Threat Research Team Lead

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

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

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

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

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

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

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

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

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

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

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

Who's Really Using Your Home Internet Connection?

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>Not every threat that matters is technically sophisticated, and that is also the case with GoFlateLoader, which is a rather simple loader written in Go, whose sole purpose is to decode and execute the payload in memory. What stands out the most is not what the loader does but rather what it does not do – it comes without anti-debugging, anti-VM, or sandbox-evasion checks, and also lacks API hashing or CFG obfuscation, the kind of tricks that loaders almost always come with. Instead, GoFlateLoader relies on one of the simplest yet still effective tricks to stay under the radar – it appends a massive PE overlay at the end of the file, deliberately inflating the binary's size (hence the name GoFlateLoader).&nbsp;Nevertheless, it is important to understand that malware does not necessarily need to be elegant or stealthy. In the end, the only thing that matters to attackers is whether it can successfully deliver the final payload. And if that is the only metric, GoFlateLoader appears to be working fairly well for its operators, as we continue to see it widely distributed across our user base. Since the beginning of April, we have protected more than 33,000 unique users from it, with the most affected countries including Brazil, India, Argentina, Mexico, Turkey and Spain.&nbsp;

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

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

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

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

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

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

GoFlateLoader: A Widespread Golang Loader Delivering Multiple Infostealers

Malware Researcher

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

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

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

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

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

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

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

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

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

Threat Research Team

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

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

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

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

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

<h2>Key points</h2><ul type="disc"><li data-list-item-id="e4b810198ac9891110da1771c3f2d0397">Gen Threat Labs has identified Remus, a new 64-bit infostealer we attribute to the infamous Lumma Stealer family – emerging in the wake of Lumma’s takedown and the doxxing of its alleged core members.</li><li data-list-item-id="ede8006689bbfaba0d6fa856478b3ea79">In this technical blog post, we detail the compelling evidence tying Remus to Lumma across multiple dimensions.</li><li data-list-item-id="ef3f0d531ccf019ab00b98a38b3c43c17">We also describe a previously undocumented Application-Bound Encryption bypass employed specifically by Remus and Lumma.</li><li data-list-item-id="ee0040d0e9463ac082d24fcdbfd7af133">The first Remus campaigns date back to February 2026, with the malware switching from Steam/Telegram dead drop resolvers to EtherHiding and employing new anti-analysis checks.&nbsp;</li></ul><h2>Introduction</h2>When the security industry talks about information stealers, Lumma Stealer, without a doubt, has become the notorious icon of this landscape. Not only could it count itself among the most sophisticated, technically advanced, and widespread stealers-as-a-service in the world, but it was also described in a variety of blog posts from basically everyone in the industry, including us.In this analysis, we describe a new variant of Lumma Stealer that we call Remus, a new x64 build which we suspected might occur in the foreseeable future after the doxxing of Lumma authors from late August to October 2025. And the future is here.Remus brings the same stealing arsenal to the table as we already know from Lumma, capable of stealing stored browser passwords, cookies, cryptocurrency, and much more. However, rather than revisiting Lumma's well-documented capabilities, we focus on the remarkable resemblance between Remus and Lumma, as well as the new techniques Remus introduces, including the use of EtherHiding to resolve C2s, replacing the traditional use of Steam and Telegram dead drop resolvers, and additional anti-analysis checks.&nbsp;The attribution of Remus to Lumma is many-fold and described thoroughly throughout the blog post. The main indicators, to name a few, are the use of the same string obfuscation technique, AntiVM checks, direct syscall/sysenter handling, indirect control flow obfuscation, and, most importantly, an almost identical approach to bypassing AppBound Encryption, which we’ve only seen used explicitly by Lumma to date.With that said, we can still see active Lumma campaigns all around the world, which makes Remus not a replacement, but a continuous evolution.<h2>Development timeline</h2>Before diving into the technical details, it is worth noting that during our hunting efforts, we came across several test samples, which even carry a <code>testbuild</code> label embedded directly in the binaries (see Figure 1). We are internally referencing these builds as Tenzor (based on encrypted strings). These test builds are already 64-bit and structurally very close to Remus, suggesting they represent a transitional step, or perhaps even a testing ground, between Lumma and Remus.

This also brings us to the name itself. Both Tenzor and Remus have left a string artifact in the same part of the code, referencing the <code># TENZOR LOG</code> and <code># REMUS LOG</code> strings. Since we have been tracking only test builds with the Tenzor string and Remus is the variant that is being actively distributed in live campaigns, we took the liberty of naming the new x64 Lumma variant after the latter string – Remus.

Notably, all the Tenzor samples carry a build date of September 16, 2025, in their stack-encrypted strings, which is months before the first Remus samples began appearing in the wild at the turn of January and February 2026. The build timing closely coincides with a period when Lumma Stealer suffered a major blow through a doxxing campaign that exposed alleged core members and severely disrupted its operations. This may suggest that some of Lumma’s authors split off, or that Lumma decided to rebrand under a new name in the midst of the doxxing fallout. Nevertheless, at Gen Threat Labs, we track both Remus and Tenzor as variants of Lumma Stealer. That said, the only samples bearing the Tenzor name are the ones that also carry the <code>testbuild</code> label. All others are identified as Remus.

<h2>Similarities between Remus and Lumma</h2>When we first started analyzing Remus, something felt familiar, and for good reason. Many of the techniques and design patterns we encountered in Remus closely mirrored those we had already seen in Lumma Stealer. The main difference is that Lumma was 32-bit while Remus is 64-bit, which naturally introduced some variations. Yet, when we looked past the 32-bit versus 64-bit differences and stripped away the obfuscation layers, what remained was strikingly familiar.In the following subsections, we highlight the most compelling evidence of a direct connection between Remus and Lumma. Note that this is not an exhaustive list. The overlaps are far more numerous, but we selected just a few that we believe speak for themselves.<h3>Forgotten dead drop resolver and string obfuscation&nbsp;</h3>For a malware researcher, strings are among the most valuable resources during reverse engineering, and in the case of attributing Remus to Lumma, they proved no different.&nbsp;Both Remus and Lumma use a virtually identical mechanism for string obfuscation. The encrypted string is first assembled on the stack using a series of various forms of the <code>mov</code> instruction, followed by a decryption loop that transforms the data byte by byte. The transformation is often further obscured by MBA (Mixed Boolean-Arithmetic) obfuscation and is either inlined or placed in a standalone function. In any case, each string is protected using a unique transformation, making it very difficult to build a universal static decryptor. As a result, emulating the decryption loops is likely the most effective approach for dealing with encrypted strings in both Remus and Lumma.&nbsp;

Notably, the decryption loops are often preceded by several <code>nop</code> instructions (Lumma) or a single <code>nop</code> instruction encoded using multiple bytes (Remus), likely inserted as padding during a custom compilation pass. We are aware that the compilers themselves can emit <code>nop</code> paddings. Still, in both Remus and Lumma, these sequences go beyond the typical, making it yet another indicator linking the two together.After decrypting all strings in both Remus and Lumma and comparing them side by side, we found roughly 100 completely identical strings. Most of these, however, could be attributed to virtually any information stealer. The more telling discovery came when we looked at the strings that were not identical – most of them turned out to be semantically the same, just slightly refactored. This alone would already be a strong indicator.&nbsp;However, what ultimately confirmed our theory was shifting the string comparison from Lumma vs. Remus to Lumma vs. Tenzor, as Tenzor effectively acts as a bridge between the other two – many of the strings we had previously matched only semantically between Remus and Lumma were still present in Tenzor in their original, unmodified form, directly matching Lumma’s. At the same time, Tenzor also contained very specific strings found exclusively in Remus but not in Lumma, such as <code>B9%????4rnO/@NQe?Nx*</code>, used as a wildcard mask in the ABE-bypass (which we will discuss later), firmly linking it to both sides.

On top of that, among the decrypted Tenzor strings, we also found a Steam dead drop resolver <code>hxxps[://]steamcommunity[.]com/profiles/76561199861614181</code>, which turned out to be the exact same resolver present in multiple confirmed Lumma samples, namely:<ul><li data-list-item-id="e7c7ac2aae55af0df2c596fe8463f5a97">002f714f93bed53f165129a820c2d5b72227f1cafac43be19e5e223ce219a5e1 (Lumma)</li><li data-list-item-id="ea0c5ca160e92372f6b8b681415725a00">066c4ab954fc1270ee62c0d7c582c4c691e58e0ffef0c654bc204a46e440d16d (Lumma)</li><li data-list-item-id="e2eae0f6619eb5e4ffb9d1e84b3e057d6">0683f353cf3e101f721f1658e2a554ff7888ff9f2c32e23ceb3d23876864a264 (Lumma)</li><li data-list-item-id="efe40dcac54b87ce148882d49b97105a6">0580ebf601989457f0708799b431fd4d9f5e59d98838282d72936099aa6636da (Tenzor)</li></ul>

<h3>Application-bound encryption bypass</h3>Another very strong indicator tying Remus and Lumma together is the way they bypass Application-Bound Encryption (ABE), as both employ a highly specific technique that, until now, we have only observed and have been actively tracking in Lumma Stealer.The technique involves injecting a very short shellcode (less than 100 bytes) into the browser process to decrypt the <code>v20_master_key</code>. However, unlike most techniques relying on injection, Lumma as well as Remus do not use this shellcode to call <code>IElevator::Decrypt</code> with the key obtained from the <code>Local State</code> file (the <code>[“os_crypt”][“app_bound_encrypted_key”]</code> JSON field). Instead, it locates the <code>v20_master_key</code> directly in the browser’s process memory, where it is stored in an encrypted form protected by <code>CryptProtectMemory</code> with the <code>CRYPTPROTECTMEMORY_SAME_PROCESS</code> flag. It then calls the complementary <code>CryptUnprotectMemory</code> with the same flag from within the browser’s context to decrypt it.It is important to note that this in-memory encrypted form of the <code>v20_master_key</code> is protected with a different key than the one used to protect the key on disk – it is a separate protection layer that Chromium browsers use to safeguard the key while it resides in memory at runtime. However, since the key is protected with the <code>CRYPTPROTECTMEMORY_SAME_PROCESS</code> flag, decryption can only be performed by the same process that encrypted it, which is precisely why injection into the browser process is necessary in this specific bypass. For the curious, we discussed this topic in more detail in our recent&nbsp;<a href="https://www.gendigital.com/blog/insights/research/voidstealer-abe-bypass" target="_blank">VoidStealer blog post</a>.From this point on, we will walk through the ABE bypass as implemented in Remus, noting any differences from Lumma’s implementation where applicable. Where no differences are explicitly mentioned, the two implementations work either identically or very similarly.&nbsp;So how does Remus find the protected <code>v20_master_key</code>? It begins by walking the browser’s PEB module list, looking for two modules in a single pass: <code>dpapi.dll</code> and the browser DLL (e.g., <code>chrome.dll</code>).&nbsp;When <code>dpapi.dll</code> is found, Remus manually parses its PE export directory, reading the DOS header, PE header, and export table through repeated <code>NtReadVirtualMemory</code> syscalls. For each exported function name, it computes a seeded CRC32 hash and compares it against the pre-computed hash&nbsp;corresponding to&nbsp;<code>CryptUnprotectMemory</code>. Essentially, a typical API-hashing, but in a remote process. Once a match is found, it&nbsp;resolves the function’s RVA to obtain its absolute address in the browser’s address space.When the browser DLL is found during the same pass, Remus decrypts a hex pattern <code>488d058bcc8d02488901488b024889415b488d41</code>, which was encrypted by the same string obfuscation technique described earlier and stores the value <code>0xEFF87</code> (which corresponds to <code>11101111111110000111</code> in binary) into a variable we labeled as <code>wildcard_mask</code> (see&nbsp;Figure&nbsp;8).&nbsp;

The hex pattern corresponds to a sequence of opcodes that Remus searches for within the browser DLL (e.g., <code>chrome.dll</code>), while the wildcard mask, read from the least significant bit, indicates which bytes must match exactly and which should be treated as wildcards. Lumma does the same but takes a different approach to wildcarding. Instead of a hex mask, it uses a 20-character string <code>B9%????4rnO/@NQe?Nx*</code>, where ? denotes a wildcard and any other character means the byte must match exactly. Notably, this exact string also appears in Tenzor builds, which directly ties Tenzor to the Lumma codebase.

Looking into <code>chrome.dll</code>, we can see that the pattern is designed to locate a <code>LEA</code> (Load Effective Address) instruction that loads a 32-bit displacement pointing to the <code>os_crypt_async::Encryptor</code> virtual function table (vftable). The reason for specifically targeting the <code>os_crypt_async::Encryptor</code> class is that it holds the protected <code>v20_master_key</code>.

Once the pattern is found, Remus reads the 32-bit displacement at <code>pattern_address + 3</code> and computes the absolute address of the <code>os_crypt_async::Encryptor</code> vftable using the formula: <code>target_addr = pattern_addr + disp32 + 7</code> (since the <code>LEA</code> instruction is 7 bytes long and the displacement is relative to the next instruction).&nbsp;It then scans the browser’s memory for this 8-byte vftable pointer by enumerating committed, readable memory regions via <code>NtQueryVirtualMemory</code> and reading each one into a local buffer via <code>NtReadVirtualMemory</code>. Wherever a match is found, it marks the start of an <code>os_crypt_async::Encryptor</code> instance in memory, as the vftable pointer sits at the very beginning of the object. From there, extracting the protected <code>v20_master_key</code> is just a matter of walking the structure at known offsets.&nbsp;

After resolving all the necessary&nbsp;addresses, Remus allocates&nbsp;a buffer&nbsp;in the browser process via <code>NtAllocateVirtualMemory</code>, which serves as both the copy destination and the in-place decryption target.&nbsp;It then constructs&nbsp;a 51-byte&nbsp;shellcode (shown in Figure 12), allocates a second buffer with the <code>PAGE_EXECUTE_READWRITE</code> protection constant for the shellcode itself, and&nbsp;writes it into the browser process via <code>NtWriteVirtualMemory</code>. The shellcode is then executed via <code>NtCreateThreadEx</code>, and Remus waits for its completion using <code>NtWaitForSingleObject</code>.&nbsp;Once the <code>v20_master_key</code> is decrypted, Remus simply reads it from the buffer it previously allocated (and, therefore, already knows its&nbsp;address) via <code>NtReadVirtualMemory</code>.

The injected shellcode differs slightly between Remus and Lumma, but both fundamentally do the same thing – copy the protected <code>v20_master_key</code> into a pre-allocated buffer and jump to <code>CryptUnprotectMemory</code> to decrypt it in place. Both construct the shellcode on the stack, patching in the resolved addresses before writing it into the browser process. The only real difference is that Remus produces a more compact variant (51 vs 62 bytes) by reusing registers. Concrete examples of the injected shellcodes are shown in Figure 13 (Remus) and Figure 14 (Lumma).

Another detail linking the two is that when no browser process is already running, or when injection into an existing one fails, both Remus and Lumma spawn a new one on a separate, hidden desktop, preventing any visible windows from appearing on the user’s screen. They first attempt to open an existing desktop using <code>OpenDesktopW</code> and fall back to creating a new one via <code>CreateDesktopW</code> if it does not yet exist. A new browser instance is then launched via <code>CreateProcessW</code> with <code>STARTUPINFOW.lpDesktop</code> set to this desktop. The only difference is that Lumma uses a hardcoded desktop name&nbsp;<code>ChromiumDev</code> (or previously also <code>ChromeBuildTools</code>), while Remus generates a random 16-character alphanumeric string using a Mersenne Twister PRNG, a natural evolution rather than a change in approach.Furthermore, both Remus and Lumma also employ SYSTEM token impersonation as an alternative method to bypass ABE. Interestingly, the two families differ in which method they prefer: Remus attempts SYSTEM elevation first and falls back to shellcode injection, while Lumma tries injection first and resorts to SYSTEM elevation only if it fails. That said, SYSTEM token impersonation for ABE bypass is a well-known technique widely used across many stealer families. What, however, is highly distinctive is the injection-based bypass described above, which to our knowledge has only been used by Lumma and now also Remus.&nbsp;<h3>AntiVM CPUID checks</h3>Another identical technique shared by Remus and Lumma is their anti-VM check based on the&nbsp;<code>cpuid</code>&nbsp;instruction with&nbsp;<code>EAX</code>&nbsp;set to&nbsp;<code>0x40000000</code> – the hypervisor vendor identification leaf. When executed inside a virtual machine, the processor returns the hypervisor’s vendor signature in <code>EBX:ECX:EDX</code>. Both Remus and Lumma specifically check the&nbsp;<code>ECX</code>&nbsp;portion (4 bytes) against five known hypervisor signatures: KVM (<code>KVMKVMKVM</code>), QEMU/TCG (<code>TCGTCGTCGTCG</code>), VMware (<code>VMwareVMware</code>), VirtualBox (<code>VBoxVBoxVBox</code>), and Xen (<code>XenVMMXenVMM</code>). Notably, both check the same substrings of hypervisor names in the same order, and the checked substrings are obfuscated.&nbsp;The decompiled code performing the check can be seen in Figure 15 (Remus) and Figure 16 (Lumma). For clarity, we omitted the decryption loops from the Lumma listing, but they are the same decryption loops we described earlier.

<h3>Crypter check</h3>When executed without a protective layer, both Remus and Lumma display a warning dialog before proceeding with their malicious payloads (see Figures 17 and 18). The purpose of this mechanism is twofold: to discourage distributors from spreading the raw, unprotected executables, which are more easily detected by security products, and to prevent less skilled affiliates from accidentally infecting their own machines.

This type of check is relatively rare. So far, only a handful of families have been observed employing it – namely, <a href="https://outpost24.com/blog/lummac2-anti-sandbox-technique-trigonometry-human-detection/">Lumma</a>, <a href="https://research.checkpoint.com/2025/rhadamanthys-0-9-x-walk-through-the-updates/" target="_blank">Rhadamanthys</a>, Remus, and <a href="https://www.gendigital.com/blog/insights/research/defeating-aurastealer-obfuscation">more recently also AuraStealer</a>, albeit AuraStealer takes a slightly different approach (rather than simply displaying a warning and waiting for the user to click <code>Yes</code> or <code>OK</code>, it additionally requires the user to enter a randomly generated code shown in the dialog).&nbsp;However, despite the surface-level similarity, the underlying implementations differ. <a href="https://research.checkpoint.com/2025/rhadamanthys-0-9-x-walk-through-the-updates/">Rhadamanthys displays its warning dialog</a> using <code>MessageBoxW</code>, whereas both Remus and Lumma invoke it through a direct <code>NtRaiseHardError</code> syscall.&nbsp;<h3>Direct syscalls/sysenters</h3>Both Remus and Lumma make use of direct syscalls/sysenters. While their implementations differ in some details, mostly as a natural consequence of the 32-bit vs 64-bit architecture, the overall design is strikingly similar.One of the first things both do upon execution is that they enumerate all <code>Nt</code>-prefixed exports from <code>ntdll.dll</code> and build a lookup table mapping their name hashes to the corresponding Syscall Service Numbers (SSNs). The process is the same in both cases: they walk the <code>ntdll.dll</code> export directory, hash each matching export name, and scan the first 32&nbsp;bytes of each function’s prologue to extract the SSN from the <code>mov eax, &lt;SSN&gt;</code> instruction. The extracted SSN is then stored alongside the export’s name hash in a hash-to-SSN lookup table.&nbsp;

To invoke a specific syscall/sysenter, both perform a linear scan of the lookup table until they find an entry whose hash matches the requested function. The SSN from the matching entry is then passed to a central dispatcher, which we refer to as <code>lumma_sysenter</code> and <code>remus_syscall</code>. The calling conventions of those dispatcher functions are nearly identical: the first argument is the SSN, followed by the argument count and the variadic arguments themselves. The only difference is that Lumma expresses the argument count in bytes (<code>number_of_arguments × 4</code>), whereas Remus passes the actual count directly.

The dispatchers themselves look different at first glance, but this is mostly a consequence of ABI differences between 32-bit and 64-bit code. The core logic is the same: arrange the syscall/sysenter arguments and invoke the kernel. Remus does so directly via the&nbsp;<code>syscall</code>&nbsp;instruction. Lumma, on the other hand, resolves a dispatcher address during the hash-to-SSN initialization through a fallback chain: it first attempts to resolve the <code>Wow64Transition</code> export from <code>ntdll.dll</code> using API-hashing, then tries the <code>TEB-&gt;WOW32Reserved</code>, and finally, if that value is <code>NULL</code>, falls back to a hardcoded <code>sysenter</code>stub. Each of these fallback mechanisms ultimately achieves the same outcome – a transition from user-mode 32-bit code into the kernel.

Separately from the 32-bit <code>sysenter</code> dispatch, Lumma also employs Heaven’s Gate, a&nbsp;technique that allows 64-bit code to be executed from within a 32-bit process on a 64-bit operating system. Lumma uses it specifically when interacting with 64-bit browsers (during the ABE bypass) to call native 64-bit <code>ntdll.dll</code> functions that have no NtWow64 equivalents – namely <code>NtCreateThreadEx</code> to start the injected thread, <code>NtFreeVirtualMemory</code> for clean up, and <code>NtQueryVirtualMemory</code> to query the browser’s memory layout. The remaining cross-process operations, such as memory reads, memory allocations, and shellcode writes, are handled through standard NtWow64 sysenter calls (<code>NtWow64ReadVirtualMemory64</code>, <code>NtWow64AllocateVirtualMemory64</code>, and <code>NtWow64WriteVirtualMemory64</code>). Since Heaven’s Gate is inherently a WoW64 technique applicable only to 32-bit processes running on 64-bit systems, and Remus is a 64-bit binary, it has no equivalent. Remus simply uses direct syscalls for all these operations.<h3>Shared code patterns</h3>Another striking similarity is the layout of many functions, which shows a remarkable resemblance and, in some cases, is nearly identical (if we disregard the obfuscation and differences caused by 32-bit vs 64-bit code). While there are many such functions, we highlight two concrete examples. The first is the heap allocation wrapper, which Remus and Lumma implement in a virtually identical manner, as illustrated in Figures&nbsp;24 and 25. Furthermore, the same holds for other memory helpers, such as the reallocation and deallocation wrappers.&nbsp;

The second example is the clipboard-stealing routine (Figures 26 and 27). Both implementations follow the same sequence: they open the clipboard, retrieve its contents, convert it from UTF-16 to UTF-8, decrypt the output filename <code>Clipboard.txt</code> using a stack-based decryption loop, and finally append the result to the exfiltration archive. The only difference is that Remus wraps each API call in a thin stub, which, however, might just be a result of different compiler optimization.

Notably, although the vast majority of API calls are obfuscated through API hashing, the clipboard functions <code>OpenClipboard</code>, <code>GetClipboardData</code>, and <code>CloseClipboard</code> are among the very few unobfuscated imports in both binaries.Equally notable is the entry point structure. Both Remus and Lumma begin executing their malicious logic directly from the PE entry point (the&nbsp;<code>start</code>&nbsp;function), with no runtime initialization whatsoever. In a typical C/C++ binary, the entry point calls the CRT startup routine, which sets up the heap, initializes global variables, registers exception handlers, processes the command line, and invokes static constructors before eventually calling&nbsp;<code>main</code>. However, neither Remus nor Lumma follows this pattern. Instead, both jump directly from the entry point into their core logic: resolving modules and APIs, performing anti-analysis checks, initializing C2 communication, and exfiltrating data, ending with a direct call to <code>ExitProcess(0)</code>. This suggests both were compiled without linking to the standard C runtime, likely a deliberate choice to minimize the binary size and reduce unnecessary dependencies.The structural similarities extend further. Both binaries share the same section layout, with each section serving an identical semantic role. Even the C2 configuration follows the same pattern: in both cases, the encrypted blob, key, and nonce are statically embedded in the <code>.rdata</code> section and decrypted at runtime using ChaCha20.To summarize, we strongly believe the degree of similarity between the two codebases is too deliberate to be a coincidence, pointing to a single, shared origin.<h3>Indirect control flow obfuscation</h3>Finally, both Remus and Lumma make use of control flow obfuscation by replacing direct jumps with indirect ones, where the target is read from an offset stored in the <code>.data</code> section. As shown in Figure 28, this technique takes several forms. In its simplest case, the target address is resolved by a single pointer dereference (highlighted in yellow). In more complex cases, it extends to a full jump table, where a computed index selects among several target addresses stored consecutively in the <code>.data</code> section (highlighted in orange). Lastly, in some cases, the target address is first loaded into a register (highlighted in blue) and then jumped to via a register-based indirect jump (highlighted in pink).

Although functionally equivalent to normal jumps, the indirection&nbsp;complicates the control flow analysis in two ways. First,&nbsp;some of the indirect jump targets cannot be statically determined (for example, when the register holding the target address can be set in multiple different preceding basic blocks, each potentially loading a different target), leaving the control flow graph incomplete as the connections between basic blocks are lost. Second, some of these orphaned blocks end up being misidentified as standalone functions, further disrupting function boundary detection and leading to a broken, incomplete decompiler output. Notably, Figure 28 also provides another glimpse of the <code>nop</code> paddings discussed earlier.

Both binaries contain hundreds of these indirect dispatch points, and once again, if we set aside the minor differences arising from the 32-bit vs. 64-bit differences, the obfuscation is virtually identical. Although removing the obfuscation entirely requires more effort, the IDA’s decompiler output can be significantly improved by marking the referenced offsets as constants. For those interested, we covered this trick in our <a href="https://www.gendigital.com/blog/insights/research/defeating-aurastealer-obfuscation">AuraStealer blog post</a>, along with other approaches for tackling control flow obfuscation.<h2>What’s new in Remus</h2>Having covered the key similarities between Remus and Lumma, let’s now focus on what is new in Remus. The core architecture is clearly inherited from Lumma. Still, several changes go beyond a straightforward 32-bit-to-64-bit port. These range from refactored strings and more thorough device fingerprinting to a different exfiltration archive format, a switch from FNV-1a to CRC32 for API hashing, and an overhauled C2 communication that has been restructured to deliver dynamic configurations incrementally rather than in a single large blob. &nbsp;The most significant changes, however, are the replacement of traditional Steam/Telegram dead drop resolvers with blockchain-based C2 resolution using EtherHiding, a persistence technique allowing attackers to store malware-related data (such as domains, payloads, encryption keys, among others) on blockchain as a smart contract, and the introduction of additional anti-analysis checks.<h3>Dead drop resolvers via EtherHiding</h3>Both Remus and Lumma employ dead drop resolvers, a mechanism in which the malware does not contact its C2 server directly but instead retrieves the C2 address at runtime from an intermediary hosted on a legitimate platform. This makes the infrastructure significantly more resilient, as if a C2 domain is taken down, the dead drop can be adjusted to point to a new server without the need to distribute an updated binary. Moreover, the dead drop URLs typically reside on well-known, high-reputation services, making them more difficult for security vendors to block without collateral damage.Where the two differ is in the choice of platform. Lumma relies on Steam profiles and Telegram channels, typically encoding C2 URLs using ROT-15, while Remus goes a step further, replacing these with Ethereum smart contracts. At runtime, it sends an <code>eth_call</code> JSON-RPC request to a hardcoded contract address via a public RPC endpoint and extracts the C2 URL from the hex-encoded response. Because blockchain data is decentralized and immutable, there is no platform operator to report abuse to, making the dead-drop effectively immune to takedowns.Just like Lumma’s Steam profile URL, Remus stores the smart contract address separately from the static C2 configuration, as a stack-encrypted string. Notably, after the JSON response is retrieved, the C2 address is, at least for now, merely hex-encoded in the <code>result</code> field (no other obfuscation, such as ROT-15 in case of Lumma, is applied).

<h3>Additional anti-analysis checks</h3>Remus introduces two additional anti-analysis mechanisms, both evaluated early during startup (before connecting to its C2). If either check fails, the malware silently terminates via <code>ExitProcess(0)</code>.&nbsp;The first check targets sandbox and analysis tool DLLs. Remus walks the PEB's <code>InLoadOrderModuleList</code>, hashes the name of every loaded module using a CRC32 variant with a custom initialization constant, and compares the result against 11 pre-computed hashes stored in its <code>.rdata</code> section, each corresponding to a module commonly injected by analysis environments, including Avast sandbox (<code>snxhk.dll</code>), Sandboxie (<code>sbiedll.dll</code>), Comodo sandbox (<code>cmdvrt32.dll</code>, <code>cmdvrt64.dll</code>), and several others.&nbsp;

If no sandbox DLLs are detected, Remus proceeds by expanding the path&nbsp;<code>%UserProfile%\Documents\Outlook Files</code>, enumerating all&nbsp;<code>*.pst</code>&nbsp;files in that directory, and checking for the presence of&nbsp;<code>honey@pot.com.pst</code>, which would be treated as a sandbox environment.&nbsp;<h2>Summary</h2>In this analysis, we described our findings regarding a new x64 variant of Lumma Stealer, which we call Remus. We focused on various similarities and differences between Lumma and Remus, thoroughly describing the technical aspects of both. We also provided a timeline, referencing the very first test builds of Remus (historically called Tenzor), which correlate with the extensive doxxing of Lumma authors from late August to October 2025.The key aspects that allowed us to attribute Remus as a new x64 version of Lumma are the use of the same string obfuscation technique, AntiVM checks, direct syscall/sysenter handling, indirect control flow obfuscation, and, most importantly, an almost identical approach to bypassing AppBound Encryption, which we’ve only seen used by Lumma to date.Remus, however, is not merely an x64 port of Lumma. It also introduces a couple of novel techniques in how it operates, including the use of EtherHiding instead of a traditional approach of using Steam/Telegram as dead drop resolvers, as well as additional anti-analysis checks to evade a wider range of security vendors.<h2>Indicators of Compromise (IoCs)</h2>A complete IoC list is available on our <a href="https://github.com/gendigitalinc/ioc/tree/master/Remus">GitHub</a>.<h3>C2 domains</h3>hxxp[://]217[.]156[.]122[.]12:80 hxxp[://]217[.]156[.]122[.]57:80 hxxp[://]217[.]156[.]122[.]75:1378 hxxp[://]45[.]151[.]106[.]110:80 hxxp[://]80[.]97[.]160[.]155:80 hxxp[://]86[.]107[.]168[.]103:80 hxxp[://]94[.]231[.]205[.]229:28313 hxxp[://]adveryx[.]biz:6573 hxxp[://]backbou[.]biz:5902 hxxp[://]baxe[.]pics hxxp[://]baxe[.]pics:48261 hxxp[://]borscer[.]biz:9592 hxxp[://]buccstanor[.]pics hxxp[://]buccstanor[.]pics:28313 hxxp[://]buccstanor[.]pics:48261 hxxp[://]chalx[.]live:5902 hxxp[://]chromap[.]biz:4219 hxxp[://]coox[.]live:28313 hxxp[://]drymoge[.]biz:4192 hxxp[://]forestoaker[.]com:6290 hxxp[://]gluckcreek[.]online:48261 hxxp[://]intem[.]lat:9592 hxxp[://]interxo[.]biz:7481 hxxp[://]josegza[.]biz:8521 hxxp[://]krondez[.]com:28982 hxxp[://]lazzo[.]bet:3989 hxxp[://]managew[.]biz:5902 hxxp[://]navelum[.]biz:3201 hxxp[://]nitroca[.]biz:6782 hxxp[://]outcrol[.]biz:4895 hxxp[://]padaz[.]pics:4219 hxxp[://]parky[.]pics:3989 hxxp[://]prickaz[.]biz:2039 hxxp[://]remnane[.]biz:5692 hxxp[://]ropea[.]top:28313 hxxp[://]siltsoh[.]biz:7481 hxxp[://]texakgi[.]cloud:3849 hxxp[://]vinte[.]online hxxp[://]vinte[.]online:28313 hxxp[://]woodena[.]biz:7821 hxxp[://]zadno[.]run:4219 hxxps[://]cheekiez[.]biz hxxps[://]nobleckly[.]biz&nbsp;<h3>Remus SHA-256 (Non-exhaustive)</h3>0a8f734f10400f7ae8fef591147e78dab6350089683be84c1cb6c82113cb1319 25e74a76f2f3601abcb20fd743a7e3cf3befd5a3838c7501af5d87d293233809 4428c3ffe2532f162f31d7573bbc1cca2299195421da3d8e8a3e535e9fc42b08 484e3ab5d425a97819f01dcc330e005dc444c51625bfdcd7ea9a3954018d1fc9 64db10e76b46be8db36e02993d36559bc3f86606c9ea955731872b716c8f0c69 788b56e9be2f1dd6a977dce0265f293ab42d3e8ffb287ab584e169fbf115da1f 8653d7158486aa10fc0078c3ca9318cd7ace05d4b3e6f3b1fb84ffb7a6a339ec a4f111e5425690fcd384c62ecb5b57b0f645925572af3541748e01d810cd2b40 ab2e47720388fa201e242552f8d8b82363c6c52f6c63fa3fec9dce027cb12e77 bc11d036fe59abb3915f736307c56d2fd43e8127e46c31f926eeda864f4d66dc c3f7cea80dbafaa90a88b28a6dfb1227caaf5c2a29f0ce06bf663d6ed2cfc079&nbsp;<h3>Tenzor SHA-256&nbsp;</h3>0580ebf601989457f0708799b431fd4d9f5e59d98838282d72936099aa6636da 7a25253e6d8d9ccf62a67f8014cacb301daf9e40f1b68ecc7f354d6896d16960 cab7855ccfca19a06eea76e0e170f592dcc95906ecfa5436f5a11947e04e63d5 dfbeab30d14df9104a95de83ab4690308c653eb0c3706554687c45a77adc1385

The Lumma Rebrand We’ve Been Waiting For?

Remus: Unmasking The 64-bit Variant of the Infamous Lumma Stealer

Sr Threat Analysis Engineer

Junior Threat Analyst Intern

Welcome to the era of VibeScams, where creating a phishing site takes just a few clicks. Using simple prompts, scammers can create nearly any type of website almost instantly, without any prior knowledge. Some web-building platforms recreate an entire site from a screenshot provided by the scammer – from a home page to a shopping cart. They typically also allow perfect language localization, letting scammers instantly clone a legitimate site’s look and feel in multiple languages.&nbsp;This new wave of AI-driven web building tools has dramatically lowered the barrier to entry for cybercriminals, democratizing access to sophisticated site cloning and scam operations. No longer limited to those with knowledge of HTML, CSS, and JavaScript, these platforms allow anyone with an internet connection to replicate trusted brands, banking portals, or exchange interfaces with alarming fidelity. The ease and speed of this process not only accelerate the proliferation of fraudulent websites but also makes it increasingly difficult for users to distinguish between genuine and malicious websites.&nbsp;&nbsp;<h2>A new kind of phishing — VibeScams&nbsp;</h2>We call them VibeScams because the trick isn’t in the code alone — it’s in the feeling. These pages pass the “vibe check”: the right colors, spacing, logo placement, and tiny footer links people trust at a glance. Because AI web builders can recreate that look from a single prompt or screenshot, attackers don’t need coding skills — they only need to get the vibe right. That’s enough to trick people into handing over credentials or cash, and it’s why this wave of scams spreads so fast.&nbsp;

As part of our research for this article, we contacted the web builders which attackers leverage more prevalently, according to our detections. We would like to share our appreciation for those companies, including Lovable, Elementor, Flazio, Softr, Webflow, WebWave, among others, for their fast reactions, quick banning of the websites, and overall cooperation. We would like to encourage anyone who stumbles upon suspicious or malicious websites to also report them to the specific providers. It matters and makes a difference.&nbsp;<h2>VibeScams as an Evolution of Phishing&nbsp;</h2>AI-powered web builders make it easy for anyone—even without coding or design skills—to create professional-looking websites quickly and cheaply. By automating templates, layouts, and even content, these tools lower barriers and speed up development, but they also give attackers the same advantages, enabling scams and phishing campaigns with minimal effort. The process of using such a web builder is so intuitive and user-friendly, it’s familiar to almost everyone. Individuals are typically presented with a simple screen containing a textbox, prompting users to immediately fill in a few lines of instructions to build their dream website. Below, you can find an example of a prompt screen from Lovable, one of the popular AI web building platforms:&nbsp;

AI chatbots always have been constrained in the space what the chatbot allows the user to prompt for. These security mechanisms, which are called “guardrails,” serve as a control system to detect malicious, harmful, or otherwise inappropriate prompts and block the associated responses. However, as we will see in the below examples of impersonating reputable brands, these mechanisms are frequently insufficient.&nbsp;As is typical with phishing and scams, attackers tend to focus on brands, topics or products that are already popular. Typical scam sites include cryptocurrency exchanges, investment portals, banking sites, social media platforms, delivery services, shopping sites and much more.&nbsp;Below, you can find a couple of examples of scam and phishing sites we observed hosted on many of these platforms, all AI generated. Other examples we discovered included MetaMask, DHL and AT&amp;T, among many others.&nbsp;&nbsp;

From cryptocurrency exchanges to fake e-shops and login pages, we’ve also seen other typical types of scams&nbsp; – such as tech-support scams. As can be seen below, in this case, it was recreated using an AI web builder and even localized into German. The attacker likely just uploaded a screenshot of a traditional scam page and asked the AI to rebuild and host it, making the whole process effortless.&nbsp;

While talking about brand impersonation, which is an attempt to mimic the design, logos, content, and URL of a well-known and respected brand, we’ve observed multitude of additional cases using AI web builders. This way the user is easily tricked into believing the website they visited is the official website of the brand. A few examples of brand impersonation of well-known crypto exchange platforms can be found below. Note that none of these are legitimate websites of known cryptocurrency exchanges or login sites for that matter, all of them are either phishing or otherwise scam websites:&nbsp;<ul><li>auth-binance.webflow[.]io</li><li>binance-cdn-auth.webflow[.]io</li><li>coinbase-wallet-verify.replit[.]app</li><li>coinbase-wallet.squarespace[.]com</li><li>auth-coinbase-login.typedream[.]app</li><li>chrome-coinbase-extension.typedream[.]app</li><li>wallet-coinbase.typedream[.]app</li><li>microsoft-teams-login.elementor[.]cloud</li><li>microsoft-outlookserver.odoo[.]com</li><li>cz-microsoft.webflow[.]io</li><li>updateaccount-microsoft.webflow[.]io&nbsp;</li></ul>

Typosquatting is very typical as well. This means the attackers are choosing similarly looking names, with one or more changed characters in the URL:&nbsp;<ul><li>app—trrezor-wallet.webflow[.]io</li><li>app-atomiic-wallet.webflow[.]io</li><li>coiinbase-com-wallet.typedream[.]app</li><li>connect-metamesk-wallet.typedream[.]app&nbsp;</li></ul>A broader list of IoCs can be found in our <a href="https://github.com/avast/ioc/tree/master/VibeScams" target="_blank" rel="noreferrer noopener">GitHub</a>.&nbsp;<h2>Scam Types – Breakdown&nbsp;</h2>Nearly half of the detected websites were traditional phishing pages impersonating familiar login portals like Microsoft, Gmail, or Amazon. About a quarter targeted the cryptocurrency space—either by mimicking popular exchanges such as Coinbase, Binance, and MetaMask, or by promoting cryptocurrency-based investment scams. The remainder included more general scams and other malicious pages.&nbsp;

<h2>Our Testing&nbsp;</h2>During our research, we took this opportunity to try a few web builders ourselves, to see how easy it actually is to create a website resembling known brands. We also used only free versions of the web builders to see what the baseline looks like.&nbsp;You can see our results in a few examples below:&nbsp;

In our opinion, the AI generated testing websites look fairly convincing and they took no money and almost zero effort to create. That’s because we didn’t want to kill the vibe, so we also created all the testing prompts using AI and simply copy and pasted them into the AI web builders. In most cases, we didn’t need any further prompting or tweaking.&nbsp;Note that what we generated here are only the designs. For phishing and scam sites to work properly, there also needs to be some functionality provided, for example credentials exfiltration. Additional functionality like this is commonly offered in paid subscriptions of the web builders, or the attacker can use once again other LLMs to implement this directly into the code because in a lot of cases they can download the design locally and continue the work from there.&nbsp;This demonstration directly showed us the core of why the attackers are using this – they don’t need to understand web at all to create highly believable websites for free (we only used free versions of the web builders).&nbsp;<h2>Pricing models&nbsp;</h2>In general, we consider these services to be cheap since there is quite a bit of competition and the user/attacker has plenty of choices to try, even for free. The fact that the attacker requires almost no skill to create a scam or phishing website highly outweighs the costs here.&nbsp;The range of paid offers is vast, spanning from $0.5 per month to $500 per month, with a variety of free tiers and/or trial periods. Some of the providers also offer lifetime deals for $249 or $599.&nbsp;Many of these subscriptions use a relatively common credits-based model where the customer receives credits which they can use for the AI prompts. Additional aspects, since many of the web builders also offer hosting for the generated webpages, are the website limit, subdomain name selection and the ability to change the subdomain name in the future.&nbsp;Finally, some of the web builders also offer APIs that can be used for streamlining the webpage creation, making the whole process more automatic.&nbsp;<h2>The Impact&nbsp;</h2>It’s important for defenders and analysts to recognize just how accessible and varied these platforms have become. Because the barrier to entry is so low and payment models are flexible, we’re seeing a rapid growth in the number of AI-generated scam and phishing websites. This massive availability means the attackers can quickly spin up new designs and relaunch new scams almost instantly.&nbsp;In total, we were investigating 40 different AI web builders, 12 of which we directly contacted due to a high volume of identified malicious websites. In total, from the beginning of 2025 to the end of August, we blocked approximately 140,000 different AI-generated websites during this time, which makes roughly 580 new malicious generated websites every day, on average.&nbsp;Since January 2025 alone, we protected nearly 190,000 our users, with the U.S., France, Brazil, Germany, and Japan among the most affected. This shows that misuse of AI web builders is a world-wide problem, and we can only expect the threat landscape to grow.&nbsp;

<h2>Conclusion&nbsp;</h2>During our research, we investigated various AI web builders which are being misused by attackers for performing what we call VibeScams – a creation of scam or phishing pages without the need of any knowledge of coding or web creation.&nbsp;AI web builders provide support for individuals who are not technically invested in creating websites, where a simple prompt can suffice for building a new website from scratch. This results in a double-edged sword where it is very useful for both regular users as well as for attackers, which is rather typical with other various AI tools, too.&nbsp;Many of the malicious webpages follow the expected pattern of brand impersonation for phishing attempts and scams, which our research has seen daily for many years. The difference is that with the AI web builders, the attackers need to invest little to no effort or knowledge into creating them. As the AI web builder ecosystem evolves, it’s clear that new features and easier integrations will fuel even more sophisticated phishing campaigns in the future.&nbsp;How to protect yourself:&nbsp;<ul><li>Always watch out for too good to be true offers or investment opportunities</li><li>When dealing with a known brand, always verify whether this is the official website</li><li>Look out for any deviations from the designs you are already familiar with</li><li>Use unique passwords and MFA everywhere, preferably along with a reputable password manager</li><li>Use a reputable AV solution to be proactively protected against phishing and scam attempts</li><li>When in doubt, use products like Norton 360 with AI-powered scam protection to consult the security of links and web pages&nbsp;</li></ul><h3>Indicators of Compromise (IoCs)&nbsp;</h3>A broader list of IoCs can be found on our <a href="https://github.com/avast/ioc/tree/master/VibeScams" target="_blank" rel="noreferrer noopener">GitHub</a>.&nbsp;auth-binance.webflow[.]io&nbsp; binance-cdn-auth.webflow[.]io&nbsp; coinbase-wallet-verify.replit[.]app&nbsp; coinbase-wallet.squarespace[.]com&nbsp; auth-coinbase-login.typedream[.]app&nbsp; chrome-coinbase-extension.typedream[.]app&nbsp; wallet-coinbase.typedream[.]app&nbsp; microsoft-teams-login.elementor[.]cloud&nbsp; microsoft-outlookserver.odoo[.]com&nbsp; cz-microsoft.webflow[.]io&nbsp; updateaccount-microsoft.webflow[.]io&nbsp; app—trrezor-wallet.webflow[.]io&nbsp; app-atomiic-wallet.webflow[.]io&nbsp; coiinbase-com-wallet.typedream[.]app&nbsp; connect-metamesk-wallet.typedream[.]app&nbsp;

AI web builders are fueling a surge in realistic phishing sites that anyone can make

VibeScams: How AI website builders are shaping the internet 

Cybercriminals continue to refine their methods, and our latest discovery ties together multiple advanced attack techniques into a single campaign which demonstrates the latest trend in “Scam-Yourself” attacks. This particular campaign utilizes fully AI-generated videos created using deepfake principles and AI-generated scripts, created with ChatGPT and hosted on Pasteco and other script sharing websites. In our&nbsp;<a href="https://www.gendigital.com/blog/insights/reports/threat-report-q3-2024">Q3/2024 Threat Report</a>, we reported a 614% surge from “Scam-Yourself” attacks, those which&nbsp;manipulate people into voluntarily downloading malware or compromising their own security. Shortly after, we announced our&nbsp;<a href="https://www.gendigital.com/blog/news/family-of-brands/clipboard-protection">Clipboard Protection</a> feature, adding an extra security layer against these attacks. In a matter of days, we uncovered&nbsp;<a href="https://www.gendigital.com/blog/insights/research/cybercriminals-leveraging-chatgpt-to-create-malicious-payloads">cybercriminals actively leveraging ChatGPT to create and refine malicious payloads</a>. This article sheds light on the latest evolution of these tactics.What makes this discovery even more significant is the revelation that the campaign originates from a well-crafted video hosted on a verified YouTube channel with 110,000 subscribers. The channel appears to have been either compromised or acquired, and the video has already amassed +24,000 views. Additionally, numerous YouTube accounts, all variations of the same AI-generated profile, have been used to amplify the scheme, accumulating hundreds of thousands of subscribers.

<h2>The Dangerous Convergence of Threats</h2>This campaign connects multiple security challenges:<ul><li>Compromised accounts: The attack likely started with data theft using information stealers, allowing cybercriminals to take control of a verified YouTube channel and repurpose it for their scam operations. The channel, at first glance, appears legitimate as it includes a list of videos from the official TradingView channel, making it seem trustworthy. However, it also contains an unlisted deepfake video, which can be shared externally. This deception makes it difficult for victims to identify the fraud when viewing the channel. Furthermore, numerous fake accounts have been created, misusing this person as the “face” of multiple fraudulent channels. These fake YouTube accounts using the AI persona of Thomas Harris / Thomas Roberts have hundreds of thousands of subscribers. Many appear to be taken over or bought-off old accounts, some dating back to 2011. These accounts are continuously being taken down. Additionally, the engagement on these channels is manipulated, with all positive comments typically being 'bought' and added in bulk within a short period. Some of the observed fake accounts include @ThomasHarris-Iives, @thomas_hrris_a, @Thomas_View, @Thomas_Harris_Us, @ThomasRoberts_crypto, @tiommewweb3, @Thomas_Now, @RudoAMIN, and @UC6QvVs237am4RyYo1vGHF3g.</li></ul>

<ul><li>Deceptive tutorial video: The video presents itself as a tutorial to unlock TradingView’s developer mode, claiming that doing so will provide AI-powered indicators to help users grow their portfolio by a significant percentage. This lure is designed to appeal to individuals looking for an easy way to maximize their financial gains, making them more susceptible to following the video's malicious instructions.</li></ul>

<ul><li>Deepfake technology: The video appears to depict a completely AI-generated person, with no identifiable real-world counterpart. It utilizes techniques similar to those used in deepfake creation, combining voice, facial synthesis, and body movement replication to create a highly convincing impersonation. Furthermore, we’ve observed more AI-generated videos used across the fake profiles, suggesting attackers are re-creating multiple versions of this well-crafted content.</li><li>AI-generated malicious scripts: The script used in the attack was created with AI, in this particular case with ChatGPT. The script itself remains largely unchanged across different variations of the campaign, with the only significant difference being the Command-and-Control (C&amp;C) address. Attackers modify the C&amp;C domain as previous ones are flagged or blocked, demonstrating an adaptive approach to evade detections.</li><li>Scam-Yourself trend: The video demonstrates the same tactics seen in past attacks, instructing users to manually execute harmful commands (e.g., pressing Win+R and entering specific code).</li><li>Hosting services: Attackers have shifted from pasteco[.]com to equivalent sites such as 0bin[.]net or pastefy[.]com, and likely attacker-controlled domains such as pasteco[.]net and pasteco[.]io, a strategy designed in an attempt to evade detection.</li><li>Remote access and data theft: The end goal of this campaign is to install NetSupport or Lumma Stealer, granting attackers full control over the infected system and enabling data exfiltration. Cryptocurrency serves merely as a lure to attract an audience motivated by easy profit.&nbsp;</li></ul><h2>Malvertising as the origin</h2>Especially when we consider that some of the videos are unlisted and on verified accounts, one might ask how the user comes across such a video in the first place. What we can see is quite a big promotion via Sponsored advertising on YouTube, allowing the videos to pop-up while watching other videos with similar type of content, for example cryptocurrency related investments or other financial opportunities.

In this case, the account was named Oscar Davies, leading to slightly different variant of the AI generated video. This indicates that the attackers don’t hesitate to re-generate the original video to refresh the content and stay undetected for a longer period of time. When following the advertisement, the video is, in this case, also verified with the checkmark. We can see that the account has nearly half a million subscribers, with the video having 12 thousand views and the time of the analysis. Interestingly, these numbers do not match with the advertised views.

We observed multiple different thumbnails and video titles for the advertised videos, too, likely provided by additional AI driven image generation.

<h2>How We Are Protecting Users</h2>Our security solutions detect and block this threat. Our&nbsp;<a href="https://www.gendigital.com/blog/news/family-of-brands/clipboard-protection">Clipboard Protection</a> feature, designed to prevent clipboard-based scams, is particularly relevant in mitigating such attacks. This campaign reinforces the growing importance of proactive security measures against evolving social engineering threats.We remain committed to tracking these threats and investigating similar campaigns as they evolve. With deepfakes, AI-generated malware, and social engineering tactics advancing rapidly, proactive security measures are more crucial than ever. Given the involvement of a major player in this space, Lumma Stealer, it is likely that we will see even more campaigns following a similar model. This trend could lead to the creation of even more sophisticated AI-generated personas, making it increasingly difficult to distinguish real from fake. As cybercriminals refine their techniques, vigilance and robust security measures will be essential in combating these evolving threats. Stay safe and always verify sources before executing any online instructions.<h2>Indicators of Compromise (IoCs)</h2>902387e7be025fbe2c4b1a3f2ea212a26f9cbbcf82f656416b362c84089b712b (PowerShell commandline)a5e0635363bbb5d22d5ffc32d9738665942abdd89d2e6bd1784d6a60ac521797 (PowerShell script)developer-update[.]dev (C&amp;C)1b5b3e104f80e09f006a1080fefae16fed2dbf1abb697e464cbef3f2fabc5d90 (PowerShell commandline)begenokev[.]com/oauth/harum/voluptas (C&amp;C)2fe60aa1db2cf7a1dc2b3629b4bbc843c703146f212e7495f4dc7745b3c5c59e (Lumma Stealer)developerbeta[.]dev (C&amp;C)b89aa5a2837aa09d0caae2bc5be130186f236471411b279049dd1cfce6a5643f (PowerShell commandline)bcbb6de248a95bfdbf4cfe8db8d84f8a10db2896f27cb90058c69b7bb052d506 (PowerShell script)

Inside the Evolution of AI-Powered Scam Tactics

How AI-Generated Personas, Deepfake Tactics and Scripts Are Powering the Next Wave of"Scam-Yourself" Attacks

<h2>Clipboard Protection Reveals ChatGPT’s Role in Malware Attacks&nbsp;&nbsp;</h2>Attackers are constantly seeking innovative ways to deliver malicious payloads – harmful malware components. Our Norton, Avast and AVG brands recently launched Clipboard Protection, and the feature has uncovered an intriguing new development. &nbsp;Through Clipboard Protection, which analyzes content copied from websites in Firefox and Chrome, we identified a sophisticated attack chain involving a Remote Access Trojan (RAT) known as NetSupport RAT. This discovery highlights how attackers are exploiting AI tools like ChatGPT to create and iteratively improve malicious scripts, further enhancing their effectiveness.&nbsp;<h2>From AI to Malware: The ChatGPT Connection&nbsp;</h2>In a recent investigation, we discovered the development process of a malicious script in three distinct stages. The first two iterations were clearly drafts obtained from ChatGPT. AI-generated content’s ability to rapidly generate, adjust and polish technical scripts makes it a double-edged sword. While tools like ChatGPT offer benefits for applications, they also enable malicious actors to streamline the development of harmful payloads.The final version of the script, a polished malicious script, was later copied by unsuspecting users from legitimate content-sharing websites. The script poses as a prompt to "Enable dev-mode for TradingView," urging users to run terminal commands that ultimately install malware on their systems. This tactic leverages the trust and interest in popular platforms like TradingView to manipulate users into unwittingly executing harmful code.&nbsp;<h2>The Stage of the Malicious Script&nbsp;</h2>By analyzing the clipboard data, we were able to trace the evolution of the malicious script:&nbsp;<ol><li>Initial Drafts: Generated via ChatGPT, providing a basic framework for the attack. </li><li>Refinement: Adjustments made by the attackers to improve the script’s functionality and conceal malicious intent. </li><li>Final Version: A seamless, deceptive script disseminated on legitimate platforms, primed for unsuspecting users to execute.&nbsp;</li></ol>

Clipboard Protection’s ability to track these stages showcases the role of real-time analysis in identifying and reducing emerging threats.&nbsp;<h2>Clipboard Protection: A Game Changer&nbsp;</h2>This discovery underscores the importance of proactive features like our in an ever-evolving threat landscape. Cybercriminals are continuously adapting, finding new ways to bypass defenses and deliver their malicious payloads. Our Clipboard Protection feature was designed to analyze content copied from websites in real time, alerting users to potential threats like <a href="https://us.norton.com/blog/emerging-threats/threat-report-q3-2024" target="_blank" rel="noreferrer noopener">ClickFix and FakeCaptcha</a> attacks. By staying ahead of attackers with innovative solutions like this, we not only detect and prevent attacks but also gather valuable insights into emerging tactics, enabling us to anticipate and counter new threats effectively.&nbsp;As cybercriminals continue to adapt their tactics, relying on sophisticated tools like AI to refine and distribute malicious payloads, solutions like Clipboard Protection play a pivotal role in staying ahead of these threats. Tracking the evolution of attacks and intercepting them before they can cause harm can ensure protection for users and systems in this complex digital landscape.&nbsp;&nbsp;<h2>Indicators of Compromise (IoCs)&nbsp;</h2>SHA256&nbsp;46114741a409cdc05b10152cd469dd724dd44e1e61155ab47de26799c96d298f&nbsp;25dbd7505d600dbe024f5491a61c9a1ee16b81c151f7e8fd633d7144fe045106&nbsp;cd341e404f40bd826e4e3b5a60db28161a70a29a9fb360c74884c6254b0031a8&nbsp;URL&nbsp;https://pasteco[.]com/t2yc80yb&nbsp;

How ChatGPT Is Aiding Cybercriminals in Crafting Advanced Malware Scripts

Cybercriminals actively leveraging ChatGPT to create and refine malicious payloads

<h2>Key points:&nbsp;</h2><ul><li>Glove Stealer is an information stealer written in .NET, stealing sensitive data from wide range of browser extensions as well as locally installed software&nbsp;</li><li>It focuses on browser data (cookies, autofill, …), cryptocurrency wallets, 2FA authenticators, password managers, email clients, and more&nbsp;</li><li>Glove Stealer uses a dedicated supporting module to bypass App-Bound encryption by using IElevator service&nbsp;</li><li>While observed being spread via phishing emails resembling ClickFix, it itself also tries to mimic a fixing tool which users might use during troubleshooting problems they might have encountered&nbsp;</li></ul><h2>Introduction&nbsp;</h2>We’ve been closely observing various social engineering tactics, such as ClickFix and FakeCaptcha, for quite some time now. Tactics like these deceive users into thinking they are helping themselves, but by following the instructions from the attackers, they are actually inadvertently infecting their own devices. The steps the users are led to take commonly involve copying a malicious script that’s typically copied on background without user noticing. The instructions then prompt the user to paste and execute it via a PowerShell terminal or a Run prompt on Windows, displayed by a Win+R key combination.&nbsp;In a recent campaign using traditional phishing emails, we observed these scripts to distribute an interesting information stealer written in .NET, which we’ve named Glove.&nbsp;The stealer is relatively simple and contains minimal obfuscation or protection mechanisms, indicating it might still be in an early development phase. However, it uses a recent method to bypass App-Bound Encryption which was <a href="https://security.googleblog.com/2024/07/improving-security-of-chrome-cookies-on.html" target="_blank" rel="noreferrer noopener">introduced by Google</a> in Chrome 127. This bypass method involves using an IElevator service and was publicly <a href="https://github.com/xaitax/Chrome-App-Bound-Encryption-Decryption" target="_blank" rel="noreferrer noopener">disclosed by Alexander Hagenah</a> on Oct. 27, 2024.&nbsp;Other than stealing private data from browsers, it also tries to exfiltrate sensitive information from a list of 280 browser extensions and more than 80 locally installed applications. These extensions and applications typically involve cryptocurrency wallets, 2FA authenticators, password managers, email clients and others.&nbsp;<h2>Technical analysis&nbsp;</h2><h3>Distribution&nbsp;</h3>Even though ClickFix can take many forms, including inserting the crafted fake error messages into compromised websites or attacker-hosted pages, this campaign started with a user receiving a phishing email. Along with the e-mail, typically an attachment is present in HTML format. An HTML page like this contains typical ClickFix motives, showing a crafted fake error message stating that some content couldn’t be accessed properly, then advising the user how to fix it. By following the instructions, the user copies a malicious script to their clipboard and, after executing it in a terminal or the Run prompt, they unintentionally infect their own system. Below is an example of such an HTML page displayed when the user opens the attachment.&nbsp;

Under the hood, the script copied and executed by the user invokes a PowerShell command encoded by Base64. However, as is common in these campaigns, it’s followed by a couple of consecutive intermediary scripts.&nbsp;

After going through additional intermediary scripts, we get to a point where the script is calling and displaying an additional message to the user:&nbsp;

Following the messaging of the OneDrive example above, it attempts to appear as it should, as if it is fixing the DNS configuration. However, this is false, as there is no issue to begin with—the problem was artificially introduced to the user. The malicious script is contacting the attacker’s C&amp;C server to obtain Glove Stealer, ultimately leading the user’s device to become infected.&nbsp;In this specific case, the script downloads the Glove Stealer from this address below:&nbsp;https://master.volt-texs[.]online/api/c4slhp3l&nbsp;The payload is downloaded from the server as a text from the HTML page, and an analyst’s eye can immediately recognize the beginning of a PE file, just Base64 encoded.&nbsp;

<h2>Putting the gloves on to bypass and steal&nbsp;</h2>Naturally, this payload gets decoded and executed. Upon execution, Glove Stealer follows the whole narrative of ClickFix once again by displaying a terminal window with a text “Wait Search problems…”.&nbsp;

Along with the authors likely lacking some English skills, the “o” in the word “problems” is also a homoglyph, which is a character that looks the same (or almost identical) to an original one, yet with a different binary representation. However, this might not get properly interpreted in some terminal or environment configurations, as can be seen in our testing environment below.&nbsp;

The malware then pings another C&amp;C server, using a randomly generated string (a limited set of upper-cased letters and numbers) in the path, with the ID set to 0 to indicate it was successfully executed on the victim’s infected device:&nbsp;https://master.hdsjfkgsadoghdsiougds[.]space/mother/RANDOM_STRING?id=0&nbsp;This address is used multiple times during the malware execution to indicate successfully passing certain stages and submitting other data, such as encryption keys. For example, with an ID set to 1, it indicates that the initial malware configuration is set properly on the device.&nbsp;Before the data exfiltration takes place, Glove Stealer terminates any (browser) processes which contain any of the substrings mentioned below in the process name:&nbsp;<ul><li>chrome&nbsp;</li><li>yandex&nbsp;</li><li>browser&nbsp;</li><li>msedge&nbsp;</li><li>opera&nbsp;</li><li>brave&nbsp;</li><li>chromium&nbsp;</li><li>CryptoTab&nbsp;</li></ul>This is also being done in an infinite loop every 55 milliseconds and the malware steals data from other browsers, like Firefox.&nbsp;<h2>The exfiltration and data structure&nbsp;</h2>Glove Stealer parses and stores the information obtained from browsers into dedicated text files stored in the following directory structure:&nbsp;<ul><li>\AllPws.txt&nbsp;</li><li>\INFS.txt&nbsp;</li><li>\Cookies\&nbsp;</li><li>\Autofill\&nbsp;</li><li>\Restore\&nbsp;</li><li>\OTP\&nbsp;</li><li>\Wallets\&nbsp;</li></ul>All folders and files used to store the stolen data are prefixed with a path that represents a directory that contains the user's most recently used documents (via Environment.SpecialFolder.Recent), followed by an MD5 hash of from the combination of computer’s name and a SerialNumber of the device’s disk drive.&nbsp;The files stored in the folders which contain the stolen data from browser are labeled with the browser name and a possible postfix. For example, in case of cookies, this postfix would be the name of the exfiltrated browser profile (Default, Profile 1, Profile 2, …). As an example, the location and the name for Chrome cookies could be stored as follows:&nbsp;%APPDATA%\Microsoft\Windows\Recent\MD5_HASH\Cookies\Chrome_Default.txt&nbsp;The file INFS.txt contains a fingerprint of the device, including details like the OS, computer and username, maximum available RAM, language, CPU information and more. The remaining files stored in the dedicated directories don’t deviate from the used folder names and file names mentioned above.&nbsp;The cookies, wallets and other possible data are not obtained just from the browsers, but Glove Stealer is using vast lists of predefined locally installed applications and browser extensions, then tries to search for these and exfiltrate valuable data from them. Since the lists are too exhaustive to mention in the text (84 defined locations for the locally installed apps and 280 browser extensions), an interested reader can find these on our <a href="https://github.com/avast/ioc/tree/master/GloveStealer" target="_blank" rel="noreferrer noopener">GitHub. </a>To name here at least part of the scope, the Glove Stealer focuses on:&nbsp;<ul><li>Vast variety of cryptocurrency wallet browser extensions&nbsp;</li><li>2FA authenticators, including Google Authenticator, Microsoft, Aegis and LastPass, to name a few&nbsp;</li><li>Password managers, including Bitwarden, LastPass and KeePass, to name a few&nbsp;</li><li>Email clients like Thunderbird&nbsp;</li><li>Gaming platforms like Steam and Battle.net&nbsp;</li></ul>Data in which the stealer is interested in can be represented by searching for this list (not exhaustive) of files and locations for each of the app:&nbsp;<ul><li>\Network\Cookies&nbsp;</li><li>\Login Data&nbsp;</li><li>\Web Data&nbsp;</li><li>\Local Extension Settings&nbsp;</li><li>\Sync Extension Settings&nbsp;</li><li>\Local State&nbsp;</li><li>\LocalPrefs.json&nbsp;</li></ul>After the stealer is finished with data harvesting, the entire directory structure is then packed using FileZip and encrypted by 3DES in ECB mode and sent to a C&amp;C server. The key for the 3DES cipher is generated on the fly as a string value represented by DateTime.Now.Ticks string hashed using MD5.&nbsp;The ZIP file, representing the exfiltration package, is stored in the Recent directory with the name of MD5_HASH.xexe (the hash is still the computer’s name + SerialNumber). Immediately after it is encrypted using 3DES, it is renamed to MD5_HASHXE, where XE is an appended substring.&nbsp;To be able to decrypt the package, the attackers are sending this key in parameters using the ping C&amp;C server once again. For this, the ID parameter is set to 2 and the MD5 hash used as 3DES key immediately follows:&nbsp;https://master.hdsjfkgsadoghdsiougds[.]space/mother/RANDOM_STRING?id=2&amp;ids=MD5_key&nbsp;However, the exfiltrated package is sent to the other C&amp;C server as a POST request, with the data encoded by Base64:&nbsp;https://master.volt-texs[.]online/index2.php&nbsp;For the attackers to stay safe from losing the key, they also back-up the 3DES key here as well, using the non-hashed timestamp in the POST request.&nbsp;<h2>Supporting module for App-Bound encryption bypass&nbsp;</h2>In order to use the stolen data from Chrome, Glove Stealer needs to bypass the App-Bound encryption. To do this, it requests the original server once again to retrieve a .NET payload to do the job.&nbsp;This payload is a supporting module, which is rather small, and it is dedicated to bypassing the App-Bound encryption using IElevator service.&nbsp;https://master.volt-texs[.]online/postovoy/RANDOM_STRING&nbsp;Named as zagent.exe, this payload is downloaded and Base64-decoded into Chrome’s Program Files directory: %PROGRAMFILES%\Google\Chrome\Application\zagent.exe&nbsp;After execution, the module is using a hardcoded "app_bound_encrypted_key":" string for searching and retrieving the App-Bound encryption key stored in the local state file: %LOCALAPPDATA%\Google\Chrome\User Data\Local State&nbsp;&nbsp;

After the key is retrieved, it is Base64-decoded and stored in a dedicated file called chromekey.txt so it can be also accessed by Glove Stealer itself. After that, the C&amp;C server is pinged to indicate the bypass was successful (ID=4).&nbsp;Note that, since App-Bound encryption also performs a path validation for the location of the caller processes, this supporting module must be placed in the Chrome’s Program Files directory tree. As a result, Glove Stealer needs to acquire local admin privileges first to use this supporting module.&nbsp;<h2>Conclusion&nbsp;</h2>In this analysis, we described Glove Stealer, an information stealer observed in recent phishing campaigns that leverage social engineering tactics, such as ClickFix. In these tactics, the attackers aim to trick users into thinking they are helping themselves, when in reality, they are inadvertently infecting their devices by following the instructions provided by the attackers.&nbsp;Glove Stealer is capable of stealing various kind of information from many browsers, including Chrome, Firefox, Edge, Brave and others. To achieve this, Glove Stealer uses a dedicated supporting module that leverages IElevator service to bypass App-Bound encryption. Additionally, it steals sensitive data from two extensive lists: one representing locally installed applications and the other representing browser extensions. These include cryptocurrency wallets extensions, 2FA authenticators, password managers, email clients and more.&nbsp;<h3>Indicators of Compromise (IoCs)&nbsp;</h3>For more detailed list of IoCs, including the lists of locally installed apps and browser extensions, please visit our <a href="https://github.com/avast/ioc/tree/master/GloveStealer" target="_blank" rel="noreferrer noopener">GitHub</a>.&nbsp;Original script copied in clipboard:&nbsp; 2bf6fab237ab58ae6cfe78f9a61ab6dcaf55f437cb7a77878e2e6aae3b208e80&nbsp;Glove Stealer&nbsp;56da496329d54587c31119d8878a7831a9814a92839aa6a9873ceeb91575b11a&nbsp;Supporting module for App-Bound encryption bypass:&nbsp;86ad4082e086a0b9a22dc91a16d0d9be38232975ab4d3d035224fb6d6cc7a44c&nbsp;C&amp;C&nbsp;master.hdsjfkgsadoghdsiougds[.]space&nbsp;master.volt-texs[.]online&nbsp;

A .NET malware, bypasses Chrome's App-Bound Encryption, stealing data from browsers, crypto wallets, 2FA authenticators

Glove Stealer: Leveraging IElevator to Bypass App-Bound Encryption & Steal Sensitive Data

Over the past four weeks, we've protected more than 1.4 million customers against attempts to distribute malware through various fake Captcha campaigns, highlighting the sheer scale and persistence of these attacks. The primary malware being distributed, Lumma Stealer, poses a significant threat to users as it is designed to steal sensitive information, such as login credentials, cryptocurrency wallets, and other valuable data. This makes it a dangerous tool in the hands of cybercriminals, who can use it for identity theft, financial fraud, and further attacks.&nbsp;The latest campaign as part of these Captcha-style attacks launched just days ago and begins with a phishing email, which falsely claims to be from the GitHub Security Team, alerting the recipient to a security vulnerability in their repository. The email directs the user to a malicious website, as shown in the first image below.

Once the user clicks the link, they are redirected to a fake Captcha screen that prompts them to verify their identity by clicking on a button:&nbsp;

This action copies a malicious script to the user’s clipboard, followed by instructions on how to execute it. These instructions inadvertently lead the user to run the script, thereby infecting their own system. The instructions may suggest using the system's command line or, as in this case, the Win+R prompt:&nbsp;

The provided script, which is a malicious PowerShell command, contacts a command-and-control (C&amp;C) server to download additional malicious payloads. The primary objective is to download and execute the Lumma Stealer or an intermediary loader that eventually drops Lumma Stealer on the victim’s system.&nbsp;Under the hood, the button functionality is enabled by a small JavaScript that ensures copying of the PowerShell command into the user’s clipboard via the “I’m not a robot” button:&nbsp;

After the user pastes the command into their command line or the Win+R prompt, the script will download and execute additional PowerShell script from https://github-scanner[.]com/download.txt:&nbsp;

This script contacts the C&amp;C server once again and downloads an executable file called l6E.exe. This is the final Lumma Stealer payload which is downloaded to a Temp folder under the name SysSetup.exe, and executed.&nbsp;&nbsp;<h5>Geographical Spread of Fake Captcha Campaigns&nbsp;</h5>For a long time, we have been closely monitoring various fake Captcha campaigns. In the past four weeks alone, we have protected over 1.4 million unique users, highlighting the massive and widespread impact of these attacks. The heatmap below shows the regions most affected by fake Captcha attacks during this period. The 5 most affected countries are Italy, Argentina, France, Spain and Brazil.&nbsp;

<h5>Indicators of Compromise (IoCs)&nbsp;</h5>C&amp;C:https://github-scanner[.]comPowerShell script:10d4e15b63a07368299f2245661d7a4626cd1a91a9950a3cbed5b4276d2dc31fLumma Stealer:d737637ee5f121d11a6f3295bf0d51b06218812b5ec04fe9ea484921e905a207

<h5>Protecting Yourself&nbsp;</h5>We recommend the following tips to help protect yourselves from Captcha campaigns:&nbsp;<ul><li>Be Skeptical of Unsolicited Emails: Always verify the authenticity of unexpected emails, especially those prompting you to take immediate action on your repositories.&nbsp;</li><li>Do Not Execute Unknown Scripts: Never run scripts or commands from untrusted sources. Verify the script's content and origin before executing it.&nbsp;</li><li>Enable Two-Factor Authentication (2FA): Adding an extra layer of security can help protect your accounts even if your credentials are compromised by information stealers like Lumma Stealer.&nbsp;</li><li>Use a Reputable Antivirus Solution: Make sure you have a reliable antivirus solution, such as those offered by Gen (Avast, AVG, Avira, or Norton), installed and regularly updated on your devices. These solutions provide essential protection against malware like Lumma Stealer, detecting and blocking malicious scripts and payloads before they can cause any harm. Even if you accidentally interact with a phishing attempt, having a trusted security solution in place can act as a crucial safety net.&nbsp;</li></ul>&nbsp;<h5>Conclusion&nbsp;</h5>We are actively monitoring these attacks and will continue to keep you updated with the latest developments. Our team is committed to staying ahead of these threats, and if there are any new updates, we will share them here and in&nbsp; X (<a href="https://x.com/GenThreatLabs">https://x.com/GenThreatLabs</a>). Staying informed and taking proactive steps to protect yourself are key to staying safe online.&nbsp;For more information about recent campaigns also distributing Lumma Stealer, you can refer to our previous analysis <a href="https://www.gendigital.com/blog/news/innovation/lumma-stealer-github">here</a>. Stay safe and informed!&nbsp;

How Fake Captcha Campaigns are Distributing Lumma Stealer

Global Surge in Fake Captcha Attacks 

Senior Malware Researcher

<h2>Key Points</h2><ul><li>Avast discovered and analyzed a malware campaign hijacking an eScan antivirus update mechanism to distribute backdoors and coinminers</li><li>Avast disclosed the vulnerability to both eScan antivirus and India CERT. On 2023-07-31, eScan confirmed that the issue was fixed and successfully resolved</li><li>The campaign was orchestrated by a threat actor with possible ties to Kimsuky</li><li>Two different types of backdoors have been discovered, targeting large corporate networks</li><li>The final payload distributed by GuptiMiner was also XMRig</li></ul><h2>Introduction</h2>We’ve been tracking a curious one here. Firstly, GuptiMiner is a highly sophisticated threat that uses an interesting infection chain along with a couple of techniques that include performing DNS requests to the attacker’s DNS servers, performing sideloading, extracting payloads from innocent-looking images, signing its payloads with a custom trusted root anchor certification authority, among others.The main objective of GuptiMiner is to distribute backdoors within big corporate networks. We’ve encountered two different variants of these backdoors: The first is an enhanced build of PuTTY Link, providing SMB scanning of the local network and enabling lateral movement over the network to potentially vulnerable Windows 7 and Windows Server 2008 systems on the network. The second backdoor is multi-modular, accepting commands from the attacker to install more modules as well as focusing on scanning for stored private keys and cryptowallets on the local system.Interestingly, GuptiMiner also distributes XMRig on the infected devices, which is a bit unexpected for such a thought-through operation.The actors behind GuptiMiner have been capitalizing on an insecurity within an update mechanism of Indian antivirus vendor eScan to distribute the malware by performing a man-in-the-middle attack. We disclosed this security vulnerability to both eScan and the India CERT and received confirmation on 2023-07-31 from eScan that the issue was fixed and successfully resolved.GuptiMiner is a long-standing malware, with traces of it dating back to 2018 though it is likely that it is even older. We have also found that GuptiMiner has possible ties to Kimsuky, a notorious North Korean APT group, by observing similarities between Kimsuky keylogger and parts of the GuptiMiner operation. In this analysis, we will cover the GuptiMiner’s features and its evolution over time. We will also denote in which samples the particular features are contained or introduced to support the overall comprehension in the vast range of IoCs.It is also important to note that since the users rarely install more than one AV on their machine, we may have limited visibility into GuptiMiner’s activity and its overall scope. Because of this, we might be looking only at the tip of the iceberg and the true scope of the entire operation may still be subject to discovery.<h2>Infection Chain</h2>To illustrate the complexity of the whole infection, we’ve provided a flow chart containing all parts of the chain. Note that some of the used filenames and/or workflows can slightly vary depending on the specific version of GuptiMiner, but the flowchart below illustrates the overall process.The whole process starts with eScan requesting an update from the update server where an unknown MitM intercepts the download and swaps the update package with a malicious one. Then, eScan unpacks and loads the package and a DLL is sideloaded by eScan clean binaries. This DLL enables the rest of the chain, following with multiple shellcodes and intermediary PE loaders.Resulted GuptiMiner consists of using XMRig on the infected machine as well as introducing backdoors which are activated when deployed in large corporate networks.

<h2>Evolution and Timelines</h2>GuptiMiner has been active since at least 2018. Over the years, the developers behind it have improved the malware significantly, bringing new features to the table. We will describe the specific features in detail in respective subsections.With that said, we also wanted to illustrate the significant IoCs in a timeline representation, how they changed over time – focusing on mutexes, PDBs, and used domains. These timelines were created based on scanning for the IoCs over a large sample dataset, taking the first and last compilation timestamps of the samples, then forming the intervals. Note that the scanned dataset is larger than listed IoCs in the <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#ioc">IoC section</a>. For more detailed list of IoCs, please visit our <a href="https://github.com/avast/ioc/tree/master/GuptiMiner">GitHub</a>.<h3 id="domains-in-time">Domains in Time</h3>In general, GuptiMiner uses the following types of domains during its operations:&nbsp;<ul><li><code>Malicious DNS</code> – GuptiMiner hosts their own DNS servers for serving true destination domain addresses of C&amp;C servers via DNS TXT responses </li><li><code>Requested domains</code> – Domains for which the malware queries the DNS servers for </li><li><code>PNG download</code> – Servers for downloading payloads in the form of PNG files. These PNG files are valid images (a logo of T-Mobile) that contain appended shellcodes at their end </li><li><code>Config mining pool</code> – GuptiMiner contains two different configurations of mining pools. One is hardcoded directly in the XMRig config which is denoted in this group </li><li><code>Modified mining pool</code> – GuptiMiner has the ability to modify the pre-defined mining pools which is denoted in this group </li><li><code>Final C&amp;C</code> – Domains that are used in the last backdoor stage of GuptiMiner, providing additional malware capabilities in the backdoored systems </li><li><code>Other</code> – Domains serving different purposes, e.g., used in scripts&nbsp;</li></ul>Note that as the malware connects to the malicious DNS servers directly, the DNS protocol is completely separated from the DNS network. Thus, no legitimate DNS server will ever see the traffic from this malware. The DNS protocol is used here as a functional equivalent of telnet. Because of this, this technique is not a DNS spoofing since spoofing traditionally happens on the DNS network.&nbsp;Furthermore, the fact that the servers for which GuptiMiner asks for in the <code>Requested domain</code> category actually exist is purely a coincidence, or rather a network obfuscation to confuse network monitoring tools and analysts.

From this timeline, it is apparent that authors behind GuptiMiner realize the correct setup of their DNS servers is crucial for the whole chain to work properly. Because of this, we can observe the biggest rotation and shorter timeframes are present in the <code>Malicious DNS</code> group.&nbsp;Furthermore, since domains in the <code>Requested domain</code> group are irrelevant (at least from the technical viewpoint), we can notice that the authors are reusing the same domain names for longer periods of time.&nbsp;<h3 id="mutexes-in-time">Mutexes in Time&nbsp;</h3>Mutexes help ensure correct execution flow of a software and malware authors often use these named objects for the same purpose. Since 2018, GuptiMiner has changed its mutexes multiple times. Most significantly, we can notice a change since 2021 where the authors changed the mutexes to reflect the compilation/distribution dates of their new versions.&nbsp;

An attentive reader can likely observe two takeaways: The first is the apparent outliers in usage of <code>MIVOD_6</code>, <code>SLDV15</code>, <code>SLDV13</code>, and <code>Global\Wed Jun&nbsp; 2 09:43:03 2021</code>. According to our data, these mutexes were truly reused multiple times in different builds, creating larger timeframes than expected.&nbsp;Another point is the re-introduction of <code>PROCESS_</code> mutex near the end of last year. At this time, the authors reintroduced the mutex with the string in UTF-16 encoding, which we noted separately.<h3>PDBs in Time&nbsp;</h3>With regard to debugging symbols, the authors of GuptiMiner left multiple PDB paths in their binaries. Most of the time, they contain strings like <code>MainWork</code>, <code>Projects</code>, etc.&nbsp;

<h2 id="installation-process">Stage 0 – Installation Process&nbsp;</h2><h3>Intercepting the Updates</h3>Everyone should update their software, right? Usually, the individual either downloads the new version manually from the official vendor’s site, or – preferably – the software itself performs the update automatically without much thought or action from the user. But what happens when someone is able to hijack this automatic process?&nbsp;Our investigation started as we began to observe some of our users were receiving unusual responses from otherwise legitimate requests, for example on:&nbsp;<code>http://update3[.]mwti[.]net/pub/update/updll3.dlz</code>This is truly a legitimate URL to download the <code>updll3.dlz</code> file which is, under normal circumstances, a legitimate archive containing the update of the eScan antivirus. However, we started seeing suspicious behavior on some of our clients, originating exactly from URLs like this.&nbsp;What we uncovered was that the actors behind GuptiMiner were performing man-in-the-middle (MitM) to download an infected installer on the victim’s PC, instead of the update. Unfortunately, we currently don’t have information on how the MitM was performed. We assume that some kind of pre-infection had to be present on the victim’s device or their network, causing the MitM.&nbsp;<h3>Update Package</h3><code>c3122448ae3b21ac2431d8fd523451ff25de7f6e399ff013d6fa6953a7998fa3</code> <code>(version.dll, 2018-04-19 09:47:41 UTC)</code>Throughout the analysis, we will try to describe not just the flow of the infection chain, malware techniques, and functionalities of the stages, but we will also focus on different versions, describing how the malware authors developed and changed GuptiMiner over time.The first GuptiMiner sample that we were able to find was compiled on Tuesday, 2018-04-19 09:47:41 and it was uploaded to VirusTotal the day after from India, followed by an upload from Germany: <code>c3122448ae3b21ac2431d8fd523451ff25de7f6e399ff013d6fa6953a7998fa3</code>

This file was named <code>C:\Program Files\eScan\VERSION.DLL</code> which points out the target audience is truly eScan users and it comes from an update package downloaded by the AV.&nbsp;Even though this version lacked several features present in the newer samples, the installation process is still the same, as follows:&nbsp;<ol><li>The eScan updater triggers the update </li><li>The downloaded package file is replaced with a malicious one on the wire because of a missing HTTPS encryption (MitM is performed) </li><li>A malicious package <code>updll62.dlz</code> is downloaded and unpacked by eScan updater </li><li>The contents of the package contain a malicious DLL (usually called <code>version.dll</code>) that is sideloaded by eScan. Because of the sideloading, the DLL runs with the same privileges as the source process – eScan – and it is loaded next time eScan runs, usually after a system restart </li><li>If a mutex is not present in the system (depends on the version, e.g. <code>Mutex_ONLY_ME_V1</code>), the malware searches for <code>services.exe</code> process and injects its next stage into the first one it can find </li><li>Cleanup is performed, removing the update package&nbsp;</li></ol>The malicious DLL contains additional functions which are not present in the clean one. Thankfully the names are very verbose, so no analysis was required for most of them. The list of the functions can be seen below.

Some functions, however, are unique. For example, the function <code>X64Call</code> provides Heaven’s gate, i.e., it is a helper function for running x64 code inside a 32-bit process on a 64-bit system. The malware needs this to be able to run the injected shellcode depending on the OS version and thus the bitness of the <code>services.exe</code> process.&nbsp;

To keep the original eScan functionality intact, the malicious <code>version.dll</code> also needs to handle the original legacy <code>version.dll</code> functionality. This is done by forwarding all the exported functions from the original DLL. When a call of the legacy DLL function is identified, GuptiMiner resolves the original function and calls it afterwards.&nbsp;

<h3>Injected Shellcode in services.exe&nbsp;</h3>After the shellcode is injected into <code>services.exe</code>, it serves as a loader of the next stage. This is done by reading an embedded PE file in a plaintext form.&nbsp;

This PE file is loaded by standard means, but additionally, the shellcode also destroys the PE’s DOS header and runs it by calling its entry point, as well as it removes the embedded PE from the original location memory altogether.&nbsp;<h4>Command Line Manipulation&nbsp;</h4>Across the entire GuptiMiner infection chain, every shellcode which is loading and injecting PE files also manipulates the command line of the current process. This is done by manipulating the result of <code>GetCommandLineA/W</code> which changes the resulted command line displayed for example in Task Manager.&nbsp;

After inspecting this functionality, we believe it either doesn’t work as the authors intended or we don’t understand its usage. Long story short, the command line is changed in such a way that everything before the first <code>--parameter</code> is skipped, and this parameter is then appended to the process name.&nbsp;To illustrate this, we could take a command: <code>notepad.exe param1 --XX param2</code> which will be transformed into: <code>notepad.exeXX param2</code>&nbsp;However, we have not seen a usage like <code>power --shell.exe param1 param2</code> that would result into: <code>powershell.exe param1 param2</code> nor have we seen any concealment of parameters (like usernames and passwords for XMRig), a type of behavior we would anticipate when encountering something like this. In either case, this functionality is obfuscating the command line appearance, which is worth mentioning. An interested reader can play around with the functionality at the awesome godbolt.org <a href="https://godbolt.org/#z:OYLghAFBqd5QCxAYwPYBMCmBRdBLAF1QCcAaPECAMzwBtMA7AQwFtMQByARg9KtQYEAysib0QXACx8BBAKoBnTAAUAHpwAMvAFYTStJg1DIApACYAQuYukl9ZATwDKjdAGFUtAK4sGIM6SuADJ4DJgAcj4ARpjEIJIAzKQADqgKhE4MHt6%2B/ilpGQIhYZEsMXGJtpj2jgJCBEzEBNk%2BfgF2mA6Z9Y0ExRHRsfFJCg1NLbntY32hA2VDiQCUtqhexMjsHOYJocjeWADUJgluo8ShwAB0CMfYJhoAgtu7%2B5hHJ2wsJACe17f3TzMOwYey8h2OpwI%2BFQfwSd0eANCBAOBxYhjwyS8BgImAA%2BoZ0nhcWgWGiGOhcbRZhBkAhGgcAFQkskUqlhRYAkwAdisjxRSIOeGOFgOAHpRUcAKwWYgKZLWMwaG6SgAiUplUWSAFozAAOZUqgH8wQHGiygi4uVMDb4qg44i45LEVAbBQKXHMNjCsUSkzS2XyyxmSQG9XETU6kN%2Bw18wUms2jXHoJgKG4JEXisNyhX66NhiOKg1Go4I2PFhMW5Op95qjTC8t4c2W5LWvFMO2xR3O13uz1vY61%2Bux/jEA4QQU1g519MHZmGVmzP0WIWq73Wax4A4css84sozNarV7wVUMcHMBgCtJlMII5mABs5nvs9QpPnlMX0pXaoHA/PHC1MAODvR8HxfN9yQ/MIl03awDi4PNfwSNUgMA4DtweFEsNNRtEyrW8/yFdNjzwU9xwvK8rRtdt7S7F1MDdD1WH7B8n3AlkoMwGDEOQv8gP/EC2KZV8OLZN4Lz4rYzDMIDjxRbk3AU89L1wi0qLbDsHSdejGL7QSwLnSCxO41Ua0kgSnzYwyF2gr8Di1eCeMNZD/3MGTgMssDhIgmz%2B14lzZOk/iMOw7leUw7CcKbdTbVo7SeyYthJyI8LQq5GMIu5DL%2BTI5Sr3wo4uUUoq8tU5tW1izt4oY3tmK3Y9iEwAg1gYeCgiCIcIs%2BL4ADdMAgY8n2szil0oltqM0uiEr7aNSEGh9huM6V8pvO8RTMWaSPsqLEximiqu7GrEreBz7w5YjY0a5riFarh2s6rLOUeAU0VCCAkVIWc6WIBkGQwsLj1pelkBYdAlycu8zAYVAcRbdBLkwVQ3hbYhWAULh7K1AANLGDhRtGNslNwGDczrsKdJEqAG6TzElBQ3M%2BkH0HO1KsLJDEsSYHF8QYQliRE98xJpUGWePCnBCptzD1uO86b9YmGdnEWHtLJ50o4ZZaE4SVeD8DgtFIVBOEUyw4IUVZ1hYhIeFIAhNA15YAGsQASBJLldj3Pa9%2B99E4SRdftw3OF4BQQA0W37eWOBYCQElkjoWJyEoOOE7iYAuASAIaFoe1Q4gKJA6iUJGm%2BTgbaL5hiG%2BAB5KJtE6O3uF4ZlGAIauGFoUv9d4LAoi8YA3DEWhQ6b0gsDJYBxG7sfGwbvA%2BpHg3Ec6LwcUDpFqkDqkolRquPCwQOCHOFgy41vgDGABQADU8EwAB3avkkYU%2BZEEEQxHYKRX/kJQ1ED3QuD6EMMYdclh9B4CiKHSAyxUDJFqLzTgWpq5mF4KgPqxBzhYCgRAZYHQujOAgK4CYfhAHBFmKUcoehUjpHgcQqhBR4H9AoUMQBeD4E9HGJ4Voeg2HdGmEwwYcRWHTDocI3oAj5hCNwRbDYEhNbawDtPI2wFVC6nvFqe8kgDjAGQMgeCbszBjlwIQEgd5raLF4I3LQiwnYuzdl7BxHsfZaw4P7UgesDbKJDmHCO3co4wEQCAVYBBMQECTjSV88d6DEHCMxTgqj1GaO0bo/RlwUGBHwEQTBeh%2BBv1EOIL%2BuSf4qHUNPABpA76o2SKfeRHAdbuMDso6uq9QkHFQKeBJGitE6L0RnNJY4PAsCibEMxXALG%2BOsbY127tHEON9q4xRnjg62B8VYh28z0keNQcstZNjSDoPSM4SQQA%3D">here</a>.&nbsp;<h3 id="code-virtualization">Code Virtualization&nbsp;</h3><code>7a1554fe1c504786402d97edecc10c3aa12bd6b7b7b101cfc7a009ae88dd99c6</code> <code>(version.dll, 2018-06-12 03:30:01)&nbsp;</code>Another version with a mutex <code>ONLY_ME_V3</code> introduced a code virtualization. This can be observed by an additional section in the PE file called <code>.v_lizer</code>. This section was also renamed a few times in later builds.

Thankfully the obfuscation is rather weak, provided the shellcode as well as the embedded PE file are still in the plaintext form.&nbsp;Furthermore, the authors started to distinguish between the <code>version.dll</code> stage and the PE file loaded by the shellcode by additional mutex. Previously, both stages used the shared mutex <code>ONLY_ME_Vx</code>, now the sideloading uses <code>MTX_V101</code> as a mutex.<h2 id="installation-improvements">Stage 0.9 – Installation Improvements</h2><code>3515113e7127dc41fb34c447f35c143f1b33fd70913034742e44ee7a9dc5cc4c</code> <code>(2021-03-28 14:41:07 UTC)&nbsp;</code>The installation process has undergone multiple improvements over time, and, since it is rather different compared to older variants, we decided to describe it separately as an intermediary Stage 0.9. With these improvements, the authors introduced a usage of scheduled tasks, WMI events, two differently loaded next stages (<a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#png-loader">Stage 1 – PNG loader</a>), turning off Windows Defender, and installing crafted certificates to Windows.&nbsp;There are also multiple files dropped at this stage, enabling further sideloading by the malware. These files are clean and serve exclusively for sideloading purposes. The malicious DLLs that are being sideloaded, are two PNG loaders (Stage 1):&nbsp;<ul><li><code>de48abe380bd84b5dc940743ad6727d0372f602a8871a4a0ae2a53b15e1b1739 *atiadlxx.dll</code> </li><li><code>e0dd8af1b70f47374b0714e3b368e20dbcfa45c6fe8f4a2e72314f4cd3ef16ee *BrLogAPI.dll</code>&nbsp;</li></ul><h3>WMI Events&nbsp;</h3><code>de48abe380bd84b5dc940743ad6727d0372f602a8871a4a0ae2a53b15e1b1739</code> <code>(atiadlxx.dll, 2021-03-28 14:30:11 UTC)&nbsp;</code>At this stage, WMI events are used for loading the first of the PNG loaders. This loader is extracted to a path: <code>C:\PROGRAMDATA\AMD\CNext\atiadlxx.dll</code>&nbsp;Along with it, additional clean files are dropped, and they are used for sideloading, in either of these locations (can be both):&nbsp; <code>C:\ProgramData\AMD\CNext\slsnotif.exe&nbsp;</code> <code>C:\ProgramData\AMD\CNext\msvcr120.dll</code> or <code>C:\Program Files (x86)\AMD\CNext\CCCSlim\slsnotify.exe</code> <code>C:\Program Files (x86)\AMD\CNext\CCCSlim\msvcr120.dll&nbsp;</code>The clean file <code>slsnotify.exe</code> is then registered via WMI event in such a way that it is executed when these conditions are met:WMI conditions to trigger sideloadingIn other words, the sideloading is performed on a workday in either January, July, or November. The numbers represented by <code>%d</code> are randomly selected values. The two possibilities for the hour are exactly two hours apart and fall within the range of 11–16 or 13–18 (inclusive). This conditioning further underlines the longevity of GuptiMiner operations.<h3>Scheduled Tasks</h3><code>e0dd8af1b70f47374b0714e3b368e20dbcfa45c6fe8f4a2e72314f4cd3ef16ee</code> <code>(BrLogAPI.dll, 2021-03-28 14:10:27 UTC)</code>Similarly to the WMI events, GuptiMiner also drops a clean binary for sideloading at this location: <code>C:\ProgramData\Brother\Brmfl14c\BrRemPnP.exe</code>&nbsp;The malicious PNG loader is then placed in one (or both) of these locations: <code>C:\Program Files (x86)\Brother\Brmfl14c\BrLogAPI.dll</code> <code>C:\Program Files\Brother\Brmfl14c\BrLogAPI.dll&nbsp;</code>The scheduled task is created by invoking a Task Scheduler. The scheduled task has these characteristics:&nbsp;<ul><li>It is created and named as <code>C:\Windows\System32\Tasks\Microsoft\Windows\Brother\Brmfl14c</code> </li><li>Executes: <code>C:\ProgramData\Brother\Brmfl14c\BrRemPnP.exe</code> </li><li>The execution is done under a folder containing the to-be-sideloaded DLL, e.g.: <code>C:\Program Files (x86)\Brother\Brmfl14c\</code> </li><li>The execution is performed with every boot (<code>TASK_TRIGGER_BOOT</code>) with <code>SYSTEM</code> privileges&nbsp;</li></ul><h3>Deploy During Shutdown</h3><code>3515113e7127dc41fb34c447f35c143f1b33fd70913034742e44ee7a9dc5cc4c</code> <code>(2021-03-28 14:41:07 UTC)</code>Let’s now look at how all these files, clean and malicious, are being deployed. One of GuptiMiner’s tricks is that it drops the final payload, containing PNG loader stage, only during the system shutdown process. Thus, this happens at the time other applications are shutting down and potentially not protecting the user anymore.

From the code above, we can observe that only when the <code>SM_SHUTTINGDOWN</code> metric is non-zero, meaning the current session is shutting down, as well as all the supporting clean files were dropped successfully, the final payload DLL is dropped as well.&nbsp;An engaged reader could also notice in the code above that the first function that is being called disables Windows Defender. This is done by standard means of modifying registry keys. Only if the Defender is disabled can the malware proceed with the malicious actions.&nbsp;<h3>Adding Certificates to Windows</h3>Most of the time, GuptiMiner uses self-signed binaries for their malicious activities. However, this time around, the attackers went a step further. In this case, both of the dropped PNG loader DLLs are signed with a custom trusted root anchor certification authority. This means that the signature is inherently untrusted since the attackers’ certification authority cannot be trusted by common verification processes in Windows.&nbsp;However, during the malware installation, GuptiMiner also adds a root certificate to Windows’ certificate store making this certification authority trusted. Thus, when such a signed file is executed, it is understood as correctly signed. This is done by using <code>CertCreateCertificateContext</code>, <code>CertOpenStore</code>, and <code>CertAddCertificateContextToStore</code> API functions.

The certificate is present in a plaintext form directly in the GuptiMiner binary file.

During our research, we found three different certificate issuers used during the GuptiMiner operations:&nbsp;<ul><li><code>GTE Class 3 Certificate Authority </code></li><li><code>VeriSign Class 3 Code Signing 2010</code> </li><li><code>DigiCert Assured ID Code Signing CA&nbsp;</code></li></ul>Note that these names are artificial and any resemblance to legitimate certification authorities shall be considered coincidental.&nbsp;<h3 id="storing-payloads-in-registry">Storing Payloads in Registry&nbsp;</h3><code>8e96d15864ec0cc6d3976d87e9e76e6eeccc23c551b22dcfacb60232773ec049</code> <code>(upgradeshow.dll, 2023-11-23 16:41:34 UTC)&nbsp;</code>At later development stages, authors behind GuptiMiner started to integrate even better persistence of their payloads by storing the payloads in registry keys. Furthermore, the payloads were also encrypted by XOR using a fixed key. This ensures that the payloads look meaningless to the naked eye.&nbsp;We’ve discovered these registry key locations to be utilized for storing the payloads so far:&nbsp;<ul><li><code>SYSTEM\CurrentControlSet\Control\Nls\Sorting\Ids\en-US</code> </li><li><code>SYSTEM\CurrentControlSet\Control\PnP\Pci\CardList</code> </li><li><code>SYSTEM\CurrentControlSet\Control\Wdf\DMCF</code> </li><li><code>SYSTEM\CurrentControlSet\Control\StorVSP\Parsers</code>&nbsp;</li></ul><h2 id="png-loader">Stage 1 – PNG Loader&nbsp;</h2><code>ff884d4c01fccf08a916f1e7168080a2d740a62a774f18e64f377d23923b0297</code> <code>(2018-04-19 09:45:25 UTC)&nbsp;</code>When the entry point of the PE file is executed by the shellcode from <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#installation-process">Stage 0</a>, the malware first creates a scheduled task to attempt to perform cleanup of the initial infection by removing <code>updll62.dlz</code> archive and <code>version.dll</code> library from the system.&nbsp;Furthermore, the PE serves as a dropper for additional stages by contacting an attacker’s malicious DNS server. This is done by sending a DNS request to the attacker’s DNS server, obtaining the TXT record with the response. The TXT response holds an encrypted URL domain of a real C&amp;C server that should be requested for an additional payload. This payload is a valid PNG image file (a T-Mobile logo) which also holds a shellcode appended to its end. The shellcode is afterwards executed by the malware in a separate thread, providing further malware functionality as a next stage.Note that since the DNS server itself is malicious, the requested domain name doesn’t really matter – or, in a more abstract way of thinking about this functionality, it can be rather viewed as a “password” which is passed to the server, deciding whether the DNS server should or shouldn’t provide the desired TXT answer carrying the instructions.&nbsp;As we already mentioned in the <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#domains-in-time">Domains timeline section</a>, there are multiple of such “Requested domains” used. In the version referenced here, we can see these two being used:&nbsp;<ul><li><code>ext.peepzo[.]com</code> </li><li><code>crl.peepzo[.]com</code>&nbsp;</li></ul>and the malicious DNS server address is in this case:&nbsp;<ul><li><code>ns1.peepzo[.]com</code>&nbsp;</li></ul>Here we can see a captured DNS TXT response using Wireshark. Note that <code>Transaction ID = 0x034b</code> was left unchanged during all the years of GuptiMiner operations. We find this interesting because we would expect this could get easily flagged by firewalls or EDRs in the affected network.DNS TXT response captured by WiresharkThe requests when the malware is performing the queries is done in random intervals. The initial request for the DNS TXT record is performed in the first 20 minutes after the PNG loader is executed. The consecutive requests, which are done for the malware’s update routine, wait up to 69 hours between attempts.&nbsp;This update mechanism is reflected by creating separate mutexes with the shellcode version number which is denoted by the first two bytes of the decrypted DNS TXT response (see below for the decryption process). This ensures that no shellcode with the same version is run twice on the system.&nbsp;

<h3 id="dns-txt-decryption">DNS TXT Record Decryption</h3>After the DNS TXT record is received, GuptiMiner decodes the content using base64 and decrypts it with a combination of MD5 used as a key derivation function and the RC2 cipher for the decryption. Note that in the later versions of this malware, the authors improved the decryption process by also using checksums and additional decryption keys.&nbsp;For the key derivation function and the decryption process, the authors decided to use standard Windows CryptoAPI functions.

Interestingly, a keen eye can observe an oversight in this initialization process shown above, particularly in the <code>CryptHashData</code> function. The prototype of the <a href="https://learn.microsoft.com/en-us/windows/win32/api/wincrypt/nf-wincrypt-crypthashdata">CryptHashData API function</a> is:<code>BOOL CryptHashData(</code> <code>&nbsp; [in] HCRYPTHASH hHash,</code> <code>&nbsp; [in] const BYTE *pbData,</code> <code>&nbsp; [in] DWORD&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; dwDataLen,</code> <code>&nbsp; [in] DWORD&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; dwFlags</code> <code>);&nbsp;</code>The second argument of this function is a pointer to an array of bytes of a length of <code>dwDataLen</code>. However, this malware provides the string <code>L"POVO@1"</code> in a Unicode (UTF-16) format, represented by the array of bytes <code>*pbData</code>.

Thus, the first six bytes from this array are only <code>db 'P', 0, 'O', 0, 'V', 0</code> which effectively cuts the key in half and padding it with zeroes. Even though the malware authors changed the decryption key throughout the years, they never fixed this oversight, and it is still present in the latest version of GuptiMiner.&nbsp;<h3>DNS TXT Record Parsing&nbsp;</h3>At this point, we would like to demonstrate the decrypted TXT record and how to parse it. In this example, while accessing the attacker’s malicious DNS server <code>ns.srnmicro[.]net</code> and the requested domain <code>spf.microsoft[.]com</code>, the server returned this DNS TXT response:&nbsp;<code>VUBw2mOgagCILdD3qWwVMQFPUd0dPHO3MS/CwpL2bVESh9OnF/Pgs6mHPLktvph2</code>After fully decoding and decrypting this string, we get:&nbsp;This result contains multiple fields and can be interpreted as:&nbsp;

The first two bytes, Version 1 and Version 2, form the PNG shellcode version. It is not clear why there are two such versions since Version 2 is actually never used in the program. Only Version 1 is considered whether to perform the update – i.e., whether to download and load the PNG shellcode or not. In either case, we could look at these numbers as a major version and a minor version, and only the major releases serve as a trigger for the update process.The third byte is a key size that denotes how many bytes should be read afterwards, forming the key. Furthermore, no additional delimiter is needed between the key and the URL since the key size is known and the URL follows. Finally, the two-byte checksum can be verified by calculating a sum of all the bytes (modulo <code>0xFF</code>).&nbsp;After the DNS TXT record is decoded and decrypted, the malware downloads the next stage, from the provided URL, in the form of a PNG file. This is done by using standard <code>WinINet</code> Windows API, where the <code>User-Agent</code> is set to contain the bitness of the currently running process.

The C&amp;C server uses the <code>User-Agent</code> information for two things:&nbsp;<ul><li>Provides the next stage (a shellcode) in the correct bitness </li><li>Filters any HTTP request that doesn’t contain this information as a protection mechanism&nbsp;</li></ul><h3 id="parsing-the-png-file">Parsing the PNG File&nbsp;</h3>After the downloaded file is a valid PNG file which also contains a shellcode appended at the end. The image is a T-Mobile logo and has exactly <code>805</code> bytes. These bytes are skipped by the malware and the rest of the file, starting at an offset <code>0x325</code>, is decrypted by RC2 using the key provided in the TXT response (derived using MD5). The reason of using an image as this “prefix” is to further obfuscate the network communication where the payload looks like a legitimate image, likely overlooking the appended malware code.&nbsp;

After the shellcode is loaded from the position <code>0x325</code>, it proceeds with loading additional PE loader from memory to unpack next stages using Gzip.&nbsp;<h3>IP Address Masking</h3><code>294b73d38b89ce66cfdefa04b1678edf1b74a9b7f50343d9036a5d549ade509a</code> <code>(2023-11-09 14:19:45 UTC)&nbsp;</code>In late 2023, the authors decided to ditch the years-long approach of using DNS TXT records for distributing payloads and they switched to IP address masking instead.&nbsp;This new approach consists of a few steps:&nbsp;<ol><li>Obtain an IP address of a hardcoded server name registered to the attacker by standard means of using <code>gethostbyname</code> API function </li><li>For that server, two IP addresses are returned – the first is an IP address which is a masked address, and the second one denotes an available payload version and starts with <code>23.195.</code> as the first two octets </li><li>If the version is newer than the current one, the masked IP address is de-masked and results in a real C&amp;C IP address </li><li>The real C&amp;C IP address is used along with a hardcoded constant string (used in a URL path) to download the PNG file containing the shellcode&nbsp;</li></ol>The de-masking process is done by XORing each octet of the IP address by <code>0xA</code>, <code>0xB</code>, <code>0xC</code>, <code>0xD</code>, respectively. The result is then taken, and a hardcoded constant string is added to the URL path.&nbsp;As an example, one such server we observed was <code>www.elimpacific[.]net</code>. It was, at the time, returning:&nbsp;

The address <code>23.195.101[.]1</code> denotes a version and if it is greater than the current version, it performs the update by downloading the PNG file with the shellcode. This update is downloaded by requesting a PNG file from the real C&amp;C server whose address is calculated by de-masking the <code>179.38.204[.]38</code> address:&nbsp;&nbsp;The request is then made, along with the calculated IP address <code>185.45.192[.]43</code> and a hardcoded constant <code>elimp</code>. Using a constant like this serves as an additional password, in a sense: <code>185.45.192[.]43/elimp/</code>&nbsp;

When the PNG file is downloaded, the rest of the process is the same as usual.&nbsp;We’ve discovered two servers for this functionality so far:&nbsp;

<h3>Anti-VM and Anti-debug Tricks&nbsp;</h3><code>294b73d38b89ce66cfdefa04b1678edf1b74a9b7f50343d9036a5d549ade509a</code> <code>(2023-11-09 14:19:45 UTC)&nbsp;</code>Along with other updates described above, we also observed an evolution in using anti-VM and anti-debugging tricks. These are done by checking well known disk drivers, registry keys, and running processes.&nbsp;GuptiMiner checks for these disk drivers by enumerating <code>HKEY_LOCAL_MACHINE\SYSTEM\ControlSet001\services\Disk\Enum</code>:&nbsp;<ul><li><code>vmware </code></li><li><code>qemu </code></li><li><code>vbox </code></li><li><code>virtualhd&nbsp;</code></li></ul>Specifically, the malware also checks the registry key <code>HKEY_LOCAL_MACHINE\SOFTWARE\Cylance</code> for the presence of Cylance AV.&nbsp;As other anti-VM measures, the malware also checks whether the system has more than 4GB available RAM and at least 4 CPU cores.&nbsp;Last but not least, the malware also checks the presence of these processes by their prefixes:&nbsp;

<h3>Storing Images in Registry</h3><code>6305d66aac77098107e3aa6d85af1c2e3fc2bb1f639e4a9da619c8409104c414</code> <code>(2023-02-22 14:03:04 UTC)&nbsp;</code>Similarly to <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#storing-payloads-in-registry">Storing Payloads in Registry</a>, in later stages of GuptiMiner, the authors also started to save the downloaded PNG images (containing the shellcodes) into registry as well. Contrary to storing the payloads, the images are not additionally XORed since the shellcodes in them are already encrypted using RC2 (see <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#dns-txt-decryption">DNS TXT Record Decryption</a> section for details).&nbsp;We’ve discovered these registry key locations to be utilized for storing the encrypted images containing the shellcodes so far:&nbsp;<ul><li><code>SYSTEM\CurrentControlSet\Control\Arbiters\Class </code></li><li><code>SYSTEM\CurrentControlSet\Control\CMF\Class </code></li><li><code>SYSTEM\CurrentControlSet\Control\CMF\CORE </code></li><li><code>SYSTEM\CurrentControlSet\Control\CMF\DEF </code></li><li><code>SYSTEM\CurrentControlSet\Control\CMF\Els </code></li><li><code>SYSTEM\CurrentControlSet\Control\CMF\ASN </code></li><li><code>SYSTEM\CurrentControlSet\Control\MSDTC\BSR&nbsp;</code></li></ul><h2>Stage 2 – Gzip Loader&nbsp;</h2><code>357009a70daacfc3379560286a134b89e1874ab930d84edb2d3ba418f7ad6a0b</code> <code>(2019-04-02 07:30:21 UTC)&nbsp;</code>This stage is the shortest, the Gzip loader, which is extracted and executed by the shellcode from the PNG file, is a simple PE that decompresses another shellcode using Gzip and executes it in a separate thread.&nbsp;This thread additionally loads Stage 3, which we call Puppeteer, that orchestrates the core functionality of the malware – the cryptocurrency mining as well as, when applicable, deploying backdoors on the infected systems.&nbsp;Throughout the GuptiMiner operations, Gzip loader has not been changed with later versions.&nbsp;<h2>Stage 3 – Puppeteer</h2><code>364984e8d62eb42fd880755a296bd4a93cc071b9705c1f1b43e4c19dd84adc65</code> <code>(2019-03-15 10:07:36 UTC)&nbsp;</code>Let’s now look at the biggest Stage 3, the Puppeteer. It pulls its strings everywhere across the infected system, manipulating the GuptiMiner components to do its bidding, hence the name we’ve chosen. It orchestrates further actions and deploys two core components of the malware – an XMRig coinminer and two types of backdoors that target devices present in large corporate networks. Of course, Puppeteer also introduces additional tricks to the arsenal of the whole GuptiMiner operation.&nbsp;This stage also uses one of the many <code>Global\SLDV</code> mutexes which we described in the <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#mutexes-in-time">Mutex timeline</a>. For example, this particular sample uses <code>SLDV01</code> as its mutex.<h3>Puppeteer Setup</h3>Puppeteer performs several steps for a proper setup. Firstly, it adds a new power scheme in Windows so the PC does not go to sleep. If the CPU has only one core (anti-VM) or the mutex already exists, the malware ceases to function by going to infinite sleep.&nbsp;In the next phase, the malware kills all the processes with a name <code>msiexec.exe</code>, <code>cmstp.exe</code>, or <code>credwiz.exe</code>. After that, it creates a separate thread that injects XMRig into a credwiz.exe process freshly created by the malware. The malware also disables Windows Defender by setting its service start status to disabled.&nbsp;For the persistence, Puppeteer chose an interesting approach. Firstly, it creates a scheduled task with the following configuration:&nbsp;<ul><li>A legitimate <code>rundll32.exe</code> file is copied and renamed into <code>C:\ProgramData\Microsoft\Crypto\Escan\dss.exe</code> and this file is executed from the scheduled task </li><li>The malicious DLL is placed to <code>C:\ProgramData\Microsoft\Crypto\Escan\updll3.dll3</code> and this file is loaded by <code>dss.exe</code> (exported function <code>ValidateFile</code>) </li><li>The task is executed with every boot (<code>TASK_TRIGGER_BOOT</code>) and <code>TASK_RUNLEVEL_HIGHEST</code> priority </li><li>The task is named and located at <code>C:\Windows\system32\tasks\Microsoft\windows\autochk\ESUpgrade</code>&nbsp;</li></ul>With that, the malware copies the content of <code>updll3.dll3</code> into memory and deletes the original file from disk. Puppeteer then waits for a system shutdown (similarly to <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#installation-improvements">Stage 0.9</a>) by waiting for <code>SM_SHUTTINGDOWN</code> metric to be set to non-zero value, indicating the shutdown. This is checked every 100 milliseconds. Only when the shutdown of the system is initiated, the malware reintroduces the <code>updll3.dll3</code> file back onto disk.&nbsp;Putting the malicious DLL back just before the system restart is really sneaky but also has potentially negative consequences. If the victim’s device encounters a crash, power outage, or any other kind of unexpected shutdown, the file won’t be restored from memory and Puppeteer will stop working from this point. Perhaps this is the reason why authors actually removed this trick in later versions, trading the sophistication for malware’s stability.

The repetitive loading of <code>updll3.dll3</code>, as seen in the code above, is in fact Puppeteer’s update process. The DLL will ultimately perform steps of requesting a new <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#parsing-the-png-file">PNG shellcode</a> from the C&amp;C servers and if it is a new version, the chain will be updated.&nbsp;<h3>XMRig Deployment&nbsp;</h3>During the setup, Puppeteer created a separate thread for injecting an XMRig coinminer into <code>credwiz.exe</code> process. Before the injection takes place, however, a few preparation steps are performed.&nbsp;The XMRig configuration is present directly in the XMRig binary (standard JSON config) stored in the Puppeteer binary. This configuration can be, however, modified to different values on the fly. In the example below, we can see a dynamic allocation of mining threads depending on the robustness of the infected system’s hardware.&nbsp;

The injection is standard: the malware creates a new suspended process of <code>credwiz.exe</code> and, if successful, the coinmining is injected and executed by <code>WriteProcessMemory</code> and <code>CreateRemoteThread</code> combo.&nbsp;Puppeteer continuously monitors the system for running process, by default every 5 seconds. If it encounters any of the monitoring tools below, the malware kills any existing mining by taking down the whole <code>credwiz.exe</code> process as well as it applies a progressive sleep, postponing another re-injection attempt by additional 5 hours.&nbsp;<ul><li><code>taskmgr.exe</code> </li><li><code>autoruns.exe</code> </li><li><code>wireshark.exe</code> </li><li><code>wireshark-gtk.exe</code> </li><li><code>tcpview.exe</code>&nbsp;</li></ul>Furthermore, the malware needs to locate the current <code>updll3.dll3</code> on the system so its latest version can be stored in memory, removed from disk, and dropped just before another system restart. Two approaches are used to achieve this:&nbsp;<ul><li>Reading eScan folder location from <code>HKEY_LOCAL_MACHINE\SOFTWARE\AVC3</code> </li><li>If one of the checked processes is called <code>download.exe</code>, which is a legitimate eScan binary, it obtains the file location to discover the folder. The output can look like this: <ul><li><code>\Device\HarddiskVolume1\Program Files (x86)\eScan\download.exe</code>&nbsp;</li></ul></li></ul>The check for <code>download.exe</code> serves as an alternative for locating eScan installation folder and the code seems heavily inspired by the example code of <a href="https://learn.microsoft.com/en-us/windows/win32/memory/obtaining-a-file-name-from-a-file-handle">Obtaining a File Name From a File handle</a> on MSDN.&nbsp;Finally, Puppeteer also continuously monitors the CPU usage on the system and tweaks the core allocation in such a way it is not that much resource heavy and stays under the radar.&nbsp;<h3 id="backdoor-setup">Backdoor Setup</h3><code>4dfd082eee771b7801b2ddcea9680457f76d4888c64bb0b45d4ea616f0a47f21</code> <code>(2019-06-29 03:38:24 UTC)&nbsp;</code>The backdoor is set up by the previous stage, Puppeteer, by first discovering whether the machine is operating on a Windows Server or not. This is done by checking a DNS Server registry key (DNS Server service is typically running on a Windows Server edition): <code>SOFTWARE\Microsoft\Windows NT\CurrentVersion\DNS Server&nbsp;</code>After that, the malware runs a command to check and get a number of computers joined in a domain: <code>net group “domain computers” /domain</code>The data printed by the <code>net group</code> command typically uses 25 characters per domain joined computer plus a newline (<code>CR+LF</code>) per every three computers, which can be illustrated by the example below:&nbsp;

In this version of the backdoor setup, Puppeteer checks whether the number of returned bytes is more than 100. If so, Puppeteer assumes it runs in a network shared with at least five computers and downloads additional payloads from a hardcoded C&amp;C (<code>https://m.airequipment[.]net/gpse/</code>) and executes it using PowerShell command.&nbsp;Note that the threshold for the number of returned bytes was different and significantly higher in later versions of GuptiMiner, as can be seen in a dedicated section discussing <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#modular-backdoor">Modular Backdoor</a>, resulting in compromising only those networks which had more than 7000 computers joined in the same domain!&nbsp;If the checks above pass, Puppeteer uses a PowerShell command for downloading and executing the payload and, interestingly, it is run both in the current process as well as injected in <code>explorer.exe</code>.&nbsp;Furthermore, regardless of whether the infected computer is present in a network of a certain size or not, it tries to download additional payload from <code>dl.sneakerhost[.]com/u</code> as well. This payload is yet another PNG file with the appended shellcode. We know this because the code uses the exact same parsing from the specific offset <code>0x325</code> of the PNG file as described in <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#png-loader">Stage 1</a>. However, during our analysis, this domain was already taken down and we couldn’t verify what kind of payload was being distributed here.&nbsp;The Puppeteer’s backdoor setup process was improved and tweaked multiple times during its long development. In the upcoming subsections, we will focus on more important changes, mostly those which influence other parts of the malware or present a whole new functionality.&nbsp;<h3>Later Puppeteer Versions&nbsp;</h3>In later versions, the attackers switched to the datetime mutex paradigm (as illustrated in <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#mutexes-in-time">Mutexes in Time</a> section) and also introduced additional process monitoring of more Sysinternals tools like Process explorer, Process monitor, as well as other tools like OllyDbg, WinDbg, and TeamViewer.&nbsp;<h4>Pool Configuration</h4><code>487624b44b43dacb45fd93d03e25c9f6d919eaa6f01e365bb71897a385919ddd</code> <code>(2023-11-21 18:05:43 UTC)&nbsp;</code>Additionally, the GuptiMiner authors also started to modify pool addresses in XMRig configurations with a new approach. They started using subdomains by “<code>r</code>” and “<code>m</code>” depending on the available physical memory on the infected system. If there is at least 3 GB of RAM available, the malware uses: <code>m.domain.tld</code> with <code>auto</code> mode and enabled huge pages.If the available RAM is lesser than 3 GB, it uses: <code>r.domain.tld</code> with <code>light</code> mode and disabled huge pages.In order to not keep things simple, the authors later also started to use “<code>p</code>” as a subdomain in some versions, without any specific reason for the naming convention (perhaps just to say it is a “pool”).&nbsp;The usage of all such domains in time can be seen in the <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#domains-in-time">Domains timeline</a>.&nbsp;<h4>Variety in Used DLLs&nbsp;</h4>Puppeteer used many different names and locations of DLLs over the years for sideloading or directly loading using scheduled tasks. For example, these might be:&nbsp;<ul><li><code>C:\Program Files (x86)\eScan\updll3.dll3 </code></li><li><code>C:\Program Files\Common Files\SYSTEM\SysResetErr\SysResetErr.DLL </code></li><li><code>C:\Program Files\Microsoft SQL Server\SpellChecking\MsSpellChecking.DLL </code></li><li><code>C:\Program Files\Microsoft SQL Server\SpellChecking\MsSpellCheckingHost.DLL </code></li><li><code>C:\ProgramData\AMD\CNext\atiadlxx.dll </code></li><li><code>C:\ProgramData\Microsoft\Assistance\LunarG\vulkan-1.dll</code></li><li><code>C:\ProgramData\Microsoft\Crypto\Escan\updll3.dll </code></li><li><code>C:\ProgramData\Microsoft\Crypto\Escan\updll3.dll3 </code></li><li><code>C:\ProgramData\Microsoft\Network\Escan\AutoWake.dll&nbsp;</code></li></ul><h4>Puppeteer Cleanup&nbsp;</h4><code>1c31d06cbdf961867ec788288b74bee0db7f07a75ae06d45d30355c0bc7b09fe</code> <code>(2020-03-09 00:57:11 UTC)</code>We’ve also seen “cleaner” Puppeteers, meaning they didn’t contain the setup process for backdoors, but they were able to delete the malicious DLLs from the system when a running monitoring tool was detected.&nbsp;<h4 id="deploy-per-quarter">Deploy Per-Quarter&nbsp;</h4><code>1fbc562b08637a111464ba182cd22b1286a185f7cfba143505b99b07313c97a4</code> <code>(2021-03-01 10:43:27 UTC)&nbsp;</code>In this particular version, the deployment of the backdoor was performed once every 3 months, indicating a per-quarter deployment.

<h2>Stage 4 – Backdoor&nbsp;</h2>Since no one who puts such an effort into a malware campaign deploys just coinminers on the infected devices, let’s dig deeper into additional sets of GuptiMiner’s functionalities – deploying two types of backdoors on the infected devices.<h3>PuTTY Backdoor&nbsp;</h3><code>07beca60c0a50520b8dbc0b8cc2d56614dd48fef0466f846a0a03afbfc42349d</code> <code>(2021-03-01 10:31:33 UTC)</code> <code>E:\Projects\putty-src\windows\VS2012\x64\Release\plink.pdb</code>One of the backdoors deployed by GuptiMiner is based on a custom build of PuTTY Link (<code>plink</code>). This build contains an enhancement for local SMB network scanning, and it ultimately enables lateral movement over the network to potentially exploit <code>Windows 7</code> and <code>Windows Server 2008</code> machines by tunneling SMB traffic through the victim’s infected device.&nbsp;<h4>Local SMB Scanning&nbsp;</h4>First, the plink binary is injected into netsh.exe process by Puppeteer with the <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#deploy-per-quarter">Deploy per-quarter</a> approach. After a successful injection, the malware discovers local IP ranges by reading the IP tables from the victim’s device, adding those into local and global IP range lists.&nbsp;With that, the malware continues with the local SMB scanning over the obtained IP ranges: <code>xx.yy.zz.1-254</code>. When a device supporting SMB is discovered, it is saved in a dedicated list. The same goes with IPs that don’t support SMB, effectively deny listing them from future actions. This deny list is saved in specific registry subkeys named <code>Sem</code> and <code>Init</code>, in this location: <code>HKEY_LOCAL_MACHINE \SYSTEM\CurrentControlSet\Control\CMF\Class</code> where <code>Init</code> contains the found IP addresses and <code>Sem</code> contains their total count.&nbsp;There are conditions taking place when such a scan is performed. For example, the scan can happen only when it is a day in the week <code>(!)</code>, per-quarter deployment, and only at times between 12 PM and 18 PM. Here, we denoted by <code>(!)</code> a unique coding artefact in the condition, since checking the day of the week is not necessary (always true).&nbsp;

Finally, the malware also creates a new registry key <code>HKEY_LOCAL_MACHINE\SYSTEM\RNG\FFFF</code> three hours after a successful scan. This serves as a flag that the scanning should be finished, and no more scanning is needed.&nbsp;An even more interesting datetime-related bug can be seen in a conditioning of <code>RNG\FFFF</code> registry removal. The removal is done to indicate that the malware can perform another SMB scan after a certain period of time.&nbsp;As we can see in the figure below, the malware obtains the write time of the registry key and the current system time by <code>SystemTimeToVariantTime</code> API function and subtracts those. The subtraction result is a floating-point number where the integral part means number of days.&nbsp;Furthermore, the malware uses a constant <code>60*60*60*24=5184000</code> seconds (60 days) in the condition for the registry key removal. However, the condition is comparing <code>VariantTime</code> (days) with seconds. Thus, the backdoor can activate every <code>51.84</code> days instead of the (intended?) 60 days. A true blessing in disguise.&nbsp;

<h4>Lateral Movement Over SMB Traffic&nbsp;</h4>After the local SMB scan is finished, the malware checks from the received SMB packet results whether any of the IP addresses that responded are running <code>Windows 7</code> or <code>Windows Server 2008</code>. If any such a system is found on the local network, the malware adds these IP addresses to a list of potential targets.&nbsp;Furthermore, GuptiMiner executes the <code>main()</code> legacy function from plink with artificial parameters. This will create a tunnel on the port <code>445</code> between the attacker’s server <code>gesucht[.]net</code> and the victim’s device.&nbsp;

This tunnel is used for sending SMB traffic through the victim’s device to the IP addresses from the target list, enabling lateral movement over the local network.&nbsp;Note that this version of Puppeteer, deploying this backdoor, is from 2021. We also mentioned that only <code>Windows 7</code> and <code>Windows Server 2008</code> are targeted, which are rather old. We think this might be because the attackers try to deploy an exploit for possible vulnerabilities on these old systems.&nbsp;To orchestrate the SMB communication, the backdoor hand-crafts SMB packets on the fly by modifying <code>TID</code> and <code>UID</code> fields to reflect previous SMB communication. As shown in the decompiled code below, the SMB <code>packet 4</code>, which is crafted and sent by the malware, contains both <code>TID</code> and <code>UID</code> from the responses of the local network device.&nbsp;

Here we provide an example how the SMB packets look like in Wireshark when sent by the malware. After the connection is established, the malware tries to login as anonymous and makes requests for <code>\IPC$</code> and a named pipe.

Interested reader can find the captured PCAP on our <a href="https://github.com/avast/ioc/blob/master/GuptiMiner/extras/PCAP/smb_backdoor_networking.pcap">GitHub</a>.<h3 id="modular-backdoor">Modular Backdoor&nbsp;</h3><code>f0ccfcb5d49d08e9e66b67bb3fedc476fdf5476a432306e78ddaaba4f8e3bbc4</code> <code>(2023-10-10 15:08:36 UTC)&nbsp;</code>Another backdoor that we’ve found during our research being distributed by Puppeteer is a modular backdoor which targets huge corporate networks. It consists of two phases – the malware scans the devices for the existence of locally stored private keys and cryptocurrency wallets, and the second part is an injected modular backdoor, in the form of a shellcode.&nbsp;<h4>Checks on Private Keys, Wallets, and Corporate Network</h4>This part of the backdoor focuses on scanning for private keys and wallet files on the system. This is done by searching for <code>.pvk</code> and <code>.wallet</code> files in these locations:&nbsp;<ul><li><code>C:\Users\* </code></li><li><code>D:\* </code></li><li><code>E:\* </code></li><li><code>F:\* </code></li><li><code>G:\*&nbsp;</code></li></ul>If there is such a file found in the system, its path is logged in a newly created file <code>C:\Users\Public\Ca.txt</code>. Interestingly, this file is not processed on its own by the code we have available. We suppose the data will be stolen later when further modules are downloaded by the backdoor.&nbsp;The fact that the scan was performed is marked by creating a registry key: <code>HKEY_LOCAL_MACHINE\SYSTEM\Software\Microsoft\DECLAG&nbsp;</code>If some private keys or wallets were found on the system or the malware is running in a huge corporate environment, the malware proceeds with injecting the backdoor, in a form of a shellcode, into the <code>mmc.exe</code> process.&nbsp;The size of the corporate environment is guessed by the same approach as Puppeteer’s <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#backdoor-setup">backdoor setup</a> with the difference in the scale. Here, the malware compares the returned list of computers in the domain with 200,000 characters. To recapitulate, the data printed by the <code>net group</code> command uses 25 characters per domain joined computer plus a newline (<code>CR+LF</code>) per every three computers.&nbsp;

This effectively means that the network in which the malware operates must have at least 7781 computers joined in the domain, which is quite a large number.<h4>Backdoor</h4><code>8446d4fc1310b31238f9a610cd25ea832925a25e758b9a41eea66f998163bb34&nbsp;</code>This shellcode is a completely different piece of code than what we’ve seen so far across GuptiMiner campaign. It is designed to be multi-modular with the capability of adding more modules into the execution flow. Only a networking communication module, however, is hardcoded and available by default, and its hash is <code>74d7f1af69fb706e87ff0116b8e4fa3a9b87275505e2ee7a32a8628a2d066549 (2022-12-19 07:31:39 UTC)</code>.&nbsp;After the injection, the backdoor decrypts a hardcoded configuration and a hardcoded networking module using RC4. The RC4 key is also hardcoded and available directly in the shellcode.&nbsp;The configuration contains details about which server to contact, what ports to use, the length of&nbsp;delays that should be set between commands/requests, among others. The domain for communication in this configuration is <code>www.righttrak[.]net:443</code> and an IP address <code>185.248.160[.]141</code>.

The network module contains seven different commands that the attacker can use for instructing the backdoor about what to do. A complete list of commands accepted by the network module can be found in the table below. Note that each module that can be used by the backdoor contains such a command handler on its own.&nbsp;

The modules are stored in an encrypted form in the registry, ensuring their persistence: <code>HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\PCB</code>The backdoor also uses an import by hash obfuscation for resolving API functions. The hashing function is a simple algorithm that takes each byte of the exported function name, adds 1 to it, and then multiplies the previously calculated number (<code>calculated_hash</code>, starts with 0) by 131 and adds it to the byte:The server <code>www.righttrak[.]net:443</code> had, at the time, a valid certificate. Note for example the not-at-all-suspicious email address the authors used.&nbsp;

<h4 id="other-infection-vectors-modular-backdoor">Other Infection Vectors of Modular Backdoor</h4><code>af9f1331ac671d241bf62240aa52389059b4071a0635cb9cb58fa78ab942a33b</code>During our research, we have also found a 7zip SFX executable containing two files:&nbsp;<ul><li><code>ms00.dat</code> </li><li><code>notepad.exe</code>&nbsp;</li></ul><code>notepad.exe</code> is a small binary that decrypts <code>ms00.dat</code> file using RC4 with a key <code>V#@!1vw32</code>. The decrypted <code>ms00.dat</code> file is the same Modular Backdoor malware as described above.&nbsp;However, we have not seen this SFX executable being distributed by GuptiMiner. This indicates that this backdoor might be distributed by different infection vectors as well.&nbsp;<h2>Related and Future Research</h2>We’ve also observed other more or less related samples during our research.&nbsp;<h3>PowerShell Scripts</h3>Interestingly, we’ve found the C&amp;C domain from the backdoor setup phase (in Puppeteer) in additional scripts as well which were not distributed by traditional GuptiMiner operation as we know it. We think this might be a different kind of attack sharing the GuptiMiner infrastructure, though it might be a different campaign. Formatted PowerShell script can be found below:&nbsp;

In this case, the payload is downloaded and executed from the malicious domain only when an antivirus is installed, and its name has more than 4 letters and starts with <code>eS</code>. One does not have to be a scrabble champion to figure out that the malware authors are targeting the eScan AV once again. The malicious code is also run when the name of the installed AV has less than 5 letters.&nbsp;We’ve found this script being run via a scheduled task with a used command: <code>"cmd.exe" /c type "\&lt;domain&gt;\SYSVOL\&lt;domain&gt;\scripts\gpon.inc" | "\&lt;domain&gt;\SYSVOL\&lt;domain&gt;\scripts\powAMD64.dat" -nop -&nbsp;</code> where <code>powAMD64.dat</code> is a copy of <code>powershell.exe</code>. The task name and location was <code>C:\Windows\System32\Tasks\ScheduledDefrag&nbsp;</code><h3>Usage of Stolen Certificates&nbsp;</h3>We have found two stolen certificates used for signing GuptiMiner payloads. Interestingly, one of the used stolen certificates originates in Winnti operations. In this particular sample, the digital signature has a hash:&nbsp; <code>529763AC53562BE3C1BB2C42BCAB51E3AD8F8A56</code>&nbsp;This certificate is the same as mentioned by <a href="https://securelist.com/winnti-more-than-just-a-game/37029/">Kaspersky</a> more than 10 years ago. However, we’ve also seen this certificate to be used in multiple malware samples than just GuptiMiner, though, indicating a broader leak.&nbsp;A complete list of stolen certificates and their usage can be found in the table below:&nbsp;

<h3>Possible Ties to Kimsuky&nbsp;</h3><code>7f1221c613b9de2da62da613b8b7c9afde2ea026fe6b88198a65c9485ded7b3d</code> <code>(2021-03-06 20:13:32 UTC)&nbsp;</code>During our research, we’ve also found an information stealer which holds a rather similar PDB path as was used across the whole GuptiMiner campaign (<code>MainWork</code>): <code>F:\!PROTECT\Real\startW-2008\MainWork\Release\MainWork.pdb</code>&nbsp;However, we haven’t seen it distributed by GuptiMiner and, according to our data, it doesn’t belong to the same operation and infection chain. This malware performs stealing activities like capturing every keystroke, harvesting HTML forms from opened browser tabs, noting times of opened programs, etc., and stores them in log files.&nbsp;What is truly interesting, however, is that this information stealer might come from Kimsuky operations. Also known as Black Banshee, among other aliases, Kimsuky is a North Korean state-backed APT group.&nbsp;It contains the similar approach of searching for AhnLab real-time detection window class name <code>49B46336-BA4D-4905-9824-D282F05F6576</code> as mentioned by both <a href="https://asec.ahnlab.com/en/31089/">AhnLab</a> as well as <a href="https://blog.talosintelligence.com/kimsuky-abuses-blogs-delivers-malware/">Cisco Talos Intelligence</a> in their Information-gathering module section. If such a window is found, it will be terminated/hidden from the view of the infected user.&nbsp;

Furthermore, the stealer contains an encrypted payload in resources, having a hash: <code>d5bc6cf988c6d3c60e71195d8a5c2f7525f633bb54059688ad8cfa1d4b72aa6c (2021-02-19 19.02.2021 15:00:47 UTC)</code> and it has this PDB path: <code>F:\PROTECT\Real\startW-2008\HTTPPro\Release\HTTPPro.pdb</code>&nbsp;This module is decrypted using the standard RC4 algorithm with the key <code>messi.com</code>. The module is used for downloading additional stages. One of the used URLs are: <code>http://stwu.mygamesonline[.]org/home/sel.php</code> <code>http://stwu.mygamesonline[.]org/home/buy.php?filename=%s&amp;key=%s</code>&nbsp;The domain <code>mygamesonline[.]org</code> is commonly used by Kimsuky (with variety of subdomains).&nbsp;The keylogger also downloads next stage called <code>ms12.acm</code>:&nbsp;

With this, we see a possible pattern with the naming convention and a link to Modular Backdoor. As described in the <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#other-infection-vectors-modular-backdoor">Other Infection Vectors</a> section, the 7z SFX archive contains an encrypted file called <code>ms00.dat</code> with which we struggle to ignore the resemblance.Last but not least, another strong indicator for a possible attribution is the fact that the Kimsuky keylogger sample <code>dddc57299857e6ecb2b80cbab2ae6f1978e89c4bfe664c7607129b0fc8db8b1f</code>, which is mentioned in the same blogpost from Talos, contains a section called <code>.vlizer</code>, as seen below:&nbsp;

During the GuptiMiner installation process (<a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#installation-process">Stage 0</a>), we wrote about the threat actors introducing <a href="https://decoded.avast.io/janrubin/guptiminer-hijacking-antivirus-updates-for-distributing-backdoors-and-casual-mining/#code-virtualization">Code Virtualization</a> in 2018. This was done by using a dedicated section called <code>.v_lizer</code>.&nbsp;<h2>Conclusion&nbsp;</h2>In this analysis, we described our findings regarding a long-standing threat we called GuptiMiner, in detail. This sophisticated operation has been performing MitM attacks targeting an update mechanism of the eScan antivirus vendor. We disclosed the security vulnerability to both eScan and the India CERT and received confirmation on 2023-07-31 from eScan that the issue was fixed and successfully resolved.&nbsp;During the GuptiMiner operation, the attackers were deploying a wide chain of stages and functionalities, including performing DNS requests to the attacker’s DNS servers, sideloading, extracting payloads from innocent-looking images, signing its payloads with a custom trusted root anchor certification authority, among others.&nbsp;Two different types of backdoors were discovered, targeting large corporate networks. The first provided SMB scanning of the local network, enabling lateral movement over the network to potentially exploit vulnerable Windows 7 and Windows Server 2008 systems on the network. The second backdoor is multi-modular, accepting commands on background to install more modules as well as focusing on stealing stored private keys and cryptowallets.&nbsp;Interestingly, the final payload distributed by GuptiMiner was also XMRig which is a bit unexpected for such a thought-through operation.&nbsp;We have also found possible ties to Kimsuky, a notorious North Korean APT group, while observing similarities between Kimsuky keylogger and fragments discovered during the analysis of the GuptiMiner operation.&nbsp;<h2>eScan follow-up</h2>We have shared our findings and our research with eScan prior to publishing this analysis. For the sake of completeness, we are including their statement on this topic:“I would also like to highlight some key points: 1. Our records indicate that the last similar report was received towards the end of the year 2019. 2. Since 2020, we have implemented a stringent checking mechanism that utilizes EV Signing to ensure that non-signed binaries are rejected. 3. Multiple heuristic rules have been integrated into our solution to detect and block any instances of legitimate processes being used for mining, including the forking of unsigned binaries. 4. While our internal investigations did not uncover instances of the XRig miner, it is possible that this may be due to geo-location factors. 5. Our latest solution versions employ secure (https) downloads, ensuring encrypted communication when clients interact with our cloud-facing servers for update downloads.”According to our telemetry, we continue to observe new infections and GuptiMiner builds within our userbase. This may be attributable to eScan clients on these devices not being updated properly.<h2 id="ioc">Indicators of Compromise (IoCs)</h2>In this section, we would like to summarize the Indicators of Compromise mentioned in this analysis. As they are indicators, it doesn’t automatically mean the mentioned files and/or domains are malicious on their own.&nbsp;For more detailed list of IoCs of the whole GuptiMiner campaign, please visit our <a href="https://github.com/avast/ioc/tree/master/GuptiMiner">GitHub</a>.<h3>Evolution and Timelines&nbsp;</h3><h4>Domains</h4>

<h4>PDB paths&nbsp;</h4>

<h3>Stage 0 – Installation Process&nbsp;</h3>

<h3>Stage 0.9 – Installation Improvements&nbsp;</h3><h3>Stage 1 – PNG Loader&nbsp;</h3>

<h3>Stage 2 – Gzip Loader&nbsp;</h3>

<h3>Stage 3 – Puppeteer&nbsp;</h3>

Jan Rubín

Posts by Jan Rubín