, lifecycle phases, and context.</li><li data-list-item-id="ec84e412023baa1359b60a4c189c0ce73">Security Engine: The evaluator that receives AARTS events, applies detection strategies, and returns structured verdicts.</li></ul>This separation matters. Embedding security logic in hosts results in superficial security ("checkbox security"), while engines depending on host internals compromise portability. AARTS keeps the boundary clean: hosts provide the right moments and context, and engines make decisions. AARTS also supports various integration models (in-process, subprocess, remote), accommodating different latency, isolation, and operational requirements.<h2 class="text-align-justify">How the Standard Works</h2>AARTS defines hook points, moments in an agent's lifecycle where a security engine can intercept, evaluate, and act. Version 0.1 specifies 19 of them across session management, prompt lifecycle, tool execution, plugin and skill loading, sub-agent behavior, memory operations, and output delivery.Not all hooks carry equal weight. Three illustrate the design logic:<ul type="disc"><li data-list-item-id="e9e499b9374a4cd1f71378518f359c0d6">PreToolUse is the primary checkpoint. Before an agent runs a shell command, writes a file, makes a network request, or installs a package, the security engine sees the action, its arguments, and the context that led to it, then returns a verdict: allow, deny, or ask the user. This is where most real-world threats get caught.</li><li data-list-item-id="ebd37c3ab71aca6ef9cddd1080d793f75">PreLLMRequest operates earlier in the loop, before the prompt reaches the model. It lets engines inspect prompt integrity: whether safety instructions survived context compaction, whether untrusted content was injected into the conversation, whether the instruction layering is intact. If PreToolUse is the checkpoint at the door, PreLLMRequest watches for problems before the agent even decides what to do.</li><li data-list-item-id="ecce291342cb701e4290928d827faf930">PreSkillLoad addresses supply chain risk. When an agent installs a plugin or skill from a marketplace, the engine can evaluate the trust source (where it came from, whether it's signed, whether it was modified) before it gets loaded. AARTS can also direct the host to strip hooks from skills, preventing plugins from disabling the security layer that's supposed to govern them.</li></ul>The remaining 16 hooks follow the same pattern: they sit at the moment where risk becomes real and provide the engine with enough context to make a decision.For those decisions to be portable across hosts, the inputs have to be consistent. AARTS standardizes the event structure: every event carries a mandatory envelope (hook point, session ID, timestamp, host identity) plus standard extensions, reusable field groups for conversation context, tool details, trust source metadata, modification tracking, and provenance. A canonical tool taxonomy maps host-specific tool names (Cursor's "terminal" vs. Claude Code's "bash") to common categories (shell, file_write, web_request, package_install), so detection logic written once works everywhere.Verdicts are structured: a decision (allow, deny, or ask), a threat category, a severity level, confidence, and a human-readable explanation for the user. Two design choices are worth noting. If the host can't prompt the user, ask falls back to deny, safe but a real constraint for headless orchestrators. If the engine crashes or times out, the host treats it as allow and logs the failure. Fail-open is the right default for developer tools where latency matters, but enterprise deployments will want to revisit this per environment.<h2 class="text-align-justify">Conformance Levels: Gradual Adoption</h2>AARTS defines three conformance levels:<ul type="disc"><li data-list-item-id="e18c7418214ef4f97b683f20402deb227">Level 1 (Basic): Requires SessionStart and PreToolUse hooks, minimum tool coverage (shell, file_write, web_request), enforcement of allow and deny (ask may be treated as deny).</li><li data-list-item-id="e0369d9837c299bdfd5a5c490fb899318">Level 2 (Standard): Adds plugin and skill load hooks, prompt lifecycle protection, broader tool coverage, additional hooks (PrePluginLoad, PreSkillLoad, PostInputProcessing, PreLLMRequest, PostToolUse), and requires provenance on PreToolUse.</li><li data-list-item-id="e31f70f735daa583aec2ef8c2d180686d">Level 3 (Comprehensive): Provides full lifecycle coverage including user input, prompt response cycle, tool modification, sub-agents, MCP connect, memory read/write, output delivery, and session end. Clarifies sub-agent inheritance and memory protection.</li></ul>This tiered approach offers a realistic adoption path: Level 1 addresses basic failures, Level 2 tackles prompt integrity and supply chain issues, and Level 3 supports persistent and autonomous agents.<h2 class="text-align-justify">Open&nbsp;Considerations</h2>As we move AARTS beyond a draft specification,&nbsp;the following are being considered:<ul type="disc"><li data-list-item-id="ef56e3529222a5671c75f8e80fbc5c2c9">Enterprise fail-open story: The spec mandates fail-open behavior. Deployment profiles may be needed to reflect the realities of different environments.</li><li data-list-item-id="e5e2beee0ceee16ba315173c83dcc4596">Adapter integrity: Ensuring adapters correctly map tool types and extract artifacts is vital. A conformance test suite for adapters is as important as the specification itself is a next-step priority.</li><li data-list-item-id="eea2912c8ee60ab64a8339e4d3c2b5748">Consent for privacy: Explicit consent is required for external transmission of prompts and tool data. Operational guidance or recommended patterns are needed to avoid incompatible implementations.</li><li data-list-item-id="e7dbd49139e599330dbad43c59209bc82">Ask semantics in headless environments: Treating ask as deny by default is safe but may discourage engines from using ask. A standard way for deployment policy to handle ask in these scenarios is needed.</li></ul>AARTS aims to translate&nbsp;these considerations into predictable operational behavior.<h2 class="text-align-justify">Call for Feedback</h2>AARTS v0.1 is a draft intended for early review by host builders, security vendors, and enterprise operators.&nbsp;The full specification is available <a href="https://github.com/gendigitalinc/aarts" target="_blank">here</a>. If you build agent hosts, consider whether you want every security integration to remain one-off. If you develop security engines, ask yourself whether you want to rewrite the same logic for each host and still miss critical context. AARTS aims to make the answer “no”.&nbsp;We welcome contributions — open an issue or submit a pull request.

