Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects instances where AI-based coding or agent-driven tools (e.g., Cursor, Claude Code, AutoGPT) perform suspicious file archiving/packaging activities followed by network egress to unauthorized or known exfiltration-prone services. This helps identify potential data exfiltration by AI agents or compromised development environments.
Detects instances where AI coding and agent tools (e.g., Claude Code, Cursor, Copilot CLI, Ollama, LangGraph) create archive or database files containing potentially sensitive data and subsequently initiate network connections to non-AI-provider or non-developer services (exfiltration services or non-standard port connections).
Detects AI coding assistants and LLM agent frameworks attempting to access, read, or export sensitive credential files, registry hives, or application secrets. The rule monitors both file system operations on known secret locations and process execution patterns indicative of credential dumping or API token retrieval by these tools.
Detects AI coding assistants and LLM agent frameworks attempting to access, read, or export sensitive credential files, registry hives, or application secrets. The rule monitors both file system operations on known secret locations and process execution patterns indicative of credential dumping or API token retrieval by these tools.
Detects AI coding assistants and LLM agent frameworks spawning shell processes or interpreters to execute potentially malicious commands. The rule monitors for suspicious activity often associated with download cradles, credential access, reconnaissance, or persistence, while excluding standard interactive terminal usage.
Detects AI coding assistants and LLM agent frameworks spawning shell processes or interpreters to execute potentially malicious commands. The rule monitors for suspicious activity often associated with download cradles, credential access, reconnaissance, or persistence, while excluding standard interactive terminal usage.
Detects HTTP POST requests directed at Grav CMS endpoints where the __unique_form_id__ parameter contains directory traversal sequences (e.g., ../). This behavior is indicative of attempted exploitation of CVE-2026-42608, aimed at bypassing file system restrictions to write files to unauthorized locations outside the application's expected form data directory.
Detects instances where a Claude process accesses a 'SKILL.md' file, followed closely by a suspicious command execution on the same device. The rule specifically targets command-line activity that involves encoding, download-and-execute patterns, staging in sensitive directories, or communication with suspicious domains/IPs, which is indicative of a supply-chain or poisoned agent-skill file attack.
Detects instances where a Claude process accesses a 'SKILL.md' file, followed closely by a suspicious command execution on the same device. The rule specifically targets command-line activity that involves encoding, download-and-execute patterns, staging in sensitive directories, or communication with suspicious domains/IPs, which is indicative of a supply-chain or poisoned agent-skill file attack.
Detects the GitHub Actions runner executing the Bun runtime to run a hidden index.js payload, specifically associated with the compromised 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' actions. The rule filters for process execution context tied to GitHub runner internals and validates that the Bun execution follows the pattern observed in these supply chain attacks.
Detects the GitHub Actions runner executing the Bun runtime to run a hidden index.js payload, specifically associated with the compromised 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' actions. The rule filters for process execution context tied to GitHub runner internals and validates that the Bun execution follows the pattern observed in these supply chain attacks.
Detects the GitHub Actions runner executing the Bun runtime to run a hidden index.js payload, specifically associated with the compromised 'actions-cool/issues-helper' or 'actions-cool/maintain-one-comment' actions. The rule filters for process execution context tied to GitHub runner internals and validates that the Bun execution follows the pattern observed in these supply chain attacks.
Detects unauthorized execution of a Bun runtime executing an 'index.js' payload within a GitHub Actions workflow context, specifically targeting known compromised action paths (e.g., issues-helper, maintain-one-comment). The detection requires correlation with either sensitive environment/secret access or anomalous outbound network activity originating from the same process, indicating potential credential theft or C2 communication by a malicious CI/CD pipeline component.
This rule monitors network connections, file events, and process execution command lines for references to a list of known malicious URLs, including indicators associated with ClearFake, IClickFix, AMOS, Remus, and Mozi botnet payloads.
Detects execution of netcat with the "-e" flag followed by common shells. This could be a sign of a potential reverse shell setup.
Detects the presence of a shared client-side JavaScript framework used in 'ClickFix' MaaS (Malware-as-a-Service) panels, specifically associated with the Exvicy and ErrTraffic campaigns. The rule identifies specific function patterns (e.g., xdReq, xdDec, waitForToken) and state machine logic (tokens, deduplication hashing) commonly used for token interception and clipboard interaction.
Detects the initialization of known-malicious Terraform providers (dockerd) followed within one hour by the execution of a 'go run' process from the .terraform directory. This pattern is indicative of a supply chain compromise where an adversary leverages a typosquatted or malicious Terraform provider to facilitate the execution of a secondary payload.
Detects the initialization of known-malicious Terraform providers (dockerd) followed within one hour by the execution of a 'go run' process from the .terraform directory. This pattern is indicative of a supply chain compromise where an adversary leverages a typosquatted or malicious Terraform provider to facilitate the execution of a secondary payload.
Detects potential Denial of ML Service (DoMLs) attacks targeting inference endpoints. The rule identifies anomalous behavior characterized by large request payloads (oversized requests) correlated with increased latency or high GPU utilization, suggesting an attempt to exhaust compute resources and impact model availability.
Detects recovered scripts/payloads that harvest environment variables, API keys, bearer tokens, and Kubernetes secrets/configmaps into a variable named LOOT
Detects anomalous Telnet (TCP port 23) traffic associated with Mirai-variant botnet propagation, such as scanning for new targets or inbound compromise attempts, often following cPanel/WHM vulnerabilities.
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