Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects execution of unusual preinstall, setup, or GitHub scripts initiated by Node Package Manager (npm) or node processes. This behavior is indicative of a supply chain attack or malicious dependency execution during the installation process of a package.
This rule detects a sequence of suspicious activities on macOS: downloading a file from a potentially malicious source using curl, mounting a disk image using hdiutil, and subsequently opening files from the mounted volume. This pattern is commonly associated with the delivery and execution of malicious payloads, such as trojanized software or malware installers.
Detects Node.js, NPM, or NPX processes attempting to read sensitive shell history files (e.g., .bash_history, .zsh_history). This behavior is characteristic of malicious activity such as infostealers attempting to harvest credentials or reconnaissance data from a compromised developer environment, notably observed in supply chain campaigns like the Polymarket Trap.
Detects instances where npm or node processes execute suspicious shell commands or scripts, often associated with software supply chain attacks. The rule looks for child processes like shell interpreters (sh, bash) or utilities (curl, wget, python) initiated by node/npm, combined with suspicious command-line patterns indicative of obfuscated execution or malicious package lifecycle scripts.
Detects Python processes (potentially associated with the ChocoPoC RAT, slogsec, or logcrypt modules) performing file read or access operations against common browser credential store files such as Login Data, Cookies, and Web Data on Windows and macOS systems.
This rule detects the creation of common ransomware ransom note filenames on a device. It triggers when two or more distinct ransom note files are created within a 5-minute window, indicating potential ransomware activity.
Detects unexpected crashes of critical macOS system processes such as sharingd, nsurlsessiond, airplayd, AirPlayXPCHelper, and NetAuthSysAgent. Frequent crashing of these components can indicate instability or potentially malicious attempts to interfere with system services.
Detects attempts by a Chrome web browser process to create, modify, or rename files associated with sensitive user credentials or application data, such as AWS credentials, browser logins, or environment configuration files. This behavior is indicative of credential theft or staging of sensitive information.
Detects high volumes of network connections initiated by common file sharing processes such as AirDrop, sharingd, NearShare, or QuickShare. This behavioral pattern, characterized by a large number of successful connections or numerous unique remote IP addresses, may indicate unauthorized mass data collection, reconnaissance, or potential lateral movement attempts.
This rule monitors for file and process events associated with known malicious Chrome extensions. It filters for specific extension IDs embedded within file paths or command-line strings, which typically indicate the installation or activity of malicious browser plugins.
Detects node.exe processes or file operations involving suspicious strings like 'piterpan', 'pmtrader', or 'DEFAULT_USERNAME_TAG', which are associated with malicious activity or unauthorized staging of scripts within the node environment.
This rule detects potential exploitation of an out-of-bounds write vulnerability in Python's Pillow library (ImageCms module). It identifies Python processes initialized with indicators related to known exploit research (exploitarium) that subsequently terminate with error signals indicative of a crash, such as heap-buffer-overflows or memory management errors, suggesting successful or attempted exploitation.
Detects the execution of the 'curl' utility with parameters configured to interact with an SMTP server using 'EXPN' or 'VRFY' commands while loading a configuration file via the '-K' flag. This pattern is indicative of an attempt to exploit CVEs or software vulnerabilities that are susceptible to CRLF injection within SMTP recipient fields, potentially leading to unauthorized mail relaying or protocol manipulation.
Detects suspicious usage of the Bun runtime, specifically when it is spawned by common Node.js development processes (like npm or node-gyp) or downloaded from non-official sources using command-line tools or browsers. This behavior is associated with the Miasma malware campaign, which abuses the Bun runtime for malicious operations.
This rule detects potentially malicious activity related to the 'node-gyp' build tool used by npm. It monitors for two patterns: 1) npm spawning node-gyp, which can be an automated trigger for malicious binding.gyp files, and 2) node-gyp spawning node.exe with command-line arguments indicative of obfuscated JavaScript (e.g., base64 encoding, eval, AES-GCM patterns), a technique often seen in malicious Node.js packages.
Detects the exploitation of CVE-2026-20253, where the Flowise application fails to correctly sanitize or validate environment variables. By using a case-insensitive bypass (e.g., lowercase 'node_options'), an attacker can cause a spawned Node.js child process to honor the variable and execute unauthorized JavaScript files via the '--require' command-line argument.
Detects the installation of Visual Studio Code extensions from sources other than the official Marketplace. This activity is monitored by checking process command line arguments for the --install-extension flag combined with external URLs or local file paths, which may indicate an attempt to install malicious extensions.
Detects execution of persistence-related commands such as cron job modifications or SSH key additions originating from development-related parent processes or installation commands like npm/pip. This behavior is indicative of potential supply chain compromises or malicious actors abusing development environments to maintain access.
Detects suspicious child processes spawned by AI coding agents that are potentially indicative of an indirect prompt injection attack. The rule specifically looks for the execution of 'axiom init' or 'pip install' commands, which are often used by attackers to execute malicious code within an environment when tricked by instructions embedded in repository files.
This rule detects shell commands (bash, sh, zsh) initiated by AI-agent or development-related processes (e.g., Python, Claude Desktop, openclaw) that target sensitive macOS paths associated with credentials, SSH keys, crypto wallets, and browser cookies, a technique characteristic of the Atomic Stealer (AMOS) malware.
Detects phishing links delivered through Microsoft Teams or Slack messages
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