Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule identifies potential infections associated with the MacSync malware family by matching process, file, and network indicators (hashes, malicious domains, and specific payload URLs) against endpoint telemetry. It monitors for execution of known malicious binaries, file creation events, and communication with identified command-and-control infrastructure.
This rule identifies potential infections associated with the MacSync malware family by matching process, file, and network indicators (hashes, malicious domains, and specific payload URLs) against endpoint telemetry. It monitors for execution of known malicious binaries, file creation events, and communication with identified command-and-control infrastructure.
Detects coordinated activity patterns across multiple distinct tenant environments within a one-hour window. The rule looks for shared infrastructure (C2 domains), malicious tool hashes, or specific agentic-CLI command line signatures (e.g., related to orchestration, reconnaissance, or exfiltration) that suggest an AI-orchestrated actor or automated attack campaign affecting multiple organizations simultaneously.
This rule detects activities associated with the MacSync backdoor, specifically focusing on persistence mechanisms such as LaunchAgent manipulation, .repair-run scripts, and suspicious GIT hook usage. It also correlates these events with the termination of macOS notification agents (BTMNotificationAgent, NotificationCenter, BackgroundTaskManagementAgent), which is a tactic used to suppress security alerts during malicious operations.
Detects instances where AI developer tools or IDE assistants (e.g., Claude, Copilot, Cursor) execute data collection commands (such as directory traversal or archiving) followed by or concurrent with the bulk access of sensitive files (credentials, configuration files) or personal user data.
Detects anomalous activity where AI assistant processes (e.g., Claude, ChatGPT, GitHub Copilot) execute a high volume of diverse discovery commands. The rule correlates multiple discovery categories, such as account, network service, system information, and network configuration discovery, occurring within a short timeframe to identify potential abuse of AI-integrated development tools for host reconnaissance.
Detects anomalous activity where AI assistant processes (e.g., Claude, ChatGPT, GitHub Copilot) execute a high volume of diverse discovery commands. The rule correlates multiple discovery categories, such as account, network service, system information, and network configuration discovery, occurring within a short timeframe to identify potential abuse of AI-integrated development tools for host reconnaissance.
Detects anomalous activity where AI assistant processes (e.g., Claude, ChatGPT, GitHub Copilot) execute a high volume of diverse discovery commands. The rule correlates multiple discovery categories, such as account, network service, system information, and network configuration discovery, occurring within a short timeframe to identify potential abuse of AI-integrated development tools for host reconnaissance.
Detects anomalous, rapid enumeration of system, network, process, and software configuration settings by AI coding assistant processes (e.g., Claude, Cursor, ChatGPT). This behavior often signifies an AI agent being coerced or configured to perform environment discovery or credential gathering, indicating potential compromise or abuse of the LLM-integrated development environment.
This rule detects potential AI prompt injection attacks delivered via email. It identifies users clicking external links directed at AI platforms (like OpenAI, Claude, or Perplexity) that contain suspicious URL parameters or instructions designed to manipulate or override the AI's standard behavior. It correlates the web request with a subsequent process execution on the host that includes suspicious keywords in the command line, suggesting an attempt to automate or exploit the AI assistant through local system interaction.
This rule detects potential AI prompt injection attacks delivered via email. It identifies users clicking external links directed at AI platforms (like OpenAI, Claude, or Perplexity) that contain suspicious URL parameters or instructions designed to manipulate or override the AI's standard behavior. It correlates the web request with a subsequent process execution on the host that includes suspicious keywords in the command line, suggesting an attempt to automate or exploit the AI assistant through local system interaction.
This rule detects potential AI prompt injection attacks delivered via email. It identifies users clicking external links directed at AI platforms (like OpenAI, Claude, or Perplexity) that contain suspicious URL parameters or instructions designed to manipulate or override the AI's standard behavior. It correlates the web request with a subsequent process execution on the host that includes suspicious keywords in the command line, suggesting an attempt to automate or exploit the AI assistant through local system interaction.
Detects potential supply chain or local configuration tampering involving the Model Context Protocol (MCP). The rule identifies modification of common MCP configuration files, followed by the execution of a tool runner (node, python, npx) that spawns a scripting shell, which subsequently initiates an unusual outbound network connection.
Detects potential supply chain or local configuration tampering involving the Model Context Protocol (MCP). The rule identifies modification of common MCP configuration files, followed by the execution of a tool runner (node, python, npx) that spawns a scripting shell, which subsequently initiates an unusual outbound network connection.
Detects potential supply chain or local configuration tampering involving the Model Context Protocol (MCP). The rule identifies modification of common MCP configuration files, followed by the execution of a tool runner (node, python, npx) that spawns a scripting shell, which subsequently initiates an unusual outbound network connection.
Detects anomalous discovery behavior originating from known AI agent and development tools (e.g., Claude, Cursor, Aider). The rule monitors for a rapid sequence of commands spanning multiple categories of enumeration (Network, File/Directory, Software, and System) within a short timeframe, which indicates an attempt to map the runtime environment, permissions, or system capabilities.
Detects anomalous discovery behavior originating from known AI agent and development tools (e.g., Claude, Cursor, Aider). The rule monitors for a rapid sequence of commands spanning multiple categories of enumeration (Network, File/Directory, Software, and System) within a short timeframe, which indicates an attempt to map the runtime environment, permissions, or system capabilities.
Detects anomalous discovery behavior originating from known AI agent and development tools (e.g., Claude, Cursor, Aider). The rule monitors for a rapid sequence of commands spanning multiple categories of enumeration (Network, File/Directory, Software, and System) within a short timeframe, which indicates an attempt to map the runtime environment, permissions, or system capabilities.
Detects anomalous AI-assisted activity where multiple processes (specifically AI developer tools) appear to be chaining operations by writing to and reading from shared local directories (e.g., 'handoff', 'artifacts') followed by network requests to known AI service APIs. This pattern may indicate automated task chaining or unauthorized exfiltration of context/code to AI platforms.
Detects anomalous AI-assisted activity where multiple processes (specifically AI developer tools) appear to be chaining operations by writing to and reading from shared local directories (e.g., 'handoff', 'artifacts') followed by network requests to known AI service APIs. This pattern may indicate automated task chaining or unauthorized exfiltration of context/code to AI platforms.
Detects anomalous AI-assisted activity where multiple processes (specifically AI developer tools) appear to be chaining operations by writing to and reading from shared local directories (e.g., 'handoff', 'artifacts') followed by network requests to known AI service APIs. This pattern may indicate automated task chaining or unauthorized exfiltration of context/code to AI platforms.
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