Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects AI agent tooling (e.g., Copilot, Cursor, Ollama) that makes outbound network calls to known AI model providers followed by the execution of suspicious shell commands within a short duration. The rule flags high-severity activities such as download-cradles, credential reconnaissance, LOLBin usage, and persistence mechanisms. Informational alerts are generated for standard CLI activity within the same timeframe.
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 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 network navigation to Google Sites pages that match patterns associated with known Ledger-impersonating phishing campaigns. These sites are often reached through redirect chains involving Google Ads, cloud storage, and rotating domains.
Detects process execution attempts where the binary is located in specific, non-standard macOS system library paths often associated with background tasks or masquerading, specifically when the process is invoked with a '--type=renderer' command-line argument. This is a common pattern for malicious code attempting to mimic browser-based or helper processes.
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 network connection events associated with the download of machine learning model artifacts from public hubs that display indicators of malicious intent, such as unverified or newly created publisher accounts, missing or invalid cryptographic signatures, or the presence of embedded executable/pickle-deserialization payloads.
Detects network connection events associated with the download of machine learning model artifacts from public hubs that display indicators of malicious intent, such as unverified or newly created publisher accounts, missing or invalid cryptographic signatures, or the presence of embedded executable/pickle-deserialization payloads.
Detects the installation of machine learning or AI software dependencies (pip, conda, npm, poetry) that either pull from unauthorized registries or match known malicious/typosquatting naming patterns (e.g., LiteLLM supply-chain compromise). The rule also monitors for newly published packages that have been flagged as suspicious.
Detects usage of the 'gem' command (install, build, push, publish) that includes naming patterns or specific command strings associated with the GemStuffer supply chain compromise campaign. This targets attempts to introduce malicious dependencies into the environment using RubyGems.
Detects indicators of compromise related to the TraderTraitor (KelpDAO) threat actor, including specific file hashes, malicious process paths, C2 network infrastructure (IPs and domains), and malicious TLS certificates. The rule uses filename-path matching to reduce noise from legitimate macOS binaries.
Detects the use of 'terraform init' where the command line arguments reference known typosquatted HashiCorp registry domains. This behavior is indicative of a supply chain attack attempting to pull malicious providers or modules from an adversary-controlled source.
Detects Node.js processes executing suspicious command lines that involve fetching and executing remote JavaScript code. This behavior is indicative of a loader or downloader script commonly used to pull and evaluate malicious payloads at runtime, specifically referencing indicators associated with 'GHAPPIER'.
Detects instances where a single process on a device communicates with two or more distinct hosted LLM provider APIs (OpenAI, Anthropic, DeepSeek, Google) within a 24-hour window. This behavior is indicative of multi-model orchestration or 'consensus' techniques, which may be employed by sophisticated implants for automated tasking, data processing, or evasion.
Detects network communication to domains and specific file paths associated with the MacSync macOS stealer, which is used for malware dropper delivery and command-and-control (C2) operations. The rule monitors DeviceNetworkEvents for indicators such as specific malicious URLs, iCloud path abuse, and C2 infrastructure interaction.
Detects the execution of zsh with potentially suspicious command-line arguments (e.g., usage of curl, pipes, or osascript) occurring within a 15-minute window of network activity connecting to iCloud CalDAV servers. This behavior pattern may indicate unauthorized data access or exfiltration via legitimate cloud services.
Detects modifications to the .zshrc configuration file followed by shell commands interacting with the file, or the execution of commands that source the .repair-run script within a shell environment. This pattern is indicative of potential persistence mechanisms where an adversary modifies startup scripts to maintain access or execute malicious code upon shell initialization.
This rule detects modifications or creation of Git hooks ('pre-commit', 'post-checkout') within local repository directories. Attackers can leverage these hooks as an execution trigger for arbitrary code whenever standard Git actions occur, facilitating persistence or privilege escalation on compromised developer machines.
Detects the creation of a suspicious 'com.apple.finder.agent' plist file within a user LaunchAgents directory followed shortly by the termination of known macOS notification and background management agents. This pattern is often associated with malware attempting to evade detection or manipulate user notifications by disabling system components.
This rule detects a sequence of events indicative of MacSync credential phishing on macOS. It identifies the tampering of Pluggable Authentication Modules (PAM) configuration files or SecurityAgent plugins (often used to hook authentication processes), immediately followed by the use of osascript to display a spoofed password dialog to the user. This combined activity suggests an adversary is attempting to harvest local user account credentials.
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