Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,143 detections
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Detects potential model weight poisoning or unauthorized integrity compromise by identifying training jobs launched by non-authorized users, model file modifications in production paths that bypass CI/CD approval gates, or integrity mismatches between deployed model artifacts and their corresponding approved training outputs.
Detects anomalous activity where an AI coding assistant process reads sensitive local credential files (such as .env, .aws/credentials, or SSH keys) followed by an outbound network connection to a destination not associated with known package registries or AI provider APIs. This pattern is indicative of potential prompt-injection attacks targeting autonomous AI coding tools to exfiltrate developer secrets.
This rule detects potential supply chain compromise by monitoring for the installation of pip or npm packages with names similar to popular AI/LLM frameworks (e.g., langchain, llamaindex, mastra, litellm). It then correlates these installations with subsequent suspicious activity, including potential beaconing to external IPs, execution of known crypto-miners, or access to sensitive local files (e.g., AWS credentials, SSH keys).
Detects the installation of AI agent-related packages (npm, pip, etc.) followed shortly by unauthorized access to sensitive files or credential stores on the same host. This pattern is indicative of a malicious or 'poisoned' AI agent tool performing credential theft or unauthorized exfiltration of sensitive system data.
Detects the obfuscated first-stage JavaScript payload sharedLoad.min.js dropped by the malicious npm indexed-btree package, identified by string-array encoding and self-checksumming array rotation obfuscation patterns
Detects the sharedLoad.min.js obfuscated first-stage loader dropped by the malicious npm indexed-btree package, triggered via BTree.prototype.set to evade static/taint-analysis scanners
Detects the sharedLoad.min.js obfuscated first-stage loader dropped by the malicious npm indexed-btree package, triggered via BTree.prototype.set to evade static/taint-analysis scanners
Detects the indexed-btree npm malware loader embedded in BTree.prototype.set that spawns a detached hidden Node.js child process and references sharedLoad.min.js
Detects the execution of npm commands to install specific, potentially malicious or typo-squatted npm packages. The rule filters for common npm install command line arguments and matches against a predefined list of package names that are often associated with supply chain compromise attempts.
Detects the execution of npm commands to install specific, potentially malicious or typo-squatted npm packages. The rule filters for common npm install command line arguments and matches against a predefined list of package names that are often associated with supply chain compromise attempts.
Detects the execution of npm commands to install specific, potentially malicious or typo-squatted npm packages. The rule filters for common npm install command line arguments and matches against a predefined list of package names that are often associated with supply chain compromise attempts.
Detects the execution of npm commands to install specific, potentially malicious or typo-squatted npm packages. The rule filters for common npm install command line arguments and matches against a predefined list of package names that are often associated with supply chain compromise attempts.
Detects anomalous process activity that reads sensitive developer secrets (such as Anthropic, OpenAI, Gemini, or Hugging Face API keys) from common configuration or environment files, followed by an outbound network connection to a non-private IP address. This behavior is consistent with credential harvesting and exfiltration tactics used in supply chain attacks to hijack AI services.
Detects anomalous process activity that reads sensitive developer secrets (such as Anthropic, OpenAI, Gemini, or Hugging Face API keys) from common configuration or environment files, followed by an outbound network connection to a non-private IP address. This behavior is consistent with credential harvesting and exfiltration tactics used in supply chain attacks to hijack AI services.
Detects indicators associated with the exploitation of CVE-2025-3248 in Langflow. The rule identifies anomalous web requests targeting the /api/v1/validate/code endpoint, subsequent suspicious process creation (base64 encoded payloads in Python/shell), and network communication with identified command and control (C2) or data exfiltration IP addresses. It also includes alerts for observed contact with known extortion email addresses.
Detects git checkout of a bare 40-hex commit SHA or the literal FETCH_HEAD ref, executed by a recognized AI coding-agent process. Narrowed to the Plugin4Shell exploitation fingerprint (checkout of a pinned-SHA-shaped or FETCH_HEAD ref) rather than ordinary branch/tag checkouts, which these agents perform constantly during normal plugin installs.
Detects the loading or execution of pkexec.so, which is indicative of exploitation attempts targeting CVE-2021-4034 (PwnKit). This vulnerability allows for local privilege escalation on Linux systems, including within compromised containers.
Detects exploitation attempts by the CARBONATO botnet targeting exposed Docker daemons. The rule monitors for container escape techniques using 'nsenter' to access host namespaces, the execution of specific malicious entrypoint scripts, the use of known malicious container image references, and the creation of privileged containers with host bind mounts, which are indicative of host filesystem and network compromise.
Detects exploitation attempts by the CARBONATO botnet targeting exposed Docker daemons. The rule monitors for container escape techniques using 'nsenter' to access host namespaces, the execution of specific malicious entrypoint scripts, the use of known malicious container image references, and the creation of privileged containers with host bind mounts, which are indicative of host filesystem and network compromise.
Detects exploitation attempts by the CARBONATO botnet targeting exposed Docker daemons. The rule monitors for container escape techniques using 'nsenter' to access host namespaces, the execution of specific malicious entrypoint scripts, the use of known malicious container image references, and the creation of privileged containers with host bind mounts, which are indicative of host filesystem and network compromise.
Detects exploitation attempts by the CARBONATO botnet targeting exposed Docker daemons. The rule monitors for container escape techniques using 'nsenter' to access host namespaces, the execution of specific malicious entrypoint scripts, the use of known malicious container image references, and the creation of privileged containers with host bind mounts, which are indicative of host filesystem and network compromise.
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