Threat Analysis Engineer

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.

Security Evangelist at Gen

Research

<p>A fake job interview page is easier to trust than a fake ticket shop.</p><p>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.</p><p>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.</p><p>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.</p>

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

<p>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.</p><p>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.</p><p>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.</p><p>The login flow was the clearest part. Credentials were posted to:</p><p>fifeq2026eqbackeq[.]onrender[.]com/api/login</p><p>The 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.</p><p>A page can look like it opened a Google login without ever handing control to Google.</p><p>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;</p><h4>The recruitment lure went beyond FIFA</h4><p>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.</p><p>Researchers found similar fake hiring pages abusing other major brands, including <strong>Heineken, Hilton, Coca-Cola, Netflix, PepsiCo, Delta,&nbsp;</strong>and<strong> Spotify</strong>. 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.</p>

<p>Additional hunting found two other recruitment-themed kits:</p><p>hxxps://aquent-talent-jobs[.]com<br>hxxps://hays-talent-opportunities[.]com</p><p>The 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.</p>

<p>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.</p><h4>Why fake recruitment pages work</h4><p>Recruitment gives attackers a clean excuse to ask for things that might otherwise raise suspicion.</p><p>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.</p><p>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.</p><p>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.</p><h4>What job seekers should check before signing in</h4><p>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.”</p><p>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.</p><p>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.</p><p>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.</p><h4>What companies can do</h4><p>Recruitment pages are public by design, which makes them easy to copy.</p><p>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.</p><p>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.</p><h4>Conclusion</h4><p>The fake FIFA hiring page worked because it did not feel unusual. It looked like a normal step in a hiring process.</p><p>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.</p><p>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.</p><p><strong>IOCs:</strong></p><p>hxxps://fifahiring[.]com<br>hxxps://careers-fifahiring[.]com<br>hxxps://fifajobs[.]com<br>hxxps://fifa-mg[.]shop<br>hxxps://heineken-careers[.]com<br>hxxps://aquent-talent-jobs[.]com<br>hxxps://hays-talent-opportunities[.]com<br>fifeq2026eqbackeq[.]onrender[.]com/api/login</p>

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

<p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>Here is how the market works, who pays for it, and what it can cost the household that unknowingly hosts it.</p><h2>What is a residential proxy</h2><p>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.</p><p>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.</p><p>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.</p><p>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.</p><h2>How consumer devices end up in these networks</h2><p>There are several ways a home device can become part of a residential proxy network. Some are explicit. Others are easy to miss.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p><h2>Why the "consent" is rarely meaningful</h2><p>Even when consent technically exists, it is often not the kind of consent most people would recognize.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p><h2>The risks consumers actually carry</h2><p>When a household device becomes a residential proxy node, the user takes on risks created by someone else’s traffic.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p><h2>What our telemetry shows</h2><p>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;<br>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.</p><p>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.</p><p>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.</p><p>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;</p>

<p>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.</p><h2>What consumers can do</h2><p>Most people will not identify a residential proxy component by name. But there are practical warning signs.</p><p>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.</p><p>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.</p><p>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.</p><h2>The takeaway&nbsp;</h2><p>With residential proxiesthe user’s connection becomes an infrastructure for someone else’s business.</p><p>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.</p><p>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.</p>

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?

Threat Researcher at Gen

<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><p>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;</p><p>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;</p>

<h4>GoFlateLoader’s Execution Flow&nbsp;</h4><p>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;</p><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><p>&nbsp;</p><p>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;</p>

<p>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;</p><p>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;</p>

<p>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;</p><p>&nbsp;</p><h4>Inflated PE Overlay&nbsp;</h4><p>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;</p>

<p>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;</p><p>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;</p><p>In most cases, GoFlateLoader’s PE overlays are filled with null bytes, though we have also observed builds with random padding.&nbsp;&nbsp;</p>

<p>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;</p><p>&nbsp;</p><h4>Delivered Final Payloads&nbsp;</h4><p>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;</p><p>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;</p><p>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;</p><p>&nbsp;&nbsp;</p><h4>Summary&nbsp;</h4><p>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;</p><p>Gen products protect our users against both GoFlateLoader and the payloads it delivers.&nbsp;</p><p>&nbsp;</p><h4>Indicators of Compromise (IoCs)&nbsp;</h4><p>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;</p><p>b88c5744975d2abb447aecc6c090fee9f8580413f4612eecdc6ed1973e8a1739 (password-protected archive containing GoFlateLoader x64 variant loading Remus; pwd: 1234)&nbsp;</p><p>ed5ae7f36453c5a23e9868a5729d67e0549a11f6dea54f5f52d654a8f51d4902 (archive containing GoFlateLoader x64 variant loading Remus)&nbsp;</p><p>841c9297cb8a2e0ff89433d13c05bfc760eb2e98e251cb8fa785d2ad7cbac05f (archive containing GoFlateLoader x86 variant loading Amatera)&nbsp;</p><p>ece7c48eb411b24f26762ede83badb4a644c41d5777129381ac2541804d64fc2 (archive containing GoFlateLoader x86 variant loading Lumma)&nbsp;</p><p>421ce2d2f49c23bbe9f60ef3b9cd38d7eb912ce02e56a61837656210069bd9e2 (archive containing GoFlateLoader x64 variant loading Vidar)&nbsp;</p><p>121c2dc793b3873f75a29ec02241f94136de19c049382a50a50d0d5b99507073 (GoFlateLoader x64 variant loading StealC)&nbsp;</p><p>2415db5081cec9bfd14ad6da1a66169fd96f13a49010c319a73d1ed6fafd4efa (GoFlateLoader x64 variant loading Vidar)&nbsp;</p><p>d9917ade3b4c125a95b5d3e6343cde26145dfbf569bd7e2a843fd0c6fc8ddc28 (GoFlateLoader x64 variant loading Remus)&nbsp;</p><p>4cf6893756f441522b94b36f10e5de0e47aeed4743f95c51650746d1ecf97e3d (GoFlateLoader x64 variant loading SvitStealer)&nbsp;</p><p>8b89d6c9152d3aab97aadd515ecb69ca72654db2f25425759ba4b646853d737d (GoFlateLoader x86 variant loading Lumma)&nbsp;</p><p>90ce4ff9da23ac150da0a8e17930cab1e369aa349fdc1b65691b70369145664a (GoFlateLoader x86 variant loading Amatera)&nbsp;</p>

