SR. MALWARE ANALYST

Mgr, Threat Analysis Engineering

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Threat Researcher at Gen

Research

<h2>Introduction&nbsp;</h2>Infostealers are constantly evolving, and so are the techniques they use to bypass <a href="https://security.googleblog.com/2024/07/improving-security-of-chrome-cookies-on.html" target="_blank" rel="noreferrer noopener">Application-Bound Encryption (ABE)</a>. In recent weeks, Vidar has been among the most actively developed stealers and, apart from <a href="https://x.com/GenThreatLabs/status/2064836539952361976" target="_blank" rel="noreferrer noopener">multiple updates to its string obfuscation</a> and <a href="https://x.com/GenThreatLabs/status/2061462524604649840" target="_blank" rel="noreferrer noopener">a reworked approach to protecting its configuration</a>, it has also introduced a novel technique for bypassing ABE. And while there have been many other changes in Vidar lately, with new versions dropping every week, in this blog post we focus solely on the ABE bypass and its technical aspects.&nbsp; &nbsp;<h2>The ABE Bypass&nbsp;</h2>From a high-level perspective, Vidar’s approach to bypassing ABE is somewhat similar to that of Remus/Lumma, in that both try to extract the <code>v20_master_key</code> directly from the browser’s memory. What sets them apart, however, is how they achieve it. Since we’ve already covered the inner workings of ABE in earlier posts (<a href="https://www.gendigital.com/blog/insights/research/remus-64bit-variant-of-lumma-stealer" target="_blank" rel="noreferrer noopener">Remus</a>, <a href="https://www.gendigital.com/blog/insights/research/voidstealer-abe-bypass" target="_blank" rel="noreferrer noopener">VoidStealer</a>), we don’t revisit them here and focus instead on the bypass itself. For readers interested in the broader background, we encourage checking those earlier posts.&nbsp;&nbsp;In short, the end goal of infostealers is to obtain the <code>v20_master_key</code>, as it alone is sufficient for decrypting any ABE-protected data tied to a specific application. This key can be obtained in one of two ways: either from disk, where it is protected by two layers of <code>CryptProtectData</code> (one applied in the context of the logged-in user and one as <code>NT AUTHORITY\SYSTEM</code>), or from the browser’s memory – the direction Vidar took.&nbsp;In memory, however, the key is kept protected with <code>CryptProtectMemory</code> using the <code>CRYPTPROTECTMEMORY_SAME_PROCESS</code> flag. This ensures that even if an attacker manages to read the encrypted form of the key from memory, they cannot simply decrypt it (they would need to invoke the complementary <code>CryptUnprotectMemory</code> function directly from within the browser process). That leaves attackers with two problems to solve: how to find the encrypted key in memory, and how to get it decrypted.&nbsp;<h2>Locating the v20_master_key in Browser’s Memory&nbsp;</h2>Vidar’s approach to locating the encrypted <code>v20_master_key</code> in memory involves scanning the entire memory of a running browser for a specific pattern. But that first requires a browser process to scan.&nbsp;When the target browser is already running, Vidar reuses the existing process. However, it doesn’t read its memory directly. Instead, it creates a fork of it using <code>NtCreateProcessEx</code> with <code>SectionHandle</code> set to <code>NULL</code>, instructing the kernel to clone the address space of the process specified by the <code>ParentProcess</code> as a copy-on-write mapping. The resulting process has no threads and is never resumed – it exists purely as a static memory snapshot.&nbsp;&nbsp;To perform the fork, Vidar first opens a handle to the target browser via <code>OpenProcess</code> with <code>PROCESS_QUERY_INFORMATION | PROCESS_CREATE_PROCESS | PROCESS_VM_READ</code>, falling back to <code>PROCESS_CREATE_PROCESS</code> if the initial call fails. It then passes this handle as the <code>ParentProcess</code> argument to <code>NtCreateProcessEx</code>, requesting <code>PROCESS_ALL_ACCESS</code> on the resulting child. If the fork fails, Vidar falls back to scanning the live browser process directly.&nbsp;

If no target browser process is found running, Vidar creates a new isolated desktop via <code>CreateDesktopA</code>, assigns it a name of the form <code>v20_%d</code> (where <code>%d</code> is replaced with the browser’s index in Vidar’s configuration), and launches the target browser in there. In the case of Vidar version 2.1, the browser is launched with the command line <code>--no-first-run --disable-gpu about:blank</code>, although the exact command line varies across versions. Since modern browsers use a multi-process architecture, there are typically multiple processes matching a given browser. Vidar enumerates all of them (up to 64) and applies the fork-and-scan procedure to each one independently.&nbsp;

With a handle to the forked process in hand, Vidar proceeds to enumerate its virtual memory regions using <code>NtQueryVirtualMemory</code>. It iterates through the entire browser’s address space, collecting regions that match a specific profile: committed (<code>MEM_COMMIT</code>), private (<code>MEM_PRIVATE</code>), and either readable (<code>PAGE_READONLY</code>) or read-write (<code>PAGE_READWRITE</code>). Vidar records up to 4096 memory regions matching these requirements, along with their base addresses and sizes. The actual pattern scanning is then performed in parallel with up to 64 worker threads, depending on the system.&nbsp;&nbsp;

Each thread scans its assigned regions using <code>NtReadVirtualMemory</code>, searching for a 32-byte pattern. When a match is found, the thread reads 8 bytes at offset +32 from the match, interprets them as a pointer, and validates that it points to committed, private, and readable memory (the expected memory state for the encrypted <code>v20_master_key</code>).&nbsp;&nbsp;

This naturally raises the question of what pattern Vidar searches for. The answer is a 32-byte signature: <code>76 32 30 00 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? 03 00 00 00 00 01 ?? ?? ??</code>, where <code>??</code> denotes a wildcard (Vidar uses the byte <code>AA</code> as the wildcard marker in its internal representation). Notably, the first four bytes correspond to the ASCII <code>v20\x00</code>, which is a common prefix used for ABE-related data.&nbsp;&nbsp;