AARTS standardizes how AI agent platforms expose security events, enabling portable runtime protection across the growing agent ecosystem

Introducing AARTS: An Open Standard for AI Agent Runtime Safety

You gave an AI agent access to your computer. Maybe it's writing code for you, managing your emails, or handling your finances. It's working with your credentials, acting on your behalf, and making decisions you used to make yourself. Fast, confidently, and largely unsupervised. That's the deal: you trade safety and oversight for productivity. That changes the security model.But who's watching what it actually does?That is why we built the Gen Agent Trust Hub (ATH), our trust layer for the agentic AI ecosystem, designed to secure agents across their lifecycle from skill to runtime to enforcement.Agent Detection and Response (ADR) is the runtime pillar — the layer that governs what agents actually do.<h2 class="text-align-justify">What is ADR?</h2>ADR intercepts agent actions at the moment of execution, before they land, and evaluates whether they're safe. It allows, asks the user, or denies. That's the core.Verification tells you what an agent is. ADR tells you what it's doing. These are complementary. Verification is necessary, but it can't govern what happens at runtime. A verified skill can still hallucinate a malicious package name. A signed plugin can still be exploited through prompt injection. A trusted agent can still lose its safety instructions mid-session and go off the rails. The moment of action is where risk becomes real.The principle isn't new. Every major computing paradigm has developed its own detection and response layer. Endpoints got EDR. Distributed infrastructure got XDR. Not replacements for what came before, but purpose-built layers for new execution surfaces with new threat models. AI agents are the next such surface, and ADR is the layer built for it.Within the Agent Trust Hub, ADR is the enforcement engine that turns trust signals into real-world protection.<h2>Why Now?</h2>The threats targeting AI agents are already here. What follows is a small sample of the agentic threats we’re seeing. The actual attack surface spans everything an agent can do, and agents can do a lot.Your agent installs malware for you. AI agents hallucinate. When they hallucinate a package name during installation, that's not just an error, it's an opportunity. Attackers monitor AI-suggested package names and register them on npm and PyPI, pre-loaded with infostealers and backdoors. The technique is called slopsquatting, and it's already being exploited at scale. In February 2026, researchers uncovered a campaign of&nbsp;<a href="https://www.securityweek.com/new-sandworm_mode-supply-chain-attack-hits-npm/">19 typosquatted packages specifically</a> targeting users of AI coding tools, designed to exfiltrate SSH keys, AWS credentials, and API tokens.Your agent ignores your instructions. Summer Yue, Meta's Director of Alignment, told her OpenClaw agent to&nbsp;<a href="https://techcrunch.com/2026/02/23/a-meta-ai-security-researcher-said-an-openclaw-agent-ran-amok-on-her-inbox/">confirm before taking any action</a> on her email. It worked, until the session ran long enough to trigger context compaction, a mechanism where the agent compresses its memory to keep running. The safety instructions were among the first things dropped. The agent deleted 847 emails. She had to physically run to her machine to stop it.Your agent is a new delivery channel for malware. Attackers are already using multiple paths to get malicious code running through agents. Weaponized skills on public marketplaces have delivered the&nbsp;<a