Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
733 detections
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This rule detects potential supply chain compromise by monitoring common Linux package managers (pip, npm, apt, gem) for installation commands targeting non-standard, unauthorized registries, and for the subsequent execution of shells or network utilities by these package manager processes.
Detects the presence of specific Go build path metadata associated with the sckit Go implant, including modules and internal package structures, within an executable or file.
Detects the execution of the 'sckit' Go-based implant when spawned by Node.js or Python processes using the command-line arguments 'stage0 --config64'. This pattern is associated with malicious npm and PyPI supply chain packages (memtensor) designed to harvest credentials and establish persistence.
Detects malicious BASH_ENV injection within GitHub Actions CI pipelines originating from the compromised sckit_poetry_build.py build backend. The injection, which targets GITHUB_ENV to introduce a malicious _pypi_bridge.sh script, is used to intercept PyPI publishing tokens during build processes, characteristic of the MemTensor/sckit supply chain attack.
Detects the creation of a malicious GitHub Actions workflow file named 'runtime-update.yml', a known tactic used by the Sckit supply-chain worm. The worm injects this workflow to automate persistence and further propagation of the Go-based implant within CI/CD pipelines.
Detects the execution of the 'sckit' Go implant, a malicious binary associated with trojanized '@memtensor/memos-cloud-openclaw-plugin' or 'MemoryOS' packages. The rule identifies instances where this binary is spawned as a child process of a language runtime (Node.js or Python) when the execution originates from specific directory paths associated with these packages, or when the parent process command line indicates these packages are being initialized.
Detects malicious activities associated with the SCKit worm, specifically targeting package publishing via npm/twine, GitHub Actions workflow injection (runtime-update.yml), and the dropping of postinstall propagation stubs (bootstrap.cjs) when initiated by SCKit processes.
Detects malicious activities associated with the SCKit worm, specifically targeting package publishing via npm/twine, GitHub Actions workflow injection (runtime-update.yml), and the dropping of postinstall propagation stubs (bootstrap.cjs) when initiated by SCKit processes.
This rule detects GitHub Actions workflows that explicitly reference specific 'actions-cool' GitHub actions using a known-bad mutable version tag ('@v2.2.1'). This tag corresponds to malicious code introduced into these repositories, allowing for potential CI/CD pipeline poisoning and supply chain compromise.
Detects activity associated with the MALFEX npm supply-chain campaign (also known as Overlord/movinlike). This rule performs a sweep for known malicious file hashes, suspicious process command lines, outbound network connections to malicious domains or IPs, and email communications from identified adversary-controlled accounts.
Detects unauthorized or suspicious modifications to AI model artifacts, including fine-tuned weights and LoRA adapters. This rule identifies tampering attempts via unsigned uploads, bypassing CI/CD production deployment approvals, or mismatches in hash/checksum compared to known-good versions.
This rule monitors for unauthorized changes to GitHub Actions workflows that involve release or publishing processes, specifically looking for indicators of supply chain compromise like the introduction of 'id-token: write' or changes to publish scripts. It correlates these modifications with subsequent workflow executions and npm registry publish events attributed to the same actor to identify potential account takeover and malicious package distribution.
Detects potential SaaS supply-chain attacks where a third-party application or connector triggers an OAuth permission scope escalation or publishes a new version, followed by the associated service account initiating outbound network connections to rare or newly-registered domains within a 24-hour window.
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
This rule detects the execution of common Node.js package managers (npm, yarn, pnpm) attempting to install specific dependencies that match known patterns of malicious or typosquatted packages often used in supply chain attacks. The detection focuses on suspicious package names that mimic common data structure library naming conventions.
Detects network activity associated with a malicious NPM package (typosquatting) beaconing to a known C2 server (69.48.229.140) and performing command and control operations, including command polling and data exfiltration, specifically from a Node.js environment.
Detects network activity associated with a malicious NPM package (typosquatting) beaconing to a known C2 server (69.48.229.140) and performing command and control operations, including command polling and data exfiltration, specifically from a Node.js environment.
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