Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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
This rule detects scenarios where an AI agent executes shell commands or system-level tools immediately after receiving a prompt injection attack from an untrusted source. It correlates agent audit logs—specifically identifying tool calls flagged for prompt injection—with actual endpoint process creation events. It flags process execution as 'High' severity if the command line matches common malicious patterns like encoded commands, IEX, or attempts to access credential material (e.g., lsass, mimikatz).
This rule detects scenarios where an AI agent executes shell commands or system-level tools immediately after receiving a prompt injection attack from an untrusted source. It correlates agent audit logs—specifically identifying tool calls flagged for prompt injection—with actual endpoint process creation events. It flags process execution as 'High' severity if the command line matches common malicious patterns like encoded commands, IEX, or attempts to access credential material (e.g., lsass, mimikatz).
Detects reconnaissance behavior (System Information, Account, and Network Discovery) performed by processes associated with AI and LLM coding assistants. The rule monitors for a chain of suspicious activities, such as executing discovery commands or excessive network scanning within a 10-minute window, identifying potential misuse of developer-oriented agent tools for post-exploitation reconnaissance.
Detects reconnaissance behavior (System Information, Account, and Network Discovery) performed by processes associated with AI and LLM coding assistants. The rule monitors for a chain of suspicious activities, such as executing discovery commands or excessive network scanning within a 10-minute window, identifying potential misuse of developer-oriented agent tools for post-exploitation reconnaissance.
Detects the creation of a new local account that is immediately followed by (or associated with) execution from a process identified as an AI development or agent-based tool (e.g., Cursor, GitHub Copilot, Ollama). The rule correlates process creation events with Windows Security Event ID 4720 (User account created) and optionally checks for subsequent addition to the local Administrators group. This logic aims to surface potential unauthorized credential creation triggered by automated AI coding assistants or LLM-driven workflows.
Detects the creation of a new local account that is immediately followed by (or associated with) execution from a process identified as an AI development or agent-based tool (e.g., Cursor, GitHub Copilot, Ollama). The rule correlates process creation events with Windows Security Event ID 4720 (User account created) and optionally checks for subsequent addition to the local Administrators group. This logic aims to surface potential unauthorized credential creation triggered by automated AI coding assistants or LLM-driven workflows.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects 'unshare' creating a user/mount/net namespace by a non-root user, outside
sanctioned container and sandbox runtimes. This is the shared precursor across CIFSwitch,
OVSwrap, pedit COW and container escapes. Enriched with a namespace-flag decode, an
exploit-family ProfileHint (user+net = OVSwrap shape, user+mount = CIFSwitch/overlay
shape), sandbox-lineage FP suppression, RiskScore / TriagePriority and DeviceInfo context.
Treat as a precursor/triage signal - MDE cannot tie unshare to the subsequent root
transition (no uid_change event). Detection logic adapted from Elastic Security Labs.
sanctioned container and sandbox runtimes. This is the shared precursor across CIFSwitch,
OVSwrap, pedit COW and container escapes. Enriched with a namespace-flag decode, an
exploit-family ProfileHint (user+net = OVSwrap shape, user+mount = CIFSwitch/overlay
shape), sandbox-lineage FP suppression, RiskScore / TriagePriority and DeviceInfo context.
Treat as a precursor/triage signal - MDE cannot tie unshare to the subsequent root
transition (no uid_change event). Detection logic adapted from Elastic Security Labs.
Detects a shell launched with the privilege-preserving -p flag from a location outside the
standard binary directories - the execution step that converts a SUID-root shell copy into
an interactive root shell. Pairs directly with OS-DET-LNX-005 (see correlation
OS-DET-LNX-007). Detection logic adapted from Elastic Security Labs.
standard binary directories - the execution step that converts a SUID-root shell copy into
an interactive root shell. Pairs directly with OS-DET-LNX-005 (see correlation
OS-DET-LNX-007). Detection logic adapted from Elastic Security Labs.
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 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 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 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 process command lines containing LLM tool-calling syntax (e.g., 'tool_call', 'function_call') in conjunction with keywords associated with offensive security capabilities (e.g., 'exploit', 'mimikatz', 'exfiltrate'). This pattern is consistent with the behavior of malicious LLM-based agents attempting to autonomously invoke and execute offensive tasks.
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