href="https://x.com/GenThreatLabs/status/2018323584834318419">AMOS infostealer to macOS users</a>. Malicious project configurations have achieved&nbsp;<a href="https://research.checkpoint.com/2026/rce-and-api-token-exfiltration-through-claude-code-project-files-cve-2025-59536/">remote code execution in Claude Code</a> the moment a developer opens a repository. Prompt injection through fetched content can turn a trusted agent into an attacker's proxy. The delivery mechanisms vary. The result is the same: your agent executes something it shouldn't, and it does so with your permissions.These are just three examples. The pattern is consistent: agents operate with real authority, and that authority is being exploited. Through supply chains, through the agent's own fragility, and through the same malware ecosystem that has targeted every other computing platform before them.<h2>Where ADR Must Operate</h2>To be effective, ADR must sit as close to the agent's execution loop as possible. Not as an external service watching from the outside. Inside, where the actions happen.Agent sessions contain source code, credentials, business logic, and personal data. ADR evaluates locally, on the device. No prompts, tool inputs, or code leave your machine for analysis.Agents are not robust systems. Their own internal mechanisms can silently drop safety instructions or get poisoned across sessions. A security layer that only sees inputs and outputs misses the moment things go wrong inside. ADR watches the execution loop itself: every action the agent attempts, in context, in sequence.Only a client-side layer sees the complete picture: the prompt history, what files were already modified, the sequence of prior actions. A remote service sees isolated events. ADR sees the session. That difference is what makes it possible to distinguish a legitimate file deletion from a compromised agent cleaning up its tracks.<h2>Sage: ADR in Practice</h2>In February, we&nbsp;<a href="https://www.gendigital.com/blog/news/company-news/sage-ai-agent-security">open-sourced Sage</a> as the first implementation of ADR. It runs client-side, directly inside the agent's execution loop, across Claude Code, Cursor, and OpenClaw. Over 200 detection rules cover supply chain attacks, credential exposure, dangerous commands, persistence mechanisms, and more, backed by Gen's threat intelligence.Since launch,&nbsp;<a href="https://github.com/avast/sage/">Sage</a> has quickly crossed 1,000 installs and is available across the major agentic tool marketplaces. The open-source community is already contributing, porting Sage to new platforms like OpenCode and hardening detection rules. As one independent security researcher concluded after&nbsp;<a href="https://www.linkedin.com/pulse/paranoid-sage-testing-security-tool-agents-radek-hendrych-capff/">testing Sage</a>: "Real-time monitoring and management of AI agents based on deterministic measures is, in my view, the right way to go."All these responses confirm what we suspected: people want this layer.<h2>What's Next</h2>ADR is the runtime layer. But runtime protection alone isn't enough.&nbsp;The Agent Trust Hub is built on the idea that trust in the agentic era must be layered: verification before installation, ecosystem trust signals across marketplaces, and enforcement at execution. ADR closes the gap at the moment risk becomes real.&nbsp;The industry needs a common interface for how agents expose their actions to security tools. A shared standard, so that protection works consistently regardless of which agent you use or which platform you're on. We've been working on that too. More soon.

Inside the Gen Agent Trust Hub, ADR turns trust signals into real-world protection.

