Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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'.
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 exfiltration of sensitive information, including Dahua camera credentials and internal identifiers, to the Telegram Bot API over HTTPS. These rules monitor for specific API endpoints (/sendPhoto) and common credential patterns (Login:, Password:, Model:) or indicators (Shodan links, VKontakte references) within HTTP POST requests directed toward api.telegram.org.
This rule detects the creation of XML files matching the 'export_batch_*.xml' naming convention, which is consistent with the output pattern of the Dahua DeviceManager utility. Attackers often use this tool to export camera configuration files containing sensitive credentials (IP, Port, Username, Password). The rule flags activity where multiple such files are created in a short duration, suggesting potential mass exfiltration of camera credentials.
Detects unauthorized export and outbound exfiltration of MikroTik RouterOS .rif diagnostic files. These files contain sensitive device configuration data and are often targeted during the final stage of the 'MikroTrick' attack chain, following administrative credential compromise.
Detects the execution of the rclone utility (including renamed binaries) using command-line arguments consistent with data exfiltration or synchronization (copy, sync, move) that is followed by network communication to known cloud storage providers commonly abused for staging and exfiltration in ransomware attacks.
Detects network communication to specific Gusercontent endpoints consistent with malicious browser extensions exfiltrating captured session tokens or cookies. The rule monitors for POST requests containing parameters like leadId, email, and data, often used to transmit stolen authentication material.
Detects network communication to specific Gusercontent endpoints consistent with malicious browser extensions exfiltrating captured session tokens or cookies. The rule monitors for POST requests containing parameters like leadId, email, and data, often used to transmit stolen authentication material.
Detects HTTP POST requests directed to the URI path '/api/credentials' over the non-standard TCP port 8133, which is indicative of AMOS (Atomic macOS) Stealer exfiltrating stolen credentials to a C2 server.
Detects HTTP POST requests directed to the URI path '/api/credentials' over the non-standard TCP port 8133, which is indicative of AMOS (Atomic macOS) Stealer exfiltrating stolen credentials to a C2 server.
Detects HTTP and TLS traffic patterns indicative of data exfiltration to Slack, specifically targeting hardcoded Slack API tokens, Slack channel IDs in HTTP request bodies, or traffic directed to hooks.slack.com, commonly associated with malicious npm packages.
This rule monitors outbound TLS traffic to detect communication with public Slack or Telegram webhook APIs, which are commonly abused by malware or malicious Node.js/npm packages for data exfiltration or command-and-control (C2) operations.
Detects a high volume of 'MailItemsAccessed' operations within a short 15-minute window performed by a specific application ID. This behavior is often indicative of automated, bulk exfiltration of emails from a Microsoft 365 environment, potentially by a compromised application or an attacker using a malicious OAuth application.
Detects network communication to specific Gusercontent endpoints consistent with malicious browser extensions exfiltrating captured session tokens or cookies. The rule monitors for POST requests containing parameters like leadId, email, and data, often used to transmit stolen authentication material.
Detects network communication to specific Gusercontent endpoints consistent with malicious browser extensions exfiltrating captured session tokens or cookies. The rule monitors for POST requests containing parameters like leadId, email, and data, often used to transmit stolen authentication material.
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
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-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.
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