We assess with high confidence that this pattern is intended to match a node structure within Chromium’s <code>Encryptor::KeyRing</code>, the structure that holds the keys used by the <code>Encryptor</code> class. Now, this part is admittedly a bit tricky as it relies on several assumptions we made about the internal memory layout, as well as on implementation-specific representations of various C++ standard library types <code>(std::map</code>, <code>std::string</code>, <code>std::vector</code>, <code>std::optional</code>), which is why we frame this as a high-confidence rather than a certainty.&nbsp;&nbsp;Our reasoning is as follows: <code>KeyRing</code> is <a href="https://github.com/chromium/chromium/blob/cf5d502bcaedb22927e3829a18be1ff6d8b6f598/components/os_crypt/async/common/encryptor.h#L132" target="_blank" rel="noreferrer noopener">defined</a> as <code>std::map&lt;/*tag=*/std::string, std::optional&lt;Key&gt;&gt;</code>, and <code>std::map</code> is typically implemented as a red-black tree, where each tree node stores three pointers (left child, parent, and right child) followed by the key-value pair. In this case, the pair is in the form of <code>std::pair&lt;/*tag=*/std::string, std::optional&lt;Key&gt;&gt;</code>, meaning that the <code>std::string</code> and the <code>Key</code> object might be laid out consecutively in memory within each node. For a <code>v20_master_key</code> entry, the tag corresponds to the string <code>v20</code>, which at 4 bytes including the null terminator fits within the small string optimization (SSO) buffer and is therefore stored inline rather than heap-allocated. The <code>Key</code> class that immediately follows <a href="https://github.com/chromium/chromium/blob/cf5d502bcaedb22927e3829a18be1ff6d8b6f598/components/os_crypt/async/common/encryptor.h#L93-L98" target="_blank" rel="noreferrer noopener">contains three member variables</a>: <code>std::optional&lt;mojom::Algorithm&gt; algorithm_</code>, <code>std::vector&lt;uint8_t&gt; key_</code>, and <code>bool encrypted_ = false</code>.&nbsp;Putting it all together and applying it to a concrete example from a browser memory dump, we get the following layout:&nbsp;

Please note that while the exact layout may contain minor inaccuracies on our part, the overall structure should be roughly correct. For easier orientation, the following diagram illustrates how the individual structures are nested within each other:&nbsp;

At this point, it should be clear how the scan pattern maps onto the searched structure. The 32-byte match covers the <code>std::string</code> tag <code>v20\x00</code> and the beginning of the <code>Key</code> object up to and including <code>algorithm_</code>. The pointer at offset +32 from the start of the match then lands precisely on <code>key_.begin</code>, the first member of <code>std::vector&lt;uint8_t&gt;</code> that holds the encrypted <code>v20_master_key</code>.&nbsp;Matches from all threads are collected into a shared buffer and subjected to a majority-voting scheme: candidates consisting mostly of zeroes are discarded, the rest are pairwise-compared, and each is scored by the number of identical copies found. This voting is performed independently for each browser process, producing one best candidate per process.&nbsp;<h2>Decrypting the v20_master_key via APC Injections&nbsp;</h2>For each browser process that produced a match, Vidar now holds a candidate address pointing to a potential encrypted <code>v20_master_key</code>. However, as already noted, since the key is protected with <code>CryptProtectMemory</code> using <code>CRYPTPROTECTMEMORY_SAME_PROCESS</code>, it can only be decrypted by invoking <code>CryptUnprotectMemory</code> from within the browser process itself. Vidar solves this by injecting an Asynchronous Procedure Call (APC) into the live browser process.&nbsp;Vidar supports two APC injection methods, selected based on whether ESET or Bitdefender is detected on the system. If either of these products is present, Vidar uses the first method, which it refers to as “classic” (based on decrypted strings) – it creates a suspended thread in the target browser via <code>CreateRemoteThread</code> (with <code>NtTestAlert</code> as the thread start address and the <code>CREATE_SUSPENDED</code> flag), queues an APC using <code>NtQueueApcThread</code>, resumes the thread, and then waits for it to complete via <code>WaitForSingleObject</code> with a randomized timeout. When the thread resumes, it calls <code>NtTestAlert</code>, which flushes the APC queue and executes the injected routine.&nbsp;

If neither product is detected, it uses the “special” method – it finds an existing thread in the target browser using a combination of <code>CreateToolhelp32Snapshot</code> and <code>Thread32First/Thread32Next</code>, opens it via <code>NtOpenThread</code>, and calls <code>NtQueueApcThreadEx2</code> with the <code>QUEUE_USER_APC_FLAGS_SPECIAL_USER_APC</code> flag. Unlike regular APCs, special user APCs execute immediately. Therefore, the target thread does not need to be in an alertable wait state.&nbsp;

In both cases, the APC routine to be executed is <code>CryptUnprotectMemory</code>, called with the following three arguments: the address of the encrypted <code>v20_master_key</code> candidate, its size (32 bytes), and <code>CRYPTPROTECTMEMORY_SAME_PROCESS</code> for the <code>flags</code> parameter.&nbsp;&nbsp;Since <code>CryptUnprotectMemory</code> decrypts the data in place, once the APC executes, the key is decrypted directly in the live browser's memory. To read it, Vidar creates a second fork of the browser process and reads the key from the same address using <code>NtReadVirtualMemory</code>. It then compares the result against a snapshot of the encrypted key taken from the first fork before the APC call – if the data is unchanged, decryption failed, and Vidar moves on to the next browser process’s candidate.&nbsp;If the data differs, Vidar uses the same second fork to perform an additional verification: it scans the forked process’s memory for the byte sequence <code>v20</code> and, treating each match as a potentially ABE-encrypted entry (cookie, password), attempts AES-256-GCM decryption with the key candidate using <code>BCryptDecrypt</code> (successful GCM tag verification confirms that the key is correct).&nbsp;Whenever the data has changed (indicating that decryption of the key took place), Vidar re-encrypts the key in the browser’s memory by injecting another APC with <code>CryptProtectMemory</code> as the routine, preserving the browser’s state. If no candidate produced a successful decryption (i.e., the data remained unchanged after every APC call), Vidar takes a more aggressive approach: it terminates all browser instances, restarts the browser, and repeats the entire scan-decrypt-verify cycle.&nbsp;By abusing APC injections instead of the more traditional approach of injecting code via <code>NtWriteVirtualMemory</code> that performs the decryption, Vidar trades one suspicious behavior for another. APC injections are arguably less common and may thus slip past some detection systems, though they are still well-known techniques monitored by AV and EDR solutions. But that is the nature of ABE bypasses – each has its own advantages and disadvantages, and that’s exactly why infostealers keep inventing new ones. &nbsp;<h2>Indicator of Compromise (IoC)&nbsp;</h2>459daa809751e73f60fbbe4384a7d1653c36bb06945e4eb3635270924241100a (Vidar 2.1)&nbsp;

