Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects the presence of compiled Python 3.10 bytecode (.pyc) files that have been renamed to or disguised with a .cat extension. It specifically looks for the 'settingenv.cat' file associated with the Chinotto backdoor, as well as associated C2 URL patterns or command-line artifacts indicative of its download and execution chain.
Detects the download and presence of the Atomic macOS Stealer (AMOS) payload, a 64-bit Mach-O executable, identified by specific file hashes or internal file structure characteristics (Mach-O magic bytes) within a specific file size range.
Detects the download and presence of the Atomic macOS Stealer (AMOS) payload, a 64-bit Mach-O executable, identified by specific file hashes or internal file structure characteristics (Mach-O magic bytes) within a specific file size range.
Detects the execution of DahuaConsole processes that are spawned by or associated with the scannerdahua utility. This pattern is often associated with discovery or exploitation activities targeting Dahua devices.
This rule detects the creation or pushing of a local Git branch where the branch name matches the pattern of a 40-character SHA-1 or 64-character SHA-256 hash. This behavior is indicative of the Plugin4Shell exploitation technique, where attackers create branches named after specific commit SHAs to bypass security checks on self-hosted Git services like Bitbucket by pinning them to unintended commits.
Detects access to sensitive browser credential storage files (Login Data, key4.db, logins.json) by unauthorized processes. This behavior is indicative of credential theft, where an adversary attempts to extract stored passwords from browser profiles.
Detects access to sensitive browser credential storage files (Login Data, key4.db, logins.json) by unauthorized processes. This behavior is indicative of credential theft, where an adversary attempts to extract stored passwords from browser profiles.
This rule identifies Linux devices with kernel versions that are vulnerable to CVE-2026-80844, CVE-2026-81000, CVE-2026-68121, and CVE-2026-74469. It parses the OSBuild field to extract kernel major, minor, and patch version numbers and compares them against known unpatched releases.
This rule identifies Linux devices with kernel versions that are vulnerable to CVE-2026-80844, CVE-2026-81000, CVE-2026-68121, and CVE-2026-74469. It parses the OSBuild field to extract kernel major, minor, and patch version numbers and compares them against known unpatched releases.
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 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 various persistence mechanisms used by the CARBONATO botnet on Linux systems, including registration of cron jobs, systemd services/timers, and the usage of 'chattr +i' to make persistence files immutable.
Detects the presence of an XMRig cryptocurrency miner masquerading as the systemd-logind process. The rule identifies processes named 'systemd-logind' that either reside in an incorrect file path ('/usr/sbin/systemd-logind') or utilize command-line arguments characteristic of XMRig mining activity. This behavior is associated with the CARBONATO botnet.
Detects the creation of a file on the system that matches a known malicious SHA256 hash.
Detects the execution of the Android system utility 'getevent' by suspicious processes or in contexts suggesting unauthorized input event monitoring. This may indicate an attempt to capture user input, touch events, or screen interaction data.
Detects the execution of a Google Chrome process on a system with a known vulnerability (versions below 143.0.7499.192) in conjunction with browser extension installation or loading activity. This behavior is indicative of the GlicJack exploit (CVE-2026-0628), where a malicious extension attempts to impersonate a trusted AI vendor server to send unauthorized commands to the in-browser AI agent.
This rule identifies Linux devices with kernel versions that are vulnerable to CVE-2026-80844, CVE-2026-81000, CVE-2026-68121, and CVE-2026-74469. It parses the OSBuild field to extract kernel major, minor, and patch version numbers and compares them against known unpatched releases.
This rule identifies Linux devices with kernel versions that are vulnerable to CVE-2026-80844, CVE-2026-81000, CVE-2026-68121, and CVE-2026-74469. It parses the OSBuild field to extract kernel major, minor, and patch version numbers and compares them against known unpatched releases.
Detects the execution of the Linux 'unshare' utility to create new namespaces (user or network). This command is often used as a primitive for container escapes or privilege escalation by allowing an unprivileged user to create isolated environments. The rule excludes common container management and virtualization tools to reduce noise.
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