Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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 creation of a 'ca-roots.pem' file within a hidden '.sckit' directory on Linux systems. This behavior is associated with the 'sckit' supply-chain malware (e.g., delivered via malicious npm packages like @memtensor/memos-cloud-openclaw-plugin), which uses this bundled certificate to enable HTTPS Command and Control (C2) communication in environments where standard system CA stores are unavailable.
Detects the execution of the sckit Go implant, linked to the MemTensor supply chain compromise, during its host-profiling stage. The rule monitors for the 'sckit' binary being executed with 'stage0' and '--config64' command-line arguments, or being spawned by legitimate runtime interpreters like Node.js or Python, indicating an attempt to profile host information (timezone, language, privileges) and subsequent self-deletion.
Detects potential exfiltration of sensitive DevOps credentials or configuration files (such as AWS credentials, SSH keys, Kubernetes configs, or cryptocurrency wallets) by monitoring for file access events followed by outbound network connections to known exfiltration channels like Slack or specific C2 infrastructure within a 30-minute window.
Detects instances where a non-browser process initiates connections to commercial LLM provider APIs (DeepSeek, OpenRouter, Mistral) followed by a connection to Discord CDN/Webhook endpoints within a 15-minute window. This behavior is indicative of a process acting as an autonomous C2 agent that exfiltrates data after receiving instructions or processing information via an LLM.
Detects the Sckit Go implant accessing sensitive local configuration and credential files, including SSH keys, cloud CLI tokens, and package manager credentials. The rule monitors for file read events initiated by processes identified as 'sckit' or processes with specific command line arguments.
Detects execution of PowerShell or Python scripts characterized by common patterns found in AI-generated code, including specific obfuscation techniques (base64, string manipulation), boilerplate error handling structures, verbose comments, and generic variable naming conventions. High scores indicate a higher probability of automated or obfuscated script execution.
This rule detects potential web shells that employ steganographic techniques by embedding malicious executable script code within image file structures (JPG or PNG). It specifically flags files containing common web shell markers 'ONEPIECE' or 'x_best_911' in combination with embedded script tags (e.g., <script, eval, <%, <?php) within files identified as having image magic bytes.
Matches archived SHA-256 hashes of known malicious CRX builds from the 'VPN for X' proxy farm extension family
Detects command-line operations indicative of bulk data export, dumping, or unauthorized scanning against vector databases (e.g., Pinecone, Chroma, Milvus, Qdrant) and caching backends (e.g., Redis). Such activities may indicate an adversary attempting to exfiltrate training data, embeddings, or context stored within an AI agent's memory backend.
Detects anomalous DNS query patterns originating from AI agent hosts that are characteristic of DNS tunneling, such as the use of TXT or NULL records and excessively long subdomain labels. These techniques are often used by attackers to establish covert Command and Control (C2) channels and exfiltrate data by bypassing traditional HTTP/HTTPS egress filtering.
Detects interpreter processes (e.g., python, node, bash) typically used for AI agents accessing sensitive system files or directories outside of defined sandbox or workspace paths. This behavior suggests potential sandbox escape or unauthorized path traversal by an AI agent.
Detects periodic, repetitive outbound HTTPS network connections from AI agent-runtime environments to domains that are not in the predefined allow-list. This behavior is consistent with C2 beaconing disguised as application API traffic, potentially indicating an AI agent has been compromised or misused for unauthorized external command communication.
Detects the presence of a specific, hardcoded Ed25519 public key associated with malicious cross-platform payloads (npm, Go modules, Terraform providers) that use Slack for C2 communication.
Detects high-risk destructive database operations (DROP, TRUNCATE, or UPDATE) on sensitive tables executed within six hours of a large outbound data transfer from the same host, a pattern indicative of anti-forensic database wiping following data exfiltration.
Detects network communication to specific Gusercontent endpoints consistent with malicious browser extensions exfiltrating captured session tokens or cookies. The rule monitors for POST requests containing parameters like leadId, email, and data, often used to transmit stolen authentication material.
This rule detects unauthorized access, creation, or modification of Git credential files (.git-credentials, .netrc) by processes other than standard Git credential management utilities. It also monitors command-line activity that references these credential stores to identify potential attempts to exfiltrate or manipulate stored credentials.
Detects execution of package management commands (pip, npm, conda) that attempt to pull packages from registries other than the approved internal repositories. This behavior is indicative of potential AI supply-chain compromise, including malicious packages or namesquatting targeted at machine-learning or LLM-related development workflows.
Detects instances where machine learning runtime processes (e.g., Python, torch-model-loader) load serialized model files (e.g., .pkl, .joblib, .pth) and subsequently spawn suspicious child processes like command shells, scripting engines, or download utilities. This behavior is indicative of arbitrary code execution via unsafe deserialization of a malicious model artifact.
Detects high-volume outbound transfers of sensitive machine learning artifacts, such as model files, training datasets, and model registries, to unauthorized external destinations like personal email or unmanaged cloud storage. This behavior is indicative of potential intellectual property theft.
Detects potential poisoning of machine learning training data by identifying unauthorized file modifications to datasets, DVC repositories, or model registries that occur outside of authorized ETL or CI/CD windows.
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