A Technical Walkthrough of How Vidar Defeats Application-Bound Encryption

Inside Vidar’s ABE Bypass: From Memory Scanning to APC Injections

Security Evangelist at Gen

You have a summer flight booked. You have seen the headlines about jet fuel shortages, cancelled routes and higher travel costs. Then a text message arrives: your flight has been affected, and you need to click a link to confirm your refund or rebook before the offer expires.&nbsp;That message feels plausible because the situation around it is real. In April, <a href="https://www.iata.org/en/pressroom/2026-releases/2026-04-17-01/" target="_blank" rel="noreferrer noopener">IATA warned</a> that Europe could start seeing flight cancellations by the end of May if jet fuel shortages materialized. Airlines and governments have since been trying to reassure travelers, while also publishing guidance on passenger rights, refunds and re-routing.&nbsp;&nbsp;That is exactly the kind of confusion scammers like. They do not need to cause the disruption, they only need to arrive at the right moment, with the right brand logo and the right sense of urgency.&nbsp;<h4>Why travel disruption works so well as a scam hook&nbsp;</h4>Most scam messages fail because they arrive out of context. A random refund email from an airline you never use is easy to ignore. A refund message from the airline you are flying with next week, during a period of public travel disruption, is different.&nbsp;The scammer’s job is to make you think: “This could be real.”&nbsp;That can happen in several ways. You might receive a fake cancellation email that links to a refund form. You might search for an airline’s customer service number and land on a fake support page. You might post publicly on social media asking an airline for help, then receive a reply from an account that looks official. Or you might get a WhatsApp message that knows your hotel name, travel dates or booking reference.&nbsp;Each version uses the same pressure point: you are worried about losing money, losing your flight or being stranded.&nbsp;Criminals are already using disruption this way. In March, <a href="https://www.theguardian.com/money/2026/mar/15/travel-scam-airline-accounts-fake-refunds-iran-war-flight-disruption" target="_blank" rel="noreferrer noopener">The Guardian reported</a> that fake airline support accounts were targeting travelers affected by Middle East flight disruption. Some victims were looking for help or refunds, then received replies from accounts pretending to represent airlines. The scam moved into direct messages, where victims were asked for details and sent links that led to money being taken instead of refunded.&nbsp;&nbsp;The jet fuel story gives scammers another pretext. A message can claim that your flight was cancelled, your fare changed, your fuel surcharge failed, or your refund must be claimed within a few hours. None of those claims needs to be true to be effective.&nbsp;<h4>The scam may start before the message arrives&nbsp;</h4>A fake travel message becomes much harder to spot when it includes real information.&nbsp;This is the idea behind <a href="https://www.gendigital.com/blog/insights/research/reservation-hijack-scam" target="_blank" rel="noreferrer noopener">Reservation Hijack scams</a>, a form of targeted phishing where cybercriminals use real booking details to make a message look legitimate. In some cases, attackers may use information exposed through breaches or compromised travel systems. In other cases, they collect details from emails, social media posts, previous leaks or fake customer service interactions. Gen has previously described Reservation Hijack scams as scams that use real hotel booking details to make fraudulent payment or verification requests feel authentic.&nbsp;&nbsp;That distinction matters. A scam message does not need your credit card number to fool you. Knowing your name, your destination, your travel dates and your booking platform may be enough.&nbsp;<h4>What these scams can look like&nbsp;</h4>A travel disruption scam usually does not begin with a strange offer. It begins with something boring and administrative. That is part of the trick.&nbsp;You may see messages like:&nbsp;“Your flight has been cancelled due to fuel supply issues. Claim your refund here.”&nbsp;“Your rebooking is pending. Confirm passenger details within two hours.”&nbsp;“Additional airport tax or fuel surcharge is required to keep your booking.”&nbsp;“Your hotel reservation will be cancelled unless payment is verified.”&nbsp;“Due to high call volume, please continue this request on WhatsApp.”&nbsp;The exact wording changes, but the structure is familiar. There is a problem, a deadline and a link. The message may also move you away from the official channel into SMS, WhatsApp, Telegram, direct messages or a phone call. Once that happens, the scammer controls the conversation.&nbsp;There is another detail worth watching: some messages do not ask for money immediately. They start by asking for your booking reference, last name, phone number or one-time password. That can still be dangerous. Those details can help scammers access a booking, impersonate you to customer support, reset an account or make the next message more convincing.&nbsp;<h4>Your real passenger rights can be used against you&nbsp;</h4>If an airline cancels your flight, you may have rights to a refund, re-routing or assistance, depending on where you are travelling and the rules that apply. The European Commission said passengers affected by cancellations continue to have rights to reimbursement, re-routing or return, airport assistance and compensation for last-minute cancellations in relevant circumstances. It also said airlines cannot retroactively add fuel surcharges to already published final ticket prices.&nbsp;&nbsp;That information is useful for travelers, but it is also useful for scammers.&nbsp;A fake refund message becomes more believable when people already know refunds are possible. A fake rebooking link becomes more believable when airlines are genuinely changing schedules. A fake “fuel surcharge” demand becomes more dangerous when fuel prices are in the news, even though legitimate retroactive charges may not be allowed in many cases.&nbsp;This is why the safest route is not to judge the message only by whether the story sounds plausible. During travel disruption, many scam stories will sound plausible. Judge the channel instead.&nbsp;<h4>How to check before you click&nbsp;</h4>Start from the airline or booking platform’s official app or website. Do not use the link in the message. Open the app yourself, type the website address yourself, or use the contact details listed on your original booking confirmation.&nbsp;Check whether the same cancellation, refund or payment request appears inside your account. If the message is real, the airline or hotel should be able to confirm it through an official channel.&nbsp;Be careful with social media support. If an account replies to your public complaint, check whether it is linked from the airline’s official website. Look at the handle carefully. Fake support accounts often copy logos and names, but have small differences in spelling, low follower counts or recently created profiles. The Guardian reported that fraudsters often scan social media for passengers trying to contact legitimate airline accounts and reply to them first, which makes the scam harder to spot.&nbsp;&nbsp;Do not share one-time passwords, payment card details or login credentials with anyone who contacts you unexpectedly. A real airline or hotel does not need your banking OTP to send you a refund.&nbsp;Be especially suspicious of pressure. “You have 30 minutes to claim your refund” is not customer service. It is a tactic.&nbsp;<h4>What to do if you already clicked&nbsp;</h4>If you entered payment details, contact your bank or card provider immediately. Ask whether the transaction can be blocked or reversed, and request a new card if needed.&nbsp;If you shared login details for an airline, hotel or booking platform, change the password from the official site or app. Use a unique password, not one you use elsewhere. Turn on two-factor authentication where it is available.&nbsp;If you shared a one-time password or authentication code, assume the account may have been accessed. Check your booking details, payment methods and recent account activity.&nbsp;Report the scam to the airline, hotel or booking platform being impersonated. If the scam came through social media, report the account as impersonation. If money was taken, report it to the relevant fraud authority in your country.&nbsp;<h4>The real lesson for summer travel&nbsp;</h4>Travel disruption changes how people behave. They search for support numbers in a hurry. They post publicly because customer service queues are long. They accept strange instructions because they are worried about losing a flight or refund.&nbsp;Scammers build around that behavior.&nbsp;The safest assumption is simple: if a message about your flight, hotel or refund creates urgency and gives you a link, pause. Go back to the official source. A real cancellation will still be there when you check through the airline or booking platform. A scammer needs you to act before you do.&nbsp;

Travel disruption gives scammers the one thing they need most: a believable reason to rush you

Your flight was cancelled. Is the refund message real? 

A fake job interview page is easier to trust than a fake ticket shop.In samples reviewed by Gen threat researchers, scammers used branded hiring pages that looked like ordinary recruitment flows: a company logo, a recruiter profile, a 30-minute meeting, and a button to continue with Google or Facebook. There was no attachment to open and no software to install. The credential theft attempt sat inside a step many job applicants already expect: signing in to schedule a call.FIFA is an obvious lure ahead of the 2026 World Cup. On May 22, ESET reported fake FIFA-themed websites that copied ticket and merchandise flows to steal money and personal data. Our researchers later found another FIFA-themed domain, hxxps://fifa-mg[.]shop, which followed a naming pattern not mentioned in that report. Domains like these can be short-lived, often appearing for hours or days before they are blocked, abandoned, or taken down.Our researchers also found a different kind of FIFA lure: hxxps://fifahiring[.]com, a fake hiring page that encouraged users to schedule a call with an alleged FIFA recruiter. When a user tried to register with a personal Gmail address, the page asked for a business email instead. The page was steering victims toward credentials with more value: accounts that could provide access to a company environment.

<h4>The FIFA hiring page did not point to FIFA</h4>The fake page looked believable at first glance.

It used FIFA branding, presented a recruiter identity, and copied parts of the experience users see on legitimate recruiting and scheduling platforms. The recruiter’s name and photo appeared to match a real LinkedIn profile, which made the page feel more credible. For publication, we should keep that person’s identity redacted.The domain did not match FIFA’s real hiring infrastructure. FIFA’s legitimate careers pages are jobs.fifa.com and fifa.pinpointhq.com. The fake page used neither. It also loaded the favicon from the real FIFA recruitment site hosted at pinpointhq.com, giving the page a small visual cue borrowed from the legitimate service.Other signals pointed in the same direction. The domain and certificate were newly created, from May 23. The site used commodity hosting infrastructure, including Vercel and Render, rather than a corporate environment. Those signs are not enough on their own, but they fit the rest of the evidence.The login flow was the clearest part. Credentials were posted to:fifeq2026eqbackeq[.]onrender[.]com/api/loginThe Google sign-in prompt was not a real Google authentication window. It was rendered inside the page itself. The visible controls and links did nothing, and the page did not send the user into a genuine Google sign-in flow. According to the analysis, the only Google asset loaded was the favicon, apparently to make the fake window look familiar.A page can look like it opened a Google login without ever handing control to Google.Researchers also observed hxxps://careers-fifahiring[.]com, which later became inactive, and hxxps://fifajobs[.]com, which showed the same content and was indexed by Google.&nbsp;<h4>The recruitment lure went beyond FIFA</h4>The FIFA page was one version of a wider recruitment phishing pattern. The brand can change. The flow stays familiar: recruiter profile, meeting prompt, business email, fake sign-in.Researchers found similar fake hiring pages abusing other major brands, including Heineken, Hilton, Coca-Cola, Netflix, PepsiCo, Delta,&nbsp;and Spotify. In the Heineken case, the page appeared to use the identity of someone who, according to LinkedIn, really works as a recruiter at the company.

Additional hunting found two other recruitment-themed kits:hxxps://aquent-talent-jobs[.]com hxxps://hays-talent-opportunities[.]comThe Hays-themed version targeted Facebook credentials instead of Google credentials. Researchers assessed that the Aquent and Hays pages shared code, although they differed from the earlier FIFA examples.

This flexibility makes the campaign harder to frame as a single World Cup scam. FIFA provides the timely hook, but the mechanism is recruitment phishing. A scammer can swap the logo, recruiter name, and sign-in provider while keeping the same basic path to the victim’s credentials.<h4>Why fake recruitment pages work</h4>Recruitment gives attackers a clean excuse to ask for things that might otherwise raise suspicion.A candidate expects to click a scheduling link. They expect a recruiter profile. They expect a calendar prompt. In some cases, they may also expect to use a work email, especially for corporate, contractor, or partner-facing roles.The fake login window turns that ordinary flow into credential theft. Users are used to “Continue with Google” and “Continue with Facebook” buttons, but the button itself proves nothing. A genuine authentication flow should take the user to the provider’s domain and controls. A fake page can draw a convincing copy while keeping the victim inside the attacker-controlled site.The business email prompt is also a warning sign. A page that rejects a personal email and asks for a work address may be trying to raise the value of the stolen account.<h4>What job seekers should check before signing in</h4>Start from the company’s official careers page, not from a link in a message, ad, search result, or forwarded post. For FIFA, that means checking jobs.fifa.com or fifa.pinpointhq.com, not trusting a domain because it contains “fifa,” “career,” “hiring,” or “jobs.”Look closely at the domain before entering credentials. Scam pages often add plausible hiring words around a brand name: career, talent, jobs, hiring, portal, or connect. A polished page on a new domain is still a new domain.Treat the sign-in step as the real checkpoint. If a page offers Google or Facebook login, check whether the flow actually opens a real provider-controlled authentication page. A fake window embedded inside the original site, broken controls, inactive links, or a provider logo used only as decoration should stop the process.Use security software that includes web and phishing protection. These tools can block known malicious pages before they load, which is especially useful when scam domains look polished, appear in search results, or are shared through otherwise legitimate channels.<h4>What companies can do</h4>Recruitment pages are public by design, which makes them easy to copy.Security teams should monitor for newly registered domains combining their brand with hiring-related terms. They should also watch for pages that reuse favicons, recruiter photos, job descriptions, and calendar-booking language from legitimate hiring flows.HR and recruiting teams should make the official hiring path easy to verify. Candidates should not have to guess whether a domain is legitimate. A clear careers page, consistent recruiter communication, and warnings about unofficial scheduling links can remove some of the ambiguity attackers rely on.<h4>Conclusion</h4>The fake FIFA hiring page worked because it did not feel unusual. It looked like a normal step in a hiring process.Attackers do not need to invent new behavior when they can copy one people already trust. A branded scheduling page, a recruiter profile, and a familiar sign-in button can be enough to move a victim from interest to credential entry.The decision point is the login. Before entering a work email or password, check the domain, check where the authentication flow really goes, and verify the role through the company’s official careers site. Security software with web and phishing protection adds another layer, especially against short-lived scam pages that may only stay online for hours or days.IOCs:hxxps://fifahiring[.]com hxxps://careers-fifahiring[.]com hxxps://fifajobs[.]com hxxps://fifa-mg[.]shop hxxps://heineken-careers[.]com hxxps://aquent-talent-jobs[.]com hxxps://hays-talent-opportunities[.]com fifeq2026eqbackeq[.]onrender[.]com/api/login

Scammers are copying recruitment and calendar-booking flows to make fake Google and Facebook sign-ins look routine. 

Fake hiring pages abuse FIFA and other major brands to steal work credentials 

Threat Research Team Lead

Since January 2026, we have detected 7.4 million malicious incidents tied to residential proxy traffic, affecting 572,000 users in our telemetry. In this model, the household whose connection is being used is often not the buyer of the traffic. It is the exit point.That is the uncomfortable part of residential proxy networks. A home router, laptop, phone, smart TV, or other always-on device can quietly become part of a commercial network that routes strangers’ internet traffic through ordinary consumer IP addresses. To the websites on the other end, that traffic looks as if it came from a real household, because it did.The technology itself is not inherently malicious. Businesses use residential proxies for market research, ad verification, brand protection, security testing, and services that need to see the web as a real user would. But the same feature that makes these networks useful also makes them attractive for abuse: traffic from a household IP address is hard to block without risking false positives against legitimate users.The problem for consumers is simpler. Many people do not realize their device or connection can be enrolled into this kind of network at all. Some users opt in through pay-for-bandwidth apps. Others are enrolled through “free” VPNs, bundled software development kits inside unrelated apps, or unwanted software that installs proxy components without meaningful consent.Here is how the market works, who pays for it, and what it can cost the household that unknowingly hosts it.<h2>What is a residential proxy</h2>A residential proxy is an internet connection – typically a home laptop, phone, smart TV, or router – that has been enrolled into a third-party network and is being used to route someone else's traffic. To the rest of the internet, that traffic looks like it is coming from an ordinary household IP address assigned by a consumer ISP, not from a datacenter or&nbsp;VPN provider.Residential proxy networks are sold commercially. The model is two-sided: some people pay to tunnel their traffic out through someone else's connection (much like a VPN, except the exit point is a real home), while others get paid to provide those exit endpoints. Some are structured as paid peer-to-peer networks, where any participant's connection can serve as an exit for any other.Buyers pay for access to pools of millions of these "real user" IPs because they are extremely difficult to block, or rather, blocking them carries real consequences: the traffic originates from genuine households, so any block risks shutting out a legitimate user – effectively a false positive.Common buyers include market research firms, price-comparison services, brand-protection vendors, security teams testing their own defenses, and consumer privacy tools. At the abusive end of the market, the same infrastructure used by operators running automated purchasing bots, scrapping campaigns, ad fraud, credential stuffing, phishing and other attacks where hiding the true origin of the traffic matters. Demand is also being shaped by large-scale web scraping, including&nbsp;<a href="https://blog.includesecurity.com/2026/06/the-smart-tv-in-your-livingroom-is-a-node-in-the-aiscraping-economy/">scraping for AI training data</a>. As anti-bot systems become better at identifying traffic from datacenters and known VPN providers, residential IP addresses become more attractive because they look like ordinary users. That does not make every residential proxy user malicious. It does mean consumers may be carrying traffic for businesses and actors they have never heard of.<h2>How consumer devices end up in these networks</h2>There are several ways a home device can become part of a residential proxy network. Some are explicit. Others are easy to miss.The most obvious route is the pay-for-bandwidth model. Apps such as Honeygain, IPRoyal Pawns, and PacketStream pay users to share their internet connection. The user opts in, but they may not understand who will buy access to that connection later, what kind of traffic will pass through it, or what happens if that traffic triggers abuse reports.A second route is the “free” VPN or proxy app. Instead of charging a subscription, the app monetizes the user’s connection by reselling bandwidth. In practice, the user may think they are getting a privacy tool while also becoming infrastructure for other people’s traffic.A third route is through bundled software development kits, or SDKs. A developer adds a third-party component to an otherwise unrelated app, such as a flashlight, wallpaper, game, or utility. The developer gets paid when the app is active. The user sees the host app, not the proxy network behind it.The fourth route is malware or potentially unwanted software. In those cases, the proxy component is installed silently or deceptively, with no meaningful user consent at all.<h2>Why the "consent" is rarely meaningful</h2>Even when consent technically exists, it is often not the kind of consent most people would recognize.The disclosure may be buried in a long terms-of-service document. The wording may be vague, with phrases such as “share your unused bandwidth,” “support the app,” or “join our network.” These phrases do not clearly tell the user that arbitrary third parties may route traffic through the user’s home IP address.There is also the question of downstream use. Users are rarely told who buys access to the network or what those buyers do with it. Their connection could be used for benign market research. It could also be used for scraping, automated account creation, ad fraud, credential stuffing, or phishing infrastructure.The problem gets worse when the proxy component sits inside another app. The host app’s privacy policy may mention a partner SDK by brand name, but that still does not make it clear to a normal user that their device can become a proxy endpoint.Uninstalling the visible app may not always resolve the issue cleanly either, especially when proxy components are installed separately or bundled with other unwanted software.<h2>The risks consumers actually carry</h2>When a household device becomes a residential proxy node, the user takes on risks created by someone else’s traffic.The first risk is attribution. Abusive, fraudulent, or illegal activity routed through the device appears to come from the household’s IP address. ISP abuse notices, platform bans, fraud checks, and law enforcement requests point first to the subscriber, not to the person who bought the proxy traffic.The second risk is performance. Continuous third-party traffic can consume bandwidth, slow the home network, hit data caps, or violate ISP terms of service. The user may only notice that the internet feels slower, a bill is higher, or a service suddenly blocks access.The third risk is security exposure. Proxy clients run persistent background processes and maintain network connections so they can relay traffic. That adds always-on networking software to the device, often with limited visibility for the user.The fourth risk is reputation. A home IP address used for abuse can end up on blocklists used by banks, retailers, streaming services, and online platforms. The result can be account friction, failed logins, extra verification steps, or outright blocking from services the household actually uses.There is also a family angle. A child or another household member may install a free game, VPN, or utility without understanding the consequences. The legal, financial, and reputational risk still lands on the account holder.<h2>What our telemetry shows</h2>Since the beginning of 2026, we have detected 7.4 million malicious traffic incidents associated with residential proxy traffic, affecting 572,000 unique users in our user base.&nbsp; This is not a measurement of the entire residential proxy market. It reflects what we observe in our telemetry: malicious traffic, detections, and related activity affecting protected users. The real footprint is far larger, because residential proxy networks span major consumer platforms and many devices outside our visibility.The activity is global, but it is concentrated in a smaller set of countries. India leads by a wide margin, with roughly 83,000 affected users, followed by Vietnam with 61,000 and Brazil with 39,000. The Philippines (23,000), Poland (18,000), Indonesia (18,000) and Thailand (14,000) form a second tier, with Egypt, Spain, Ukraine, Argentina, Mexico and Italy each accounting for 10,000 to 13,000. The spread across South and Southeast Asia, Latin America, and Eastern and Southern Europe reflects where the host apps that carry this software are most widely installed.That geographic spread likely reflects a combination of app distribution, local install patterns, user appetite for free or pay-for-bandwidth apps, and the economics of bandwidth resale. It should not be read as a simple ranking of where residential proxy abuse originates.The chart below breaks down the threats we observed in this proxy-related traffic. Two categories dominate: phishing and malicious advertising. Behind them sits a long tail of consumer-facing fraud, including e-shop scams, generic scams, financial scams, and fake-tutorial pages, alongside outright malware such as trojans and other malicious traffic.&nbsp;

In the malicious proxy traffic we observed, scams and malware dominate. The important point for consumers is not only that their connection may be used by someone else. It is that, when abuse happens, their household IP address can be the return address.<h2>What consumers can do</h2>Most people will not identify a residential proxy component by name. But there are practical warning signs.Review “free” VPNs, proxy apps, bandwidth-sharing apps, and utilities from unknown publishers. Check whether they mention bandwidth sharing, peer-to-peer networking, traffic routing, or network participation in their terms. If an app offers payment for leaving your device online, assume your connection may be resold unless the provider explains exactly how traffic is vetted and who can buy access.On phones and computers, remove apps you do not recognize or no longer use. On home routers, check the list of connected devices and look for anything unfamiliar. If your ISP sends abuse notices, websites start blocking you, or your home IP address suddenly fails fraud checks, a proxy component is one possible explanation.Security software can also help by detecting unwanted proxy clients, bundled components, and related malicious activity. But this should not be a guessing game for consumers. Software that turns a household connection into a commercial exit node should be named clearly and made easy to remove.<h2>The takeaway&nbsp;</h2>With residential proxiesthe user’s connection becomes an infrastructure for someone else’s business.The issue is not that residential proxy technology exists. It has legitimate uses. The issue is whether people know when their devices are part of it, understand what kind of traffic may pass through their connection, and have a real choice to opt out.The fix is not to pretend consumers will read every EULA. This software should be named clearly, shown plainly, and made easy to remove before a household connection becomes someone else’s infrastructure.

Your home connection could be routing traffic for strangers. Here's how residential proxy networks work, how devices get enrolled and what our telemetry reveals about the risks for consumers.

Who's Really Using Your Home Internet Connection?

<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

Malicious software is often designed to hide its true behavior, wrapping its underlying logic in layers that make it difficult to analyze. “Unpacking” is the process researchers use to peel back those layers and reveal what the software is actually doing. But when standard tools run into unfamiliar or custom protections, this process can quickly become slow and highly manual. This project explores whether AI can help bridge that gap, assisting with the more repetitive parts of analysis so researchers can focus on validation, reasoning, and the parts of reverse engineering that still require human judgment.&nbsp;Automated static binary unpacking is hard, and it tends to stop being hard in a useful way only when the packer is familiar. There are good unpackers out there, including some internal ones, but once you get out of their comfort zone, unsupported packers, custom routines, or slightly unusual logic can bring the whole process to a halt.&nbsp;That makes unpacking a good example of where&nbsp;<a href="https://www.gendigital.com/blog/insights/research/engineering-the-future-of-agentic-threat-hunting">AI may genuinely help</a>. Across our broader work at Gen, we are increasingly using&nbsp;<a href="https://www.gendigital.com/blog/insights/research/engineering-the-future-of-agentic-threat-hunting">AI-assisted workflows</a> to accelerate parts of reverse engineering, technical correlation, and evidence organization, not as a substitute for expert analysis, but as a way to reduce friction in the slower, more repetitive parts of the job. The responsibility for verification and final conclusions still stays with the researcher.From that perspective, the idea behind what we call the Last-Resort Unpacker is fairly simple: if AI is good at spotting patterns and translating logic across representations, could it help us recover payloads in cases where traditional static unpacking pipelines fail? We are not trying to solve the hardest forms of protection here. The goal is more pragmatic, to cover more real-world cases, reduce manual effort, and potentially extract logic that can later feed back into our regular unpacking workflows.We came up with two approaches.<h2>High level design – Variant 1</h2>Given a sample to unpack, this simple premise has a few steps. Since we’ll go the most direct but also the riskiest way, let us call it a carpe diem variant.Identification:The first step is to identify which functions might be responsible for the unpacking and which buffers they are targeting. We have decided to use Gemini 2.5 Pro for our workload and complemented it with radare2&nbsp;reverse engineering framework to provide structured access to the disassembly. Once we identify the suspected decryption routine and the corresponding buffers, we may proceed to the translation.Translation: The next step relies on Gemini’s capabilities to rewrite the identified functions into their pythonic representation with prescribed function signatures.Verification: Now we have a Python code that hopefully faithfully represents the machine code identified in the first step. Now comes up the time to admit that we will be cheating on the initial assignment and we have to ensure that we will not be caught. We can easily statically check whether the source code is a valid source code. Now comes the tricky part – we have to verify that the product Python code does not have undesirable side-effects, be it from simple hallucinations or worse – due to hypothetical prompt injection. We have decided to do a strict abstract syntax tree (AST) verification which permits only certain keywords, operations, imports, and calls. If we obtain something that does not pass these criteria, then we have an interesting research case. In the other case, we may proceed to unpacking.Unpacking: Using the verified script, we attempt to do extraction on the source sample. Being paranoid, we shall use read-only environment with limited network access. If we succeed, we have not only an unpacked buffer but also an extraction script that we can use to improve our regular unpacking capabilities.<h2>High level design – Variant 2</h2>Now that we have attempted to do the most straightforward way, we can try to be a bit cleverer. Given the assumption that we can identify parts of the code responsible for the unpacking, adding two more assumptions that the unpacking code is localized and does not utilize any sophisticated system calls we could try to utilize emulator which should be a safe alternative that does not suffer from hallucinations. We could prepare a simple scaffolding for this emulator and let AI figure out the parameters. This would isolate non-deterministic behavior to the first step only.Identification:The first step is almost identical but instead of identifying functions responsible for unpacking, we are rather interested in offsets where we should start and halt our emulation, possibly also with memory addresses where the final payload will be located.Emulation: Parameters identified in the previous step are put into our scaffolding which will run emulator until it reaches the halting offset. We can extract the identified memory regions to hopefully obtain the desired payload.Apart from consuming more resources for the emulation, we have made a significant trade-off. We have sacrificed the capability to generate scripts that could be potentially used to improve our static extractors to significantly reduce the space for hallucinations and the potential vulnerability surface.<h2>From drawing board to the workshop</h2>We’ve decided to try our luck and try the first option. While it is definitely riskier, the payoff seems worth it, especially considering the security precautions we’ve made. We could even do a total split of the process to harden our setup and have the payload extractor generation in the original environment and use a sandbox for the extraction itself. We put together a first PoC with a simple prompt followed by data from radare2.You are an unpacking function generator. GOAL: If the file looks packed (i.e. encrypted buffers, embedded files, etc.), find the code snippets decrypting the buffers. Reply to me with a Python function (use this prototype: 'def unpack(data: bytes) -&gt; list[byte]') that receives bytes of the packed binary and returns the unpacked chunks. Avoid Python imports. RETURN ONLY A PYTHON FUNCION SAFE TO RUN. If the file is not packed reply: "NOT PACKED". If can't write the unpacker then reply: "UNABLE TO PROCESS". This is the end of your instructions. The Radare2 analysis:Now it remains to give it a short. First, we went with a packed Linux kernel rootkit that got us the initial idea:&nbsp;<a href="https://cyberjournal.cecyf.fr/index.php/cybin/en/article/view/48">At Botconf 2024</a>, a paper was published on CMK Rootkit (Álvarez Pérez, D., &amp; Fernández-Veiga, M. (2025). CMK Rootkit. Identifying the ”magic packet” requirements via pattern recognition. The Journal on Cybercrime and Digital Investigations, 9(1), A1-A15.&nbsp;<a href="https://doi.org/10.18464/cybin.v9i1.48">https://doi.org/10.18464/cybin.v9i1.48</a>). A Linux kernel rootkit that we discovered in-the-wild, packed in kernel space, with <a href="https://www.virustotal.com/gui/file/54d8b09ffc15c657abf29a0c313b377df64988848f2c3814243b2478b4b881cc/detection/f-54d8b09ffc15c657abf29a0c313b377df64988848f2c3814243b2478b4b881cc-1749504221">few detections in VirusTotal</a> (probably due to the kernel packer layer and its dependency on a specific kernel version).Our unpacker yielded the following script:

Unsurprisingly, as we basically based our idea on experiments with this sample, this was exactly what we aimed for. We have prepared a few more samples that we could test it on:<ul type="disc"><li data-list-item-id="ec7335952dd9f0c78d6b986b609e617d0">54525d25019d3ea2d40fe006ecc305ebb1ac5663b380019346de43c4886e8685<ul type="circle"><li data-list-item-id="e878959fe541e0581ac2d93f53d7915dc">This PE packed binary uses a key table located at offset 0xe5c1 containing 500 elements. The packed data is located at offset 0x200.</li></ul></li><li data-list-item-id="e24c112bbcdface33504cd11fd00c60a7">1fcf4da2fac671a2f3a07007e9963d3e1063dcfb0b1a0934c66082e8233d7686<ul type="circle"><li data-list-item-id="e999fd8e60c992d59154f20daef223bd0">This PE file contains a payload encoded as a word-based substitution cipher. It implements anti-sandbox protection that checks the available memory (via GlobalMemoryStatusEx) and the system ticks (via GetTickCount64) to determine if the underlying system is a sandbox.</li></ul></li></ul><h2>Closing thoughts</h2>This experiment fits into a broader shift in how we are using AI in technical research. The value is not in handing over reverse engineering to a model and hoping for the best. It is in identifying narrow, high-friction parts of the workflow where pattern recognition and code translation can save analysts time, while validation, judgment, and final conclusions remain firmly in human hands.A last-resort unpacker will not solve the hardest cases, and it is not a replacement for established tooling or expert analysis. Advanced multi-layer protections, virtualization, and heavily environment-dependent logic still remain difficult territory. But even within those limits, the approach already shows practical value. If it helps recover payloads from unsupported packers, reduces manual effort in repetitive cases, or produces reusable logic that can later improve our existing unpacking pipelines, then it is worth pursuing.Seen from that perspective, the point is not that AI is replacing reverse engineers. It is that it can make experienced researchers faster when conventional automation runs out of road.

Exploring how AI can assist in unpacking protected binaries, recovering payloads from unsupported packers, while reducing repetitive analysis

Building a last-resort unpacker with AI

Senior Malware Analyst

You need Photoshop for a side project. The subscription is expensive. You search for a free Mac download, land on a site that looks good enough, and within an hour, the app is running. A few weeks later, your Mac is hot even when you're not doing much. The fans stay loud. The battery drains fast. Activity Monitor shows a process with a name you do not recognize, chewing through CPU in the background. That "free" app may have cost you your machine's processing power, your saved passwords, your browser sessions or access to the files on your disk. That trade is more common than many Mac users think.&nbsp;In the first 48 hours after we began tracking one recent cracked software distribution wave, our protections blocked about 108,000 attempts to launch these applications on macOS systems. This is a count of blocked launch attempts recorded by one of our shields, not unique devices. Of those detections, 58% came from Macs still supported by Apple, while 42% came from systems that no longer receive Apple's current support. The pattern is already clear: even for MacOS, cracked apps are still a practical delivery channel for cryptominers, infostealers, adware and long-term persistence.&nbsp;<h2>The signature that keeps showing up&nbsp;</h2>Open enough cracked Mac apps and you start to see recurring patterns. One of the patterns we see quite often is a string left by the TNT group:&nbsp;<code>.TNT - why join the navy if you can be a pirate&nbsp;</code>Still, this story will not be about them but rather the whole distribution pipeline around them.&nbsp;A crack may start as a modified version of a legitimate app with license checks removed. After that, it often moves through mirror sites, torrent uploads, forum reposts, Telegram channels and re-packed archives. Every handoff is another chance to add something extra to the file. That is the real problem. Users rarely download a package from a trusted source. They are downloading whatever the last person in the chain decided to upload.&nbsp;<h2>Why people still download cracked Mac apps&nbsp;</h2>Cost is one of the obvious reasons. A subscription can feel absurd when you need an app for one class, one freelance project or one weekend task. There is another reason too: old hardware. Plenty of people still use older Macs that work well enough for everyday tasks, but no longer run the latest macOS release. Once that happens, the legitimate software market starts shrinking around them. New versions no longer support old releases; official downloads for old versions get harder to find and compatibility drops. The machine still works, but the software ecosystem has moved on.&nbsp;That is exactly when cracked software starts to look practical. It promises a version that runs on old hardware, costs nothing and solves the problem immediately. What it does not say is that older Macs are often already at a disadvantage. They may miss newer security features and recent patches. If the installation also tells the user to weaken built-in protections, the system becomes much easier to abuse.&nbsp;<h2>How cracked apps reach users&nbsp;</h2>Cracked macOS software spreads through a loose supply chain. Some files come from dedicated download sites that look almost like real app stores. Others spread through torrent trackers, warez forums, Telegram channels, Discord communities and file-sharing links posted on SEO-driven "download" pages. The packaging changes, the mirrors change and the hostnames change. The model stays the same.&nbsp;The important point is not which site was involved. It is that every re-upload, re-zip and re-pack creates another opportunity to insert a payload. The original crack may be clean, but the version on a mirror site may already tell a different story; the torrent may include an extra installer, the archive posted to a chat channel may have been modified again. There is usually no reliable way for a typical user to tell the difference before running it.&nbsp;

<h2>The installation guide often tells users to defeat macOS security&nbsp;</h2>This is where the risk becomes very concrete. MacOS includes several protections that are meant to stop exactly this kind of software from running freely. Gatekeeper helps block apps from unidentified developers. Notarization checks software distributed outside the App Store for known malicious content. System Integrity Protection, or SIP, helps prevent changes to critical parts of the system. Permission controls limit what apps can read and modify. And cracked software install guides often tell users to work around those protections step by step.&nbsp;Typical instructions often include:&nbsp;<ol type="1"><li data-list-item-id="eae2da4be03f3a2b670f4a813338399f9">Disable Gatekeeper so unsigned apps can run.&nbsp;</li><li data-list-item-id="eb606b7c024052b2cb4a67f27b385f2ce">Boot into Recovery Mode and disable SIP.&nbsp;</li><li data-list-item-id="ef4a9d11e409c539f6414805c6023c5d8">Launch the crack tool with administrator privileges.&nbsp;</li><li data-list-item-id="e10349530c6710071bdfe76740183e59c">Grant Full Disk Access when prompted.&nbsp;</li></ol>

This matters because an attacker does not need to discover an advanced exploit if the user has already done the hard part for them. If an unsigned binary gets root privileges and Full Disk Access on a system with weakened protections, it has significantly more room to do real damage. On older Macs, the risk is even worse. When the OS is already behind on security updates, turning off the protections that remain can leave the system exposed for months.&nbsp;<h2>What actually gets bundled with these downloads&nbsp;</h2>Unfortunately, these payloads are a bit like a box of chocolates; you never know what you're gonna get. We tried to go through stats to identify some main types of these unwanted payloads.&nbsp;<ul type="disc"><li data-list-item-id="ed1f60a74a2f7183221b82c6518f4a758">Cryptominers, such as XMRig, aim to use computer resources to mine cryptocurrency. Thus, besides the original app, the Mac will also do a second job in the background, usually resulting in high CPU usage, loud fans and significantly reduced battery life.&nbsp;</li><li data-list-item-id="ee96f909440c5a529a92515c4c36aebfc">InfoStealers, on the other hand, are trying to stay under the radar, collect various data on the device and send it to the control server. Usually, they are after saved passwords, cookies, session tokens, wallet data or documents as these can be monetized quickly. Remember that full disk access in step 4? That one made it a whole lot easier.&nbsp;</li><li data-list-item-id="ecff0fdd0b1b2cd2b26f1850f4425166b">Backdoors are usually trying to stay low and provide persistent access to the computer, be it to deploy further malware or use the computer as a staging point for future actions (e.g., email campaign). On macOS, this usually means LaunchAgents or Launch Daemons that run automatically after rebooting.&nbsp;</li><li data-list-item-id="efc64564b7278b592130a283c2184891b">Adware and Browser Hijackers are less dramatic than previous categories but still annoying, nonetheless. They may redirect your search, inject ads, change browser settings and commit affiliate fraud (e.g., injecting their affiliate links) - as the monetization relies on abuse of (semi-)legitimate internet infrastructure; these are the easiest ways to monetize. To be clear, the point is not that every cracked app is booby-trapped with these payloads. Unfortunately, obtaining cracked software may require you to trust an anonymous supply chain with system-level access to your systems, with limited options to verify what you got.&nbsp;</li></ul><h2>The Mac myth still helps to keep this pipeline alive&nbsp;</h2>A lot of people still carry some version of the old belief that Macs do not really get malware. That idea was always overstated, as this usually relied on Macs being a smaller market for malware authors than the Windows ecosystem. Today, with many threats going multi-platform, this idea is actively dangerous.&nbsp;While macOS has its own built-in protections that manage to provide some level of protection, they still rely on the user installing the software from trusted sources and crucially (but unsurprisingly), the user must not turn them off to have any effect. The moment a user starts disabling Gatekeeper, turning off SIP and granting broad permissions to unsigned binaries, the system is left exposed.&nbsp;This combination makes cracked software on macOS quite an attractive delivery method. Leading the user to disable their AV is one of the oldest tricks in the book, but this one takes it a step further by implicitly banking on a still-popular myth to avoid raising suspicion.&nbsp;<h2>Is there a way back?&nbsp;</h2><ol type="1"><li data-list-item-id="e36f761d5067b1f3b97fcb41250f533d3">Re-enable SIP in Recovery Mode:&nbsp;</li></ol><code>csrutil enable&nbsp;</code><ol type="1"><li data-list-item-id="e13ce9ed1846fa4c90cd5595cc3c40d05">Then re-enable Gatekeeper in Terminal:&nbsp;</li></ol><code>sudo spctl --master-enable&nbsp;</code><ol type="1"><li data-list-item-id="e3f587d06dd59561a968a5024f26dcf20">Check common persistence locations. Unfamiliar .plist files or oddly named helper items are worth investigating.&nbsp;</li></ol><code>~/Library/LaunchAgents/&nbsp;</code> <code>/Library/LaunchAgents/&nbsp;</code> <code>/Library/LaunchDaemons/&nbsp;</code> <code>~/Library/Application Support/&nbsp;</code><ol type="1"><li data-list-item-id="e844d0ecf4d5c86132be363b690f9a29c">Run a full system scan using e.g., Avast for Mac or Norton Security for Mac. Running it on the Downloads folder might not be enough.&nbsp;</li><li data-list-item-id="ead808099598a77195a50d27149505dc1">Consider changing important passwords. Start with email, banking, work accounts, cloud storage, and any password manager. This is especially crucial if the full system scan identified malware.&nbsp;</li><li data-list-item-id="eb0715b9167c2c31908d76fb610c0f80e">Watch for follow-on activity. Look for login alerts, suspicious financial activity and unusual browser behavior that continue after removing the cracked app. Consider a clean install if the system was compromised for an extended period. The safest approach is a full macOS reinstall from Recovery Mode. Back up personal files (documents, photos — not applications), wipe the drive, reinstall macOS and restore only verified clean data.&nbsp;</li></ol><h2>The real price of "free"&nbsp;</h2>Cracked software might sound attractive because it solves an immediate problem. It saves money. It keeps an old Mac useful for a little longer. It avoids a subscription that people don't want to pay. But the actual deal may look different once you include the hidden terms. Even when the original crack is benign, things may get tangled once you consider distribution, potentially including other middleman in the transaction, with each possibly vying for their share.&nbsp;If the goal is simply to get work done, the safer alternatives are boring but real: official older installers when vendors provide them, open-source alternatives, lower-cost one-time-purchase tools, student licensing or used hardware that can still run a supported macOS release.&nbsp;<h2>Our role: Protection, not judgment&nbsp;</h2>As a company behind reputable antivirus solutions such as Avast, AVG and Norton, we want to be clear: we don't judge users by the software choices they make. We understand that people might turn to cracked software to solve their problems, be it tight budgets, old hardware that still works or needing an app for a short-term project. We get it.&nbsp;Our job is not to be the software police.&nbsp;Our job is to protect you from threats that hide in the places where you might be looking for solutions.&nbsp;When our detection systems flag a cracked app, it is not because we disapprove of your choice to use it. It is because we found something genuinely malicious bundled with it - a cryptominer, an infostealer, a backdoor or other malware that puts your system and data at risk.&nbsp;If we block a cracked app, you can be confident that we detected something nasty lurking inside. Our goal is to let you make informed decisions about the software you install, with full knowledge of what risks come with it.&nbsp;<h2>Final thought&nbsp;</h2>The quote that TNT and other groups stamp on cracked software, "Why join the Navy if you can be a pirate?", was about thinking differently. About building something new instead of following the established path. About creativity and independence. Using it to brand cracked software misses the point entirely. The real pirates in this story are usually not the users trying to save money or keep an old Mac useful for a little longer. They are the actors further up the chain: repackagers, mirror operators, uploaders and malware distributors who turned cracked software into a monetization engine.&nbsp;That is what makes this ecosystem work so well. The person downloading the app thinks they are taking a shortcut. They might be stepping into a supply chain built to extract value from them, whether that means mining cryptocurrency in the background, stealing credentials, hijacking browser traffic or keeping a foothold on the machine for later.&nbsp;Still, the end decision is not ours to make.&nbsp;If you make your decision, step with care.&nbsp;As you might eventually realize that you did not join the Navy. You did not become a pirate. You just ended up as cargo.

Adolf Středa

Posts by Adolf Středa

Building a last-resort unpacker with AI

Why Join the Navy if You Can Be a Pirate?