Gen Agent Trust Hub Introduces Agent Detection and Response: The Missing Security Layer for Agentic AI 

AI agents are starting to do the parts of computing that used to be “hands on keyboard”. They run terminal commands, edit files, install packages, and fetch things from the web, because that’s what it takes to get real work done. Tools like Claude Code, Cursor, and OpenClaw give these agents direct access to your machine and your sensitive information. That's what makes them useful. It's also what makes them risky. These agents make mistakes. They install packages that don't exist because they hallucinated the name. They pick up credentials and pass them where they shouldn't. They'll download and run programs from the internet without a second thought if the task seems to call for it. All of this happens fast, with confidence, and without asking. If you’ve watched an agent confidently suggest curl … | bash or “just add your API key here” into a file it shouldn’t touch, you’ve seen the problem.&nbsp;Agents are also targets. Malicious URLs, poisoned dependencies, and compromised plugins can turn an agent into an attack vector, one that already has access to your files, your terminal, and your network. This is already happening. <a href="https://x.com/GenThreatLabs/status/2018323584834318419" target="_blank" rel="noreferrer noopener">We recently identified</a> around 400 malicious skills on ClawHub, a public registry for agent extensions. At the time of discovery, they accounted for 12% of all available skills. These weren’t obviously shady throwaways; many were weaponized versions of legitimate skills, designed to trick agents into downloading malware.&nbsp;&nbsp;These are the same agents we're starting to trust with payments, infrastructure, and sensitive workflows. The gap between what agents can do and what keeps them in check is real, and it's growing.&nbsp;Today, Gen, the company behind Norton, Avast, LifeLock, and AVG, is releasing Sage to close that gap. With Sage, the Agent Trust Hub now evaluates and governs agent behavior during execution. Not just before installation. Not just at verification. But in the exact moment an action is about to happen.&nbsp;

<h2>What Sage does</h2>Think of Sage as a lightweight safety shield for AI agents. It sits inside the agent's workflow and checks every action before it happens: shell commands, URL fetches, file writes, package installs. For each action, Sage returns a verdict:&nbsp;<ul type="disc"><li data-list-item-id="e2aee30f293d4edf72390d310cdfa3f0e">Allow — no threat detected, the action proceeds&nbsp;</li><li data-list-item-id="e89d49ae9fae752dee97f089112c466b8">Ask — something looks suspicious, the user decides&nbsp;</li><li data-list-item-id="e44ca3f76876a9e666f2544e1233366ba">Deny — confirmed threat, the action is blocked&nbsp;</li></ul>The agent never notices unless something is wrong.&nbsp;Sage catches both sides of the risk. It protects agents from external threats like malicious URLs, compromised packages, and dangerous downloads. And it protects users from agents themselves, when they try to run destructive commands, expose credentials, or write to sensitive system files.&nbsp;Here's a concrete example. An agent automating a setup task decides to download and execute a script from the internet. No review, no confirmation. Sage recognizes the pattern and blocks it before anything runs.&nbsp;<h2 class="text-align-justify">Part of a larger commitment</h2>Sage is not a standalone effort. It’s part of a broader strategy to embed continuous trust into the AI skills ecosystem through the Gen Agent Trust Hub.&nbsp;&nbsp;The Skill Scanner of the <a href="https://ai.gendigital.com/agent-trust-hub" target="_blank" rel="noreferrer noopener">Gen Agent Trust Hub</a> helps determine whether an agent extension is trustworthy before it gets installed, using cloud-based verification and classification. Sage picks up where that leaves off. Once the agent is running, it enforces safety locally, on your machine, checking every action at the moment it's about to execute.&nbsp;&nbsp;Together, they cover the full lifecycle: from the decision to install something to the moment it actually runs. More capabilities are on the way as the agentic ecosystem grows.&nbsp;<h2 class="text-align-justify">Open source, on purpose</h2>The agentic platforms themselves are designed for open integration: open-source tools, community plugins, public hook APIs. A closed security layer would be harder to adopt in a space that moves fast and depends on community integrations.&nbsp;At the same time, there is no established playbook for how to secure AI agents. By open-sourcing Sage, we're proposing one and inviting the community to help define what safe agents should look like in practice.&nbsp;This first version is lightweight. It's not a full antivirus product as we know it, but it addresses the problems where they are. It covers the high-risk paths we think matter most: commands, file changes, URL fetches, and package installs, including catching hallucinated or malicious dependencies before they land on your machine. Sage ships with over 200 detection rules covering command injection, persistence, credential exposure, obfuscation, and supply-chain attacks.&nbsp;Sage currently supports Claude Code, Cursor, and OpenClaw, with more platforms on the way. If you find a detection that's too noisy or not strict enough, open an issue. If you want to add a rule, improve a check, or add support for another agent platform, send a pull request.&nbsp;We built Sage to be extended.&nbsp;&nbsp;<h2>Try it</h2>Learn more about Sage at <a href="https://ai.gendigital.com/sage" target="_blank" rel="noreferrer noopener">https://ai.gendigital.com/sage</a>. For technical details, documentation, and source code, visit the project on <a href="https://github.com/avast/sage" target="_blank" rel="noreferrer noopener">https://github.com/avast/sage.</a>&nbsp;If you use Claude Code, Cursor, or OpenClaw, install Sage and put it in the path of real work. The fastest way to improve a safety layer is to expose it to real commands, real repos, and real mistakes. That's what we're doing here — shipping early, in the open.&nbsp;

