Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
956 detections
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Detects high-frequency outbound TLS connections to 'api-gost.oneme.ru', which is indicative of potential bulk contact list uploading, possibly related to unauthorized data collection by the Max Messenger application.
Detects high-frequency outbound TLS connections to 'api-gost.oneme.ru', which is indicative of potential bulk contact list uploading, possibly related to unauthorized data collection by the Max Messenger application.
Detects high-frequency outbound TLS connections to 'api-gost.oneme.ru', which is indicative of potential bulk contact list uploading, possibly related to unauthorized data collection by the Max Messenger application.
Detects high-frequency outbound TLS connections to 'api-gost.oneme.ru', which is indicative of potential bulk contact list uploading, possibly related to unauthorized data collection by the Max Messenger application.
Detects suspicious processes accessing cryptocurrency wallet data files or browser extension settings, characteristic of infostealer malware such as AMOS or GhostCode. The rule excludes common browser processes and legitimate wallet applications to identify unauthorized attempts to harvest wallet credentials, private keys, or seed phrases.
Detects instances where a non-browser process accesses multiple distinct browser credential or session artifacts (such as Login Data, Cookies, or Local State) across different browser profiles in a short period. This behavior is indicative of credential harvesting activities, often associated with C2 frameworks or post-exploitation tools attempting to steal session cookies, 2FA backup codes, or saved passwords.
Detects Python processes (python.exe, python3.exe, pythonw.exe) loading native library files (.pyd or .so) followed by file access to sensitive browser credential and cookie storage locations (e.g., Login Data, Cookies, Local State). This pattern is characteristic of infostealer activity, where a Python script uses a native module to decrypt or extract browser-stored credentials.
Detects potential Remus infostealer activity by monitoring unauthorized processes accessing AI agent credential directories (Claude, Cursor, OpenCode, Codex) or cryptocurrency wallet files, specifically when combined with .lnk shortcut file access behavior.
Detects the creation or access of .pst files by processes other than standard email clients (Outlook) or web browsers. This activity is often associated with the staging and collection of sensitive email data, which could indicate credential theft or data exfiltration attempts.
This rule detects malicious activity associated with the Bazinga macOS backdoor, specifically focusing on the clearing of TCC privacy permissions, the staging of keychain and browser data using 'ditto', and the subsequent exfiltration of this data to a remote host via 'curl'.
Detects unauthorized access to critical Chromium-based browser credential databases (such as Login Data, Cookies, and History) by processes other than recognized browser executables. This behavior is indicative of credential-harvesting activity, specifically matching techniques employed by infostealers like RevStealer.
Detects non-browser processes accessing sensitive browser credential stores (e.g., Login Data, Cookies) and cryptocurrency wallet files within a short time window. This pattern is characteristic of infostealer malware like Vidar, which grabs browser and wallet data for exfiltration.
Detects web browser processes (Chrome, Edge, Firefox, Brave, Opera, IE) being spawned by a parent process that is not part of the standard, trusted application launch lineage. This pattern is commonly observed in credential-stealing malware like Vidar Stealer, which invokes browsers to access and exfiltrate stored browser data.
The detection rule monitors for access to files commonly used to store sensitive information, including .git-credentials, .netrc, and .gitconfig files configured to store credentials. Accessing these files is a common indicator of credential theft attempts by adversaries looking for plaintext authentication data to pivot to other systems or services.
Detects instances where AI coding assistants or tools (e.g., Claude, Copilot, Gemini) invoke git.exe to clone or fetch repositories from non-standard (non-GitHub) hosts, followed by a checkout operation within a short timeframe. This behavior may indicate an attacker using automated tools to stage or exfiltrate sensitive code repositories to unauthorized infrastructure.
Detects non-browser processes performing file operations (create, modify, rename) on known web browser credential storage, configuration files, and cryptocurrency wallet artifacts in a short duration. This behavior is indicative of credential harvesting or information stealing activity often associated with malware like Vidar, which stages these sensitive files for exfiltration.
This rule detects processes accessing sensitive credential, configuration, or environment files typically stored in locations like .aws, .ssh, .kube, or application user directories. It alerts when a non-standard or unexpected process touches multiple sensitive folders, or when specific suspicious process names or known malicious tools (such as 'NeedleStealer') are observed interacting with these files. This is indicative of potential credential harvesting or reconnaissance activities.
Detects AI agent tooling (e.g., LangChain, Copilot, Claude Code) performing read operations on databases containing sensitive data patterns, or performing suspicious bulk data enumeration. The rule monitors Azure SQL Database security audit events for suspicious application name signatures and non-schema introspective queries.
Detects AI agent tooling (e.g., LangChain, Copilot, Claude Code) performing read operations on databases containing sensitive data patterns, or performing suspicious bulk data enumeration. The rule monitors Azure SQL Database security audit events for suspicious application name signatures and non-schema introspective queries.
Detects network beaconing activity associated with the Casbaneiro (Metamorfo) banking trojan. The rule monitors for specific C2 communication patterns, including the 'client;' string and the 'VVx-4.3' version marker, which are characteristic of this malware's host reconnaissance and command-and-control exfiltration phase.
Detects file operations (open, read, write, rename) involving common sensitive credential files (e.g., .kdbx password databases, .ovpn and .pcf configuration files, .pem, and .ssh directory contents) by processes not authorized to interact with these files.
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