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

GoFlateLoader: A Widespread Golang Loader Delivering Multiple Infostealers

Malware Researcher

<p>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;</p><p>Then something else happened.&nbsp;</p><p>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;</p><p>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;</p><p><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;</p><p>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;</p><p>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;</p><p>The result is a much clearer picture.&nbsp;</p><p>We identified more than <strong>350 compromised accommodations</strong> linked to the Reservation Hijack scam flows, spread across <strong>50 countries</strong>. 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;</p><p>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;</p><p>And they are doing it at scale.&nbsp;</p>

<h3>How we connected the evidence&nbsp;</h3><p>The second phase of our investigation was already underway when Booking.com’s customer notifications started circulating publicly.&nbsp;</p><p>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;</p><p>Put together, those pieces show how reservation data moves from compromise to fraud.&nbsp;</p><p>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;</p><p>From that process, we extracted and cleaned accommodation names from captured fraudulent landing pages and identified <strong>350 compromised accommodations</strong>.&nbsp;</p><p>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;</p><p>The end result for the traveler is the same. Criminals had access to information that should have stayed inside the booking relationship.&nbsp;</p><p>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;</p><p>So 350 is not the size of the problem. It is the part we could see clearly.&nbsp;</p><h3>From scam pattern to repeatable operation&nbsp;</h3><p>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;</p><p>The model we see is consistent.&nbsp;</p><p>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;</p><p>The wording, the property, the country change. The logic does not.&nbsp;</p><p>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;</p><h3>A worldwide footprint, led by Europe&nbsp;</h3><p>The highest number of compromised accommodations in our dataset were linked to <strong>Germany</strong>, followed by <strong>France</strong>, the <strong>United Kingdom</strong>, <strong>Italy</strong>, <strong>Spain</strong>, and the <strong>United States</strong>.&nbsp;</p><p>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;</p><p>Together, the top five European countries, Germany, France, the United Kingdom, Italy, and Spain, accounted for <strong>159 compromised accommodations</strong>, about <strong>45%</strong> of the full dataset.&nbsp;</p>

<p>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;</p><p>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;</p><p>But the country distribution also shows that this is not a single-region problem. The activity spreads across <strong>50 countries</strong>, with a long tail of destinations beyond the largest clusters.&nbsp;</p>

<p>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;</p><p>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;</p><h3>Not just hotels&nbsp;</h3><p>Hotels make up the largest share of the dataset, but they are not the whole story.&nbsp;</p><p>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;</p>

<p>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;</p><p>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;</p><p>That is exactly the gap criminals are abusing.&nbsp;</p><h3>How big could this problem be?&nbsp;</h3><p>To understand the potential scale of Reservation Hijack scams, we looked at the accommodation properties appearing in our research dataset.&nbsp;</p><p>In total, we identified <strong>350 accommodation entries</strong> 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;</p><p>Across the deduplicated dataset, these properties represented an estimated maximum capacity of around <strong>82,000 guests at any given time</strong>. Using a conservative occupancy rate of <strong>50%</strong> and an average length of stay of <strong>2.5 nights</strong>, this translates into approximately <strong>6 million guest stays per year</strong>.&nbsp;</p><p>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;</p><p>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;</p><p>That is why Reservation Hijack scams are so convincing. The scam does not feel like spam. It feels like part of the trip.&nbsp;</p>

<h3>The activity does not look random&nbsp;</h3><p>We are still investigating the infrastructure behind these attacks, so we are not attributing the activity to a named group.&nbsp;</p><p>But the evidence does not look like hundreds of unrelated actors randomly copying one another.&nbsp;</p><p>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;</p><p>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;</p><p>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;</p><p>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;</p><p>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;</p><h3>Correct information is not proof of legitimacy&nbsp;</h3><p>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;</p><p>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;</p><p>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;</p><p>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;</p><p>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;</p><h3>What travelers should do&nbsp;</h3><p>The safest rule is simple: <strong>do not treat correct booking information as proof that a message is legitimate.</strong>&nbsp;</p><p>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;</p><p>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;</p><p>This applies even if the message mentions the right hotel, the right destination, or details from your stay.&nbsp;</p><p>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;</p><p>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;</p><h3>What accommodations should take from this&nbsp;</h3><p>For hotels and other accommodation providers, this is not only a guest-awareness issue. Guest communication has become part of the attack surface.&nbsp;</p><p>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;</p><p>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;</p>