Closing the growing security gap between what Al agents can do and what keeps them in check

Introducing Sage: Safety for Agents 

AI agents aren’t just helpful in chats anymore. They’re actually taking action on your behalf. Autonomous AI can access data. They move from suggestion to execution without hesitation.&nbsp;This is what we call “mindless artificial intelligence.”&nbsp;When technology starts taking action on our behalf, trust has to be built into the system itself.&nbsp;That’s why Gen is partnering with Vercel to bring independent safety verification to the AI skills ecosystem at <a href="https://skills.sh/" target="_blank">skills.sh</a>, embedding trust directly into the skills layer.&nbsp;

<h2>Independent verification. Built in.&nbsp;</h2>Through this partnership, the Gen <a href="https://ai.gendigital.com/agent-trust-hub" target="_blank">Agent Trust Hub</a> will evaluate skills on skills.sh and assign clear, transparent classifications:&nbsp;🟢 Safe&nbsp; 🟡 Low Risk&nbsp; 🟠 High Risk&nbsp; 🔴 Critical Risk&nbsp;Behind those ratings is advanced risk modeling and threat intelligence from Gen Threat Labs, analyzing permissions, behaviors, vulnerabilities and potential malicious intent.&nbsp;The goal isn’t to slow innovation. It’s to help it scale responsibly. Developers should be able to build boldly. Users should be able to install confidently. And both deserve clarity.&nbsp;<h2>Security as infrastructure, not an afterthought&nbsp;</h2>By integrating verification directly into skills.sh, Gen and Vercel are making security part of the AI development lifecycle, not something bolted on after the fact.&nbsp;In the agentic world, skills are how things get done. Until now, developers and users had limited visibility into whether a skill was truly safe. Reputation and documentation can’t tell the whole story. As agents gain autonomy, guesswork isn’t good enough.&nbsp;The trust has to be visible.

<h2>Powering Digital Freedom in what comes next&nbsp;</h2>At Gen, we believe Digital Freedom means being able to embrace new technology without sacrificing safety or confidence. As AI evolves, our responsibility evolves with it.&nbsp;The future of AI will be powerful. And expansive. But, it must also be secure by design.&nbsp;This partnership is one more step toward building the trust foundation the agentic era demands, so innovation and confidence can grow together.&nbsp;

Bringing transparent risk ratings to the AI skills ecosystem to help developers build boldly and users move confidently

The trust layer for the agentic era 

Celebrating the 10th anniversary of the mDevCamp conference, Gen proudly stepped up as the master sponsor, marking our first in-person event in five years. Held in Prague, mDevCamp is Central Europe's premier conference for mobile developers, drawing enthusiasts from across the continent for a day of insightful presentations from companies like Spotify, SoundCloud, Microsoft, and more. This year’s agenda covered essential topics in mobile development, including:<ul><li>Cross-platform development</li><li>Android and Kotlin</li><li>iOS and Swift</li><li>Security measures</li><li>Media technologies</li><li>And much more</li></ul>

Gen led a standout workshop titled "Kotlin Multiplatform - Unleash the Power of Shared Code," which focused on the strategic reuse of business logic from Android apps on iOS platforms. Our session provided a detailed roadmap on achieving efficient code sharing, offering attendees practical tips to enhance their development process. Participants gained insights into common challenges, the evolution of the technology, and the best practices to embark on multiplatform projects. The four-hour workshop was a success, and we extend our heartfelt thanks to all who participated.&nbsp;We're thrilled and excited to more and more people with so much interest in our industry and company, and we welcome talented developers from everywhere. Join us as we continue to explore the frontiers of cybersecurity and mobile development.

Alisha Robinson

Posts by Alisha Robinson