Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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 artifacts associated with the JWR phishing framework client engine, specifically focusing on the presence of self-referential .toString().search() anti-debugging checks, decoy variable naming conventions, and WebSocket communication components (JWRCID/JWRCVV, ws-worker.js) used for session establishment.
Detects anomalous behavioral patterns where an AI agent process (e.g., Claude Code, Cursor, Aider) performs multi-category discovery of its own runtime environment, plugins, and host configuration. The rule identifies agents that trigger processes spanning at least three distinct discovery categories (Network, File/Plugin, Software, or System) within a short timeframe, indicating potential reconnaissance of agent-authorized capabilities and tools rather than standard host exploration. Covers T1082, T1518, T1083, T1016
Detects the execution of shell interpreters (PowerShell, CMD, Bash, Zsh) with suspicious command-line arguments typically associated with malicious activity, such as encoded commands, hidden window styles, or network download requests, when initiated by common user-facing applications like web browsers or Windows Explorer.
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.
This rule detects scenarios where an AI agent executes shell commands or system-level tools immediately after receiving a prompt injection attack from an untrusted source. It correlates agent audit logs—specifically identifying tool calls flagged for prompt injection—with actual endpoint process creation events. It flags process execution as 'High' severity if the command line matches common malicious patterns like encoded commands, IEX, or attempts to access credential material (e.g., lsass, mimikatz).
Detects the creation of a new local account that is immediately followed by (or associated with) execution from a process identified as an AI development or agent-based tool (e.g., Cursor, GitHub Copilot, Ollama). The rule correlates process creation events with Windows Security Event ID 4720 (User account created) and optionally checks for subsequent addition to the local Administrators group. This logic aims to surface potential unauthorized credential creation triggered by automated AI coding assistants or LLM-driven workflows.
This rule detects network communication with known malicious infrastructure (C2 IPs and URLs), presence of malicious files identified by SHA256 hashes on disk, and execution of known malicious files. It also correlates these activities with specific operator email addresses involved in the malicious campaign.
This rule detects network communication with known malicious infrastructure (C2 IPs and URLs), presence of malicious files identified by SHA256 hashes on disk, and execution of known malicious files. It also correlates these activities with specific operator email addresses involved in the malicious campaign.
Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
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.
This rule detects suspicious activity involving macOS LaunchAgents and files within the 'Application Support/System' directory, often associated with persistence or malicious background execution. The detection looks for the creation or modification of specific plist files (e.g., com.apple.finder.agent.plist) or git-related files in Application Support, correlated with process execution of commands like 'sysnotif-repair', 'launchctl' manipulating agents, or git operations involving custom hooks paths.
Detects PamStealer-style browser credential theft on macOS. The detection logic identifies the forced termination of browser helper processes using 'pkill' (which releases SQLite file locks) followed by the execution of a 'KcHelper' binary that uses specific environment markers (e.g., KC_PASS) to bypass keychain authorization prompts and steal stored browser credentials.
Detects the execution of TruffleHog, an automated secret-scanning CLI tool, often utilized by threat actors to harvest sensitive credentials from local filesystems, repositories, environment variables, or CI/CD configuration files following unauthorized access.
Detects outbound HTTP requests to identified scam infrastructure (e.g., herokuapp domains) containing platform-specific query parameters (win or mac). This behavior is indicative of a user interacting with a malicious site that is delivering an OS-tailored locker or malware payload.
Detects outbound HTTP requests to identified scam infrastructure (e.g., herokuapp domains) containing platform-specific query parameters (win or mac). This behavior is indicative of a user interacting with a malicious site that is delivering an OS-tailored locker or malware payload.
Detects Node.js or Python processes exhibiting suspicious behavior consistent with information-stealing malware (such as BeaverTail or InvisibleFerret). The rule monitors for these processes accessing sensitive files, including browser credentials, cookies, browser extension wallet settings, cryptocurrency wallet files, and various identification document image types, followed by the execution of archiving utilities (e.g., zip, rar, 7z) to stage the collected data for exfiltration.
Detects a chained sequence of suspicious activity initiated by agentic AI development tools (e.g., Claude Code, Aider, AutoGen). The rule identifies the execution of these tools followed by local reconnaissance commands, lateral movement attempts, and outbound network connections originating from the same host, indicating potential automated exploitation or credential abuse.
Detects anomalous, chained execution patterns initiated by AI agents integrated with Model Context Protocol (MCP) servers. The rule identifies a multi-stage sequence involving the invocation of browser automation tools followed by shell execution and subsequent outbound network activity, potentially indicating an AI-driven attack chaining filesystem, shell, or cloud-API access in an unauthorized manner.
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