<p>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;</p><p>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;</p><p>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;</p><p>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;</p><p>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;</p><h3>What the wider travel ecosystem should learn&nbsp;</h3><p>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;</p><p>Each view is partial, and that delay helps the scam continue.&nbsp;</p><p>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;</p><p>One compromised accommodation can look like a local incident. Hundreds of compromised accommodations across 50 countries point to a repeatable abuse model.&nbsp;</p><h3>The next phase of travel fraud&nbsp;</h3><p>Reservation Hijack scams exploit a simple mistake in how we judge trust online.&nbsp;</p><p>Travelers think: “This message knows my booking, so it must be real.”&nbsp;</p><p>Criminals know: “If we have the booking context, the victim will do much of the trust work for us.”&nbsp;</p><p>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;</p><p>Our latest data shows that this model has spread well beyond isolated cases. We identified <strong>350 compromised accommodations across 50 countries</strong>, 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;</p><p>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;</p><p>For travelers, the lesson is simple.&nbsp;</p><p>Trust your booking. Do not automatically trust the message.&nbsp;</p>

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

<p>Attackers replaced selected official download links with malicious installers that deployed a Python bot, r77 rootkit components, and Windows policy-based defense evasion.</p><p>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.</p><p>&nbsp;</p><p>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.</p><p>&nbsp;</p><h2>The infection chain</h2><p>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:</p>

<p>Stage 2 acts as the campaign's quartermaster. It deploys five components from XOR-encrypted resources embedded in its PE (key: <code>fywo</code>):</p><table class="table"><thead><tr><th>Component</th><th>Purpose</th></tr></thead><tbody><tr><td><strong>PyArmor bot</strong></td><td>The main payload, a Python script with obfuscated bytecode</td></tr><tr><td><strong>PyArmor runtime</strong></td><td>DLL that decrypts the bot's bytecode at execution time</td></tr><tr><td><strong>r77 rootkit stager</strong></td><td>Native x86 PE that hides the bot's process, files, and registry keys</td></tr><tr><td><strong>WDAC Code Integrity policy</strong></td><td>Blocks 50 AV/EDR executables from running after reboot</td></tr><tr><td><strong>Python 3.14.3 installer</strong></td><td>The legitimate Python runtime (needed to run the bot)</td></tr></tbody></table><p>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.</p><p>&nbsp;</p><h2>Killing your antivirus before it can fight back</h2><p>The most operationally significant component is the WDAC Code Integrity policy.</p><p>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:</p><table class="table"><thead><tr><th>Target</th><th>Blocked executables</th></tr></thead><tbody><tr><td><strong>Avast</strong></td><td>AvastSvc.exe, AvastUI.exe, afwServ.exe, aswEngSrv.exe, aswidsagent.exe, aswToolsSvc.exe, wsc_proxy.exe, Icarus components, Overseer</td></tr><tr><td><strong>AVG</strong></td><td>All executables under Program Files\AVG\Antivirus</td></tr><tr><td><strong>Avira</strong></td><td>Security, VPN, Fallback Updater, Optimizer Host, Endpoint Protection SDK</td></tr><tr><td><strong>Windows Defender</strong></td><td>MsMpEng.exe, MpCmdRun.exe, MpDefenderCoreService.exe</td></tr><tr><td><strong>Windows Security</strong></td><td>SecurityHealthHost.exe, SecurityHealthService.exe, SecurityHealthSystray.exe</td></tr><tr><td><strong>Windows Update</strong></td><td>MoUsoCoreWorker.exe, wuauclt.exe, wusa.exe</td></tr><tr><td><strong>Others</strong></td><td>HitmanPro, Kaspersky Virus Removal Tool, Farbar Recovery Scan Tool</td></tr></tbody></table><p>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.</p><p>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.</p><h2>&nbsp;</h2><h2>How the dropper provisions the bot</h2><p>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.</p><p>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:</p><ul><li data-list-item-id="e15e9100cccadef17d91598e244b0b038"><strong>CampaignID</strong> a unique identifier for this campaign, used during bot registration with the C2 server.</li><li data-list-item-id="e7a2666057614636498fba93e483028cf"><strong>DDRList</strong> six dead-drop resolver URLs that the bot uses to discover its C2 servers (detailed in the next section).</li></ul><p>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.</p><p>&nbsp;</p><h2>Dead-drop resolvers: how the bot finds its C2</h2><p>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.</p><p>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.</p><p>We extracted all six dead-drop resolver URLs from the dropper and probed them:</p><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><p>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.</p><p>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.</p><p>&nbsp;</p><h2>The C2 protocol: RSA-OAEP wrapping AES-GCM</h2><p>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:</p>

<p>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.</p><p>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.</p><p>All identified C2 and staging servers are compromised <a href="https://qloapps.com/">QloApps</a> hotel reservation websites:</p><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><p>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.</p><h2>&nbsp;</h2><h2>The r77 rootkit stager</h2><p>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:</p><ol><li data-list-item-id="e9ca3772c2bd6052d86931268e23d036a"><strong>AMSI bypass</strong> — 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"><strong>Reflective DLL injection</strong> — 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"><strong>Registry persistence</strong> — 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"><strong>Service creation</strong> — installs a Windows service (<code>$77svc</code>) to ensure the rootkit survives reboots independently of the registry mechanism.</li></ol><p>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.</p><h2>&nbsp;</h2><h2>The Python bot under PyArmor v9</h2><p>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.</p><p>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.</p><p>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.</p><h2>&nbsp;</h2><h2>Impact and recommendations</h2><p>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.</p><p>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>.</p><p>We decrypted the PyArmor-protected Python bytecode from the Linux payload and confirmed it is <strong>the same bot source code</strong> 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.</p><p>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.</p><p><strong>If you ran the trojanized installer:</strong></p><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><p><strong>For defenders:</strong></p><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><p>Full IOC list available on <a href="https://github.com/avast/ioc">GitHub</a>.</p><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><p>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>.</p><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><p>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.</p><p>&nbsp;</p>

