Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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 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.
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 UNC6240 PSEMHUB.war web shell/tunnel/trojanized installer artifacts by distinctive content markers (x.jsp, u.jsp, tunnel.jsp/jspx, Ple64.exe)
Detects UNC6240 PSEMHUB.war web shell/tunnel/trojanized installer artifacts by distinctive content markers (x.jsp, u.jsp, tunnel.jsp/jspx, Ple64.exe)
This rule detects unauthorized tampering with AI container images within a container registry. It identifies when existing image tags are overwritten by actors outside of authorized CI/CD pipelines, or when production MLOps infrastructure pulls unsigned or invalid container images, potentially indicating a supply chain attack targeting AI model artifacts.
Detects unauthorized modifications to Group Policy Objects (GPO) involving critical attributes (such as SYSVOL paths or extension configurations) combined with the creation of new script or executable files in the SYSVOL Scripts folder. This behavior is indicative of potential GPO tampering to gain persistence or facilitate large-scale execution, such as ransomware deployment via GPOs.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects modifications to the .zshrc configuration file followed by shell commands interacting with the file, or the execution of commands that source the .repair-run script within a shell environment. This pattern is indicative of potential persistence mechanisms where an adversary modifies startup scripts to maintain access or execute malicious code upon shell initialization.
This rule detects modifications or creation of Git hooks ('pre-commit', 'post-checkout') within local repository directories. Attackers can leverage these hooks as an execution trigger for arbitrary code whenever standard Git actions occur, facilitating persistence or privilege escalation on compromised developer machines.
This rule detects a sequence of events indicative of MacSync credential phishing on macOS. It identifies the tampering of Pluggable Authentication Modules (PAM) configuration files or SecurityAgent plugins (often used to hