Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the installation of known malicious PyPI packages ('frint', 'skytext', 'slogsec', 'logcrypt.cryptography') associated with the ChocoPoC supply chain campaign. The rule monitors process execution commands related to 'pip' package installation and tracks the appearance of malicious compiled extension files ('gradient.pyd', 'gradient.so') on the filesystem.
Detects developers cloning a repository (mimicking a coding interview test) and immediately executing an anomalous binary or script hidden within the project structure. This behavior is indicative of the Void Dokkaebi threat actor's tactics.
Identifies package managers (npm, pip) executing pre-install scripts that perform immediate system reconnaissance (whoami, hostname, sw_vers, systeminfo) characteristic of Void Dokkaebi payloads. This rule looks for a sequence where a package manager initiates a process, and that initiated process then launches a reconnaissance command.
Detects anomalous system and process enumeration activity characteristic of the macOS.Gaslight threat, specifically looking for repeated execution of diagnostic commands like ps, sysctl, uname, and sw_vers in a short timeframe, which suggests an attempt to profile the infected host environment.
Detects execution of the macOS.Gaslight Rust-based implant. The rule identifies the specific malicious binary by its SHA-256 hash and targets its behavior of using runtime dynamic symbol resolution (via dlsym) to hide API calls from static analysis.
Detects the macOS.Gaslight Rust-based implant receiving and executing backdoor commands (e.g., shell, kill, upload, persistence) via the Telegram Bot API. The rule correlates outbound network connections to api.telegram.org with subsequent process execution patterns typical of command dispatch (such as execvp or posix_spawnp).
Detects unauthorized processes accessing browser credential databases (e.g., Login Data, Cookies, Web Data) associated with Chromium-based browsers on macOS. This behavior is indicative of credential harvesting often performed by infostealers like AMOS (Atomic macOS Stealer).
Detects potential malware execution chains characteristic of AMOS (Atomic macOS Stealer) and related 'ClickFix' campaigns. The rule monitors for the silent downloading of a disk image (.dmg) file to the /tmp directory using curl with quiet flags, followed immediately by the mounting of that image using hdiutil with the -nobrowse flag to bypass user notification and Gatekeeper security prompts.
Detects suspicious bash one-liners executed within macOS Terminal typically associated with the AMOS (Atomic macOS Stealer) 'ClickFix' campaign. The rule monitors for a sequence involving temporary file creation, downloading a disk image (DMG) from a known malicious domain ('svs-verificationdate.beer'), and silently attaching the DMG via hdiutil.
Detects the creation of a LaunchAgent property list (.plist) file on macOS that uses a spoofed Apple namespace label (com.apple.system.services.activity). This behavior is characteristic of the Gaslight malware family attempting to establish persistence.
Detects malicious Python or JavaScript package archives (such as those used in dependency confusion attacks) that contain obfuscated code execution logic (base64 encoded commands) within package installation hooks (setup.py), or those embedding suspicious binary executables (PE/ELF headers) within the package structure.
Detects unauthorized access to browser data and local credential stores initiated by the 'openclaw' or 'money-radar' malicious agent process trees. The rule monitors for file access events targeting sensitive credential files on Windows and macOS systems, which is characteristic of the ClawHub stealer malware.
Detects the creation of local user accounts on macOS using the 'dscl' command-line utility, specifically looking for the '-create' action within the '/Users/' directory and the presence of 'UniqueID' or 'IsHidden' parameters. The rule excludes common system-related UniqueIDs (0, 1, 202, 205, 248) to reduce false positives from legit system operations.
Detects network connections from a host to the api.adblock-for-youtube.com domain at the /api/v2/rules endpoint. This behavior is associated with the 'Adblock for YouTube' browser extension (cmedhionkhpnakcndndgjdbohmhepckk) as it retrieves remote JavaScript scriplets for rule updates, which can be a vector for unauthorized code execution if the domain is compromised or used maliciously.
Detects the 'BadBlocker' Adblock for YouTube browser extension communicating with its command-and-control infrastructure to fetch and execute arbitrary scriptlets in the MAIN JavaScript world. By injecting directly into the page context, the extension gains unauthorized access to the DOM, user browsing sessions, and sensitive form data.
This rule detects network connections or DNS queries originating from an endpoint towards domains or IP addresses associated with BlueKit command-and-control (C2) infrastructure, including TLDs (pk, ws, cc, su) and onion-based addresses. This activity is indicative of potential malware communication or C2 beacons.
This rule monitors DNS query logs for attempts to resolve domains associated with 'rrweb' or 'rrweb-snapshot'. These libraries are legitimate tools for session recording and DOM snapshotting but have been increasingly observed in malicious activity, specifically in web-based credential theft, automated session hijacking, or exfiltration of sensitive user activity via session recording.
Detects the execution of a malicious Node.js process using a fake ESLint loader named 'vscode-eioesocxetd.js'. This malware persists by modifying the global 'require' function and setting the 'global['!']' property to ensure its presence across process restarts, typically masquerading within npm package directories.
Detects HTTP GET requests to api.adblock-for-youtube.com to retrieve scriptlet rules. This endpoint is used by various browser extensions to fetch dynamic configuration updates, which can potentially facilitate MAIN-world JavaScript injection.
Detects Python scripts or processes accessing common browser credential and cookie storage files (such as Login Data, key4.db, or cookies.sqlite) and subsequently making outbound network connections, indicating potential theft and exfiltration of browser data.
This rule detects potentially malicious browser activity by monitoring for Google Chrome outbound network connections to known command-and-control (C2) domains associated with malicious browser extensions (e.g., BadBlocker). It also flags the installation or loading of specific, known-malicious browser extension IDs on endpoints.
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