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

<p>In recent weeks, our Threat Labs researchers have uncovered two new toolsets that show just how adaptive the DPRK’s operations have become. Kimsuky, known for its espionage-style campaigns, deployed a new backdoor we’ve named <strong>HttpTroy</strong>, while Lazarus introduced an upgraded version of its <strong>BLINDINGCAN</strong> remote access tool.&nbsp;</p><p>Both attacks reveal the same underlying pattern: stealthy code and layered obfuscation. In this post, we’ll break down how these tools work, what they target and what defenders can learn from the latest moves inside the DPRK playbook.&nbsp;</p><h2>Inside DPRK’s Latest Campaigns: How Kimsuky and Lazarus Refine Their Playbook&nbsp;</h2><p>The Kimsuky attack targeted a single victim in KR and started with a ZIP file that looked like a VPN invoice, then quietly installed tools that let attackers move files, take screenshots and run commands. The chain has three steps: a small dropper, a loader called MemLoad, and the final backdoor, named “HttpTroy”. We see several signs that possibly tie this activity to Kimsuky: the Korean language lure, an AhnLab-style scheduled task name and command patterns seen in past Kimsuky work.&nbsp;&nbsp;</p><p>The Lazarus attack targeted two victims in CA and was caught in the middle of the attack chain, where we observed a new version of the Comebacker malware leading to a new variant of their BLINDINGCAN remote access tool.&nbsp;&nbsp;</p><p>We’ll explain how these attacks work in plain terms, why North Korea-aligned groups use these tricks, and simple steps you can take to avoid similar threats.&nbsp;</p><p><strong>Quick takeaways for readers:</strong> do not open any attachments you did not expect, treat .scr files as programs not documents and keep your security software on and updated.&nbsp;</p><h2>Diving into the Kimsuky attack: MemLoad and HttpTroy&nbsp;</h2><h3>The Initial dropper&nbsp;</h3><p>While the exact delivery mechanism remains unknown, telemetry indicates that the samples was obtained via an internet download, packaged within a ZIP archive named “<strong>250908_A_HK이노션_SecuwaySSL VPN Manager U100S 100user_견적서</strong>”. Given the nature of the filename, it is highly probable that the archive was distributed through a phishing email.&nbsp;</p><p>Contained within the archive is a “.scr” file bearing the same name. Execution of this file initiates the entire infection chain.&nbsp;</p><p>This initial sample is a lightweight GO binary containing 3 embedded files. These files are decrypted using a simple XOR operation with the key “0x39”, then written to disk and executed. To maintain user deception, the binary displays a PDF document as a decoy, displaying a fake bill for VPN services, while simultaneously registering the next stage backdoor as a COM server using “regsvr32.exe”. See below an image of the decoy PDF and the decompiled code that decrypts it.&nbsp;</p>

<h3>Stage 1 backdoor: Memload_V3&nbsp;</h3><p>The first backdoor, internally identified as <strong>Memload_V3</strong> (“Memload_V3.dll”), performs 2 primary functions:&nbsp;</p>

<ol><li>Re-registering the “AhnlabUpdate” scheduled task&nbsp;</li></ol><p>Using COM interfaces instantiated via the Windows API “CoCreateInstance”, the backdoor re-creates a scheduled task named “AhnlabUpdate”, targeting one of the more prevalent anti-virus software in the Korean region to avoid suspicions. This task is configured to execute the current DLL silently using the command “regsvr32.exe /s &lt;CURRENT_FILENAME&gt;”. The task is then set to repeat every minute, using the “PT1M” repetition pattern.&nbsp;</p>

<p>2. Decrypting and executing the final payload&nbsp;</p><p>After establishing persistence, Memload_V3 proceeds to decrypt the third file dropped by the initial sample. The decryption algorithm used is RC4, and once decrypted, the payload is loaded directly into memory. Execution is then triggered via its exported function named “hello”.</p>

<h3>Final payload: HttpTroy backdoor&nbsp;</h3><p>The final stage of the attack chain is a highly obfuscated backdoor, internally named <strong>HttpTroy</strong> (“httptroy_dll.dll”). This component grants the attackers full control over the compromised system, offering a wide range of capabilities:&nbsp;</p><ul><li>File upload and download</li><li>Screenshot capture and exfiltration</li><li>Command execution with elevated privileges</li><li>Loading an executable in memory</li><li>Reverse shell</li><li>Process termination and trace removal&nbsp;</li></ul><p>HttpTroy employs multiple layers of obfuscation to hinder analysis and detection. API calls are concealed using custom hashing techniques, while strings are obfuscated through a combination of XOR operations and SIMD instructions.&nbsp;</p><p>Notably, the backdoor avoids reusing API hashes and strings. Instead, it dynamically reconstructs them during runtime using varied combinations of arithmetic and logical operations, further complicating static analysis.&nbsp;</p><p>&nbsp;</p>

<p>The HttpTroy backdoor communicates with its command-and-control server exclusively via HTTP POST requests. All transmitted data (both commands and responses) is obfuscated using a two-step process: XOR encryption with the key 0x56, followed by Base64 encoding. Each query to the C2 has a specific ID and is followed by buffers of interest, all formatted in a single special string. Commands received from the server follow a simple structure: “&lt;command&gt; &lt;parameters&gt;”.&nbsp;</p>

<p>After executing a command, the backdoor reports the result to the C2 using ID 2:&nbsp;</p><ul><li>ok - Successful execution</li><li>fail - Execution failed</li><li>connect ok - Successful reverse shell connection&nbsp;</li></ul><p>To request a new command from the C2, the backdoor sends a query with ID 1.&nbsp;</p><p>Below, you can see an example flow of a command in decompiled code, together with the decryption flow of a response from the server for the “down” command:&nbsp;</p>

<h3>Indicators of compromise&nbsp;</h3><p>SCR file: e19ce3bd1cbd980082d3c55a4ac1eb3af4d9e7adf108afb1861372f9c7fe0b76&nbsp;</p><p>Memload_V3: 20e0db1d2ad90bc46c7074c2cc116c2c08a8183f3ac6f357e7ebee0c7cc02596&nbsp;</p><p>HttpTroy: 10c3b3ab2e9cb618fc938028c9295ad5bdb1d836b8f07d65c0d3036dbc18bbb4&nbsp;</p><p>C2: hxxp[://]load[.]auraria[.]org/index[.]php&nbsp;</p><p>User-agent (wide-string): Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.130 Safari/537.36&nbsp;</p><p>Mutexes:&nbsp;</p><p>a:fnjiuygredfgbbgfcvhutrv&nbsp;</p><p>u:fnjiuygredfgbbgfcvhutrv&nbsp;</p><h2>Analysis of the Lazarus Attack: the comeback of Comebacker and the new BLINDINGCAN variant&nbsp;</h2><h3>Comebacker, yet again&nbsp;</h3><p>During routine threat monitoring, our team identified a sample indicative of a new variant of the previously documented Comebacker malware, which is attributed to the Lazarus APT group. While our telemetry captures the infection chain from this stage onward, the initial access vector remains unclear. However, based on the absence of any exploited vulnerabilities, we assess with moderate confidence that the initial compromise likely originated from a phishing email.&nbsp;</p><p>We observed two closely related instances of the Comebacker malware:&nbsp;</p><ul><li>A DLL variant located at “<strong>C:\ProgramData\comms.bin</strong>” (internally named “<strong>NetSvcInst_v1_Rundll32.dll</strong>”, using the exported function “<strong>InfoHook</strong>”)</li><li>An EXE variant located at “<strong>C:\ProgramData\Comms\ssh.bin</strong>”&nbsp;</li></ul><p>The DLL sample appears to have been executed via a Windows service, whereas the EXE variant was launched through cmd.exe, suggesting a different execution context.&nbsp;</p><p>Despite their differing formats, both variants share identical functionality. Their primary objectives include:&nbsp;</p><ul><li>Validating execution via a specific command-line parameter</li><li>Decrypting embedded payloads</li><li>Configuring registry entries</li><li>Deploying the next-stage payload as a service&nbsp;</li></ul><p>The dropper's behavior can be summarized in the following stages:&nbsp;</p><ol><li>Dynamic function resolution&nbsp;</li></ol><p>Prior to payload execution, the malware dynamically resolves API functions from system DLLs. This is achieved by XOR-decrypting hardcoded strings using a PRNG-like stream.</p>

<ol start="2"><li>Parameter validation&nbsp;</li></ol><p>Execution is gated by the presence of specific command-line arguments. The DLL variant expects "<strong>up45V3FR9ee9</strong>", while the EXE variant requires "<strong>760H33ls9L5S</strong>". If the parameters are incorrect, the malware terminates.&nbsp;</p><ol start="3"><li>Service name selection&nbsp;</li></ol><p>The malware enumerates the “<strong>REG_MULTI_SZ</strong>” entries under “<strong>HKLM\ SOFTWARE\Microsoft\Windows NT\CurrentVersion\Svchost\netsvcs</strong>”, identifying service names that are listed but not yet registered under “<strong>HKLM\ SYSTEM\CurrentControlSet\Services</strong>”. A random candidate is then selected and reused as both the service name and the name of the service DLL.&nbsp;</p>

<p>For some of the string decryption processes, it uses the HC256 stream cipher, while for others it uses RC4.&nbsp;</p>

<ol start="4"><li>Timestamp logging&nbsp;</li></ol><p>The malware writes the current local time to “C:\Windows\system32\AppxProvision.xml” using the format “<strong>[%04d-%02d-%02d %02d:%02d:%02d] %s\n</strong>”, appending the chosen service name. It then modifies the file’s attributes to mimic those of win32k.sys.</p>

<ol start="5"><li>Payload deployment and configuration&nbsp;</li></ol><p>The embedded PE file is decrypted using HC256, then decompressed via zlib, and written into a decoy file in the current directory named “<strong>kjepl.xml</strong>”. To obfuscate its true size, random data is appended at the end. The file is then moved via “cmd.exe” to “<strong>C:\Windows\system32\&lt;SERVICE_NAME&gt;.dll</strong>”, and its attributes are altered similarly to the timestamp file, but this time it mimics “cmd.exe”.&nbsp;</p>

<p>Subsequently, the embedded configuration is decrypted using HC256, re-encrypted using RC4 and stored as a binary blob in the registry under&nbsp; “<strong>HKLM\SYSTEM\ControlSet001\Services\kbddes\GUID</strong>”. The configuration includes potential C2 addresses, the path to cmd.exe, the path to the dropped DLL, %TEMP%, and a special integrity-check value.&nbsp;</p>

<ol start="6"><li>Service execution&nbsp;</li></ol><p>Finally, the malware registers and starts a service using the randomly selected name, thereby initiating the next stage of the attack.&nbsp;</p>

<h3>Second stage: Compcat_v1.dll&nbsp;</h3><p>Both observed variants of the Comebacker dropper deploy the same service binary, internally named “Compcat_v1.dll”. This component serves a singular purpose: it acts as a wrapper for the final payload.&nbsp;</p><p>Upon execution, it decrypts an embedded PE file using the HC256 stream cipher. The decrypted data is then decompressed using the miniz library, a lightweight zlib-compatible implementation. The resulting PE is loaded directly into memory, employing a technique similar to that used by the aforementioned MemLoad sample.&nbsp;</p><p>Once the payload is successfully mapped into memory, the malware invokes the exported function “<strong>OPENSLL_NONPIC</strong>”, thereby initiating the execution of the final stage.&nbsp;</p>

<h3>Final payload: new variant of BLINDINGCAN&nbsp;</h3><p>As the final payload, the attackers employed a new variant of their BLINDINGCAN remote access tool, improved with additional cryptographic elements and more capabilities. Internally named “<strong>T_DLL64.dll</strong>”, it acts as a complete suite for attackers, offering them the possibility to perform any action they desire.&nbsp;</p><p>The backdoor has 2 operating modes, which are dictated by the parameter passed to the main function:&nbsp;</p><ul><li>13398 – config stored in a file</li><li>13399 – config stored in registry&nbsp;</li></ul><p>Apart from the “<strong>OPENSSL_NONPIC</strong>” export, this sample exports another function, called “<strong>MemLoad</strong>”, which uses the other operating mode (13398).&nbsp;</p><p>The malware’s execution begins by dynamically loading APIs, using the same techniques as in the previous cases. It then moves on to loading the configuration, picking a C2 at random and attempting to connect to it.&nbsp;</p><p>Next, it attempts to authenticate itself within the chosen C2 by sending a “GET” request with the integrity-check value from the config, followed by 4 random uppercase letters. This newly obtained value is then shifted with a random offset between 0 and 9 (which is appended at the end of the buffer), XOR-encrypted with 0xC6 and Base64-encoded before being sent. To add to the authenticity of the request, the malware also sends additional buffers of key-value pairs filled with random values.&nbsp;</p><p>If the authentication succeeds, it proceeds to generate RSA-2048 keys for encryption. The public key is then sent to the C2 in a similar fashion. The same operations regarding the config integrity-check value are performed, and their value represents the beginning of the data. Following that, the values 23, 0, and the size of the public key are appended before the public key (separated by spaces), then encrypted with HC256 and the same XOR + Base64 combo as before. Similar randomness is added after, and the request is sent as such.&nbsp;</p>

<p>From the response, the malware obtains some RSA-encrypted values, that are then decrypted and will serve in future communications as key and IV for encryption via the EVP interface.&nbsp;</p>

<p>Due to the functions used in the structure that contains the “evp_cipher_st” struct for the encryption functions, we conclude that the encryption used is AES-128-CBC.&nbsp;</p><p>Subsequently, the malware enters its main command loop, constantly communicating with the C2 and executing the commands of the attackers.&nbsp;</p><p>The communication pattern to the C2 is now as follows:&nbsp;</p><ol><li>Join by space 2 per-command specific values and the size of the data to be sent</li><li>First parameter is the command ID</li><li><p>Second parameter represents the status of the command</p><p>1 – Success sending regular chunk (for streamed buffers – typically 100KB chunks)</p><p>2 – Error</p><p>3 – Success sending end chunk (if the buffers are not streamed, this value is used)</p></li><li>Append the data and AES-128-CBC encrypt everything</li><li>String-shift the same config token + 4 random letters by a random value (0-9) appended at the end, then XOR+Base64 encode it</li><li>Compute MD5 of the encrypted payload and append the encrypted data after the hash, then XOR+Base64 encode it</li><li>Build the base request data as “&lt;RANDOM_2_LETTERS&gt;=&lt;XOR+BASE64(SHIFTED_TOKEN || 4_RANDOM_LETTERS) || SHIFT_OFFSET&gt;&amp;&lt;RANDOM_4_LETTERS&gt;=&lt;RANDOM_4_OR_5_LETTERS&gt;&amp;&lt;RANDOM_5_LETTERS&gt;=&lt;XOR+BASE64(MD5 || ENCRYPTED_DATA)&gt;”</li><li>Append a random amount of random key-value pairs after the base data&nbsp;</li></ol>

<p>In similar fashion, responses are XOR + Base64 decoded, integrity-checked using MD5, and AES-128-CBC decrypted in case of a match.&nbsp;</p><p>Continuing, we will describe the functionalities of the RAT:&nbsp;</p><ul><li>Command ID 1<ul><li>Exfiltrate file, starting from an offset and compressing it</li><li>Input: &lt;SRC_PATH&gt;|&lt;OFFSET&gt;</li><li>Chunked</li></ul></li><li>Command ID 2<ul><li>Downloads a file from the C2</li><li>Input: &lt;DEST_PATH&gt;|&lt;SIZE_OF_DATA_BLOCK&gt;|&lt;BYTES_TO_WRITE</li><li>Chunked</li></ul></li><li>Command ID 3<ul><li>Copies a file to %TEMP% and exfiltrates it from there</li><li>Input: &lt;SRC_PATH&gt;|&lt;OFFSET&gt;</li><li>Chunked</li></ul></li><li>Command ID 4<ul><li>Securely delete a file by overwriting it and renaming it multiple times</li><li>Input: &lt;FILE_PATH&gt;</li><li>Not chunked</li></ul></li><li>Command ID 5<ul><li>Changes a file’s attributes to mimic another file</li><li>&lt;DEST_PATH&gt;|&lt;SRC_PATH&gt;</li><li>Not chunked</li></ul></li><li>Command ID 6<ul><li>Recursively traverses all sub-directories and files from a given path, also reporting their size</li><li>Input: &lt;SRC_DIR_PATH&gt;</li><li>Not chunked</li></ul></li><li>Command ID 7<ul><li>Traverses the entire file system, listing the empty space from drives, files and their attributes</li><li>Chunked</li></ul></li><li>Command ID 8<ul><li>Gathers data from the victim computer, such as locale info, computer name, OS version, MAC address, network adapters, CPU architecture, OEM code page</li><li>Not chunked</li></ul></li><li>Command ID 9<ul><li>Runs a command-line via CreateProcessW</li><li>Input: &lt;CMDLINE&gt;</li><li>Not chunked</li></ul></li><li>Command ID 10<ul><li>Runs a command-line in a given session via CreateProcessAsUserW</li><li>Input: &lt;CMDLINE&gt;|&lt;SESSION_ID&gt;</li><li>Not chunked</li></ul></li><li>Command ID 11<ul><li>Lists active processes (provides EXE name, full image path, PID, PPID, SID, user and creation time)</li><li>Chunked</li></ul></li><li>Command ID 12<ul><li>Kills a process</li><li>Input: &lt;PROCESS_ID&gt;</li><li>Not chunked</li></ul></li><li>Command ID 13<ul><li>Keep alive request</li><li>Not chunked</li></ul></li><li>Command ID 14<ul><li>Sleeps for a given period, checking for early wake conditions such as new drives or sessions every 5 seconds</li><li>Input: &lt;SLEEP_DURATION&gt;</li><li>Not chunked</li></ul></li><li>Command ID 15<ul><li>Hibernate for a given period, checking for early wake conditions every minute</li><li>Input: &lt;HIBERNATION_DURATION&gt;</li><li>Not chunked</li></ul></li><li>Command ID 16<ul><li>Updates the config from the binary’s current config</li><li>Not chunked</li></ul></li><li>Command ID 17<ul><li>Send the current config from the binary to the C2</li><li>Not chunked</li></ul></li><li>Command ID 18<ul><li>Removes traces, securely deletes itself and terminates itself</li><li>Not chunked</li></ul></li><li>Command ID 19<ul><li>Test TCP connection to a given IP address</li><li>Input: &lt;IP&gt;:&lt;PORT&gt; &lt;TIMEOUT&gt;</li><li>Not chunked</li></ul></li><li>Command ID 20<ul><li>Runs cmd.exe with the provided command line and reports back the output</li><li>Input: &lt;COMMAND_LINE&gt;</li><li>Chunked</li></ul></li><li>Command ID 21<ul><li>Changes the current working directory</li><li>Input: &lt;DIR_PATH&gt;</li><li>Not chunked</li></ul></li><li>Command ID 22<ul><li>Obtains the current working directory</li><li>Not chunked</li></ul></li><li>Command ID 23<ul><li>Updates encryption keys with new values</li><li>Not chunked</li></ul></li><li>Command ID 24<ul><li>Takes a screenshot and sends it to the C2</li><li>Chunked</li></ul></li><li>Command ID 25<ul><li>Enumerate or take a photo from the available video capture devices using COM interfaces; in the case of GETPIC, the resulting buffer is compressed</li><li>Input: &lt;GETLIST&gt; or &lt;GETPIC&gt; &lt;INDEX&gt;</li><li>Not chunked</li></ul></li><li>Command ID 26<ul><li>Runs a PE file in memory</li><li>Input: &lt;SIZE&gt;|&lt;EXPORT_NAME&gt;|&lt;ARGUMENT&gt;|&lt;MD5_OF_PE&gt;</li><li>Chunked</li></ul></li><li>Command ID 27<ul><li>Updates the config with data from the C2</li><li>Input: &lt;NEW_CONFIG_BYTES&gt;</li><li>Not chunked&nbsp;</li></ul></li></ul><h3>Indicators of compromise&nbsp;</h3><p>new Comebacker variants: 509fb00b9d6eaa74f54a3d1f092a161a095e5132d80cc9cc95c184d4e258525b&nbsp;&nbsp;</p><p>b5eae8de6f5445e06b99eb8b0927f9abb9031519d772969bd13a7a0fb43ec067&nbsp;&nbsp;&nbsp;</p><p>Service binary: 368769df7d319371073f33c29ad0097fbe48e805630cf961b6f00ab2ccddbb4c&nbsp;&nbsp;</p><p>new BLINDINGCAN: c60587964a93b650f3442589b05e9010a262b927d9b60065afd8091ada7799fe&nbsp;</p><p>C2s:&nbsp;&nbsp;</p><p>hxxp[://]166[.]88[.]11[.]10/upload/check.asp&nbsp;</p><p>hxxps[://]tronracing[.]com/upload/check.asp&nbsp;</p><p>hxxp[://]23[.]27[.]140[.]49/Onenote/index.asp&nbsp;</p><h2>Conclusion&nbsp;</h2><p>Kimsuky and Lazarus continue to sharpen their tools, showing that DPRK-linked actors aren’t just maintaining their arsenals, they’re reinventing them. These campaigns demonstrate a well-structured and multi-stage infection chain, leveraging obfuscated payloads and stealthy persistence mechanisms. From the initial stages to the final backdoors, each component is designed to evade detection, maintain access and provide extensive control over the compromised system. The use of custom encryption, dynamic API resolution and COM-based task registration/services exploitation highlights the groups’ continued evolution and technical sophistication. Monitoring for these indicators and behaviors is essential for early detection and mitigation of such threats.&nbsp;</p><p>By tracking every line of code and every new variant, we help surface the patterns that keep defenders one step ahead. Awareness, collaboration, and constant vigilance are what turn technical insights into real-world protection — and that’s where Gen Threat Labs and our global intelligence network can make a difference.&nbsp;</p><p>&nbsp;</p>

Technical walkthroughs of two DPRK intrusions: Kimsuky’s invoice-themed dropper to HttpTroy, and Lazarus’s Comebacker to BLINDINGCAN plus IOCs, TTPs, and mitigation steps 

DPRK’s Playbook: Kimsuky’s HttpTroy and Lazarus’s New BLINDINGCAN Variant 

<p>While conducting routine clipboard monitoring, our team uncovered a sophisticated attack chain masquerading as an official NVIDIA-related update. What initially appeared to be a hiring assessment challenge quickly revealed itself to be a cleverly disguised lure.&nbsp;</p><h2><strong>The deceptive journey&nbsp;</strong></h2><p>The attack begins with a prompt to complete a mock interview form, followed by a page instructing users to set up their camera. This page falsely claims there are issues with the user's webcam or microphone, adding a layer of urgency and credibility to the ruse.</p>

<p>A pop-up then urges users to “Request camera access,” which triggers a seemingly legitimate update command using an NVIDIA domain, to help the user fix his issues.</p>

<p>However, once copied, the command morphs into a malicious payload.&nbsp;</p>

<h2><strong>The attack chain&nbsp;</strong></h2><p>Executing the command initiates a multi-stage attack:&nbsp;</p><ul><li>A malicious archive is downloaded and automatically extracted.</li><li>The VBS script launches a Python environment embedded within the archive, posing as a legitimate driver update process. It displays verbose output to further trick the user.</li><li>The Python script itself is heavily obfuscated and aligns with known tactics used by the Lazarus APT group.&nbsp;</li></ul>

<p>Upon deobfuscation, the script reveals five major functionalities:&nbsp;</p><ol><li>Credential Theft: <em>WebBrowserPassView</em> is downloaded, decrypted and executed to extract browser-related credentials. Those credentials, together with system information, are exfiltrated to the command-and-control (C2) server.</li><li>Credential Theft: <em>MailPassView</em> is downloaded, decrypted and run to harvest email credentials, which are also sent to the C2.</li><li>Remote Access: An instance of <em>MeshAgent</em> is silently installed, providing the attackers with persistent remote control over the infected system.</li><li>Advanced Data Theft and Remote Access: A <em>PyInstaller </em>EXE enables file and secret extraction via FTP. Persistence is achieved through a scheduled task disguised as <em>ChromeUpdate.exe</em>, placed within the legitimate Chrome directory to avoid suspicion.</li><li>Cryptocurrency Targeting: Browser extensions and local folders related to cryptocurrencies are harvested and sent to the C2.&nbsp;</li></ol><h2><strong>Attribution and implications&nbsp;</strong></h2><p>The attack’s complexity and methodology, paired with the initial lure and code similarities throughout the Python scripts, strongly suggest Lazarus’ involvement. This campaign reflects the group’s evolving tactics under the <em>DeceptiveDevelopment </em>umbrella, combining psychological manipulation with technical sophistication to bypass defenses and exploit trust.&nbsp;</p><h2><strong>Indicators of Compromise (IoCs)&nbsp;</strong></h2><p>Assessment URL: hxxps[://]assessdome[.]com/invite/7e462f3c/8002565804&nbsp;</p><p>C2: metakenproxy[.]com:81&nbsp;</p><p>Initial VBS script (SHA256): 7013822c0a794712c5fe8f62c126e5992dca4a744882a039040569ae4ec1a868&nbsp;</p><p>Initial Python script (SHA256): 03ad194456951695eb4d4ceb40d9e52aaadbc9a4f4b8b1d020077115103e5359&nbsp;</p><p>WebBrowserPassView (SHA256): 36541fad68e79cdedb965b1afcdc45385646611aa72903ddbe9d4d064d7bffb9&nbsp;</p><p>MailPassView (SHA256): bc7bd27e94e24a301edb3d3e7fad982225ac59430fc476bda4e1459faa1c1647&nbsp;</p><p>MeshAgent (SHA256): 9757780860ec5637c412a8756f25c56f7d1d89358e447782164ba418def1c64e&nbsp;</p><p>ChromeUpdate.exe (SHA256): 00bef70cd031a830f2ee1ec4ce750947a9c8838995289ecbb253426cca53d046&nbsp;</p>

Alexandru-Cristian Bardaș

Posts by Alexandru-Cristian Bardaș

DPRK’s Playbook: Kimsuky’s HttpTroy and Lazarus’s New BLINDINGCAN Variant

Lazarus’ latest tactics: Deceptive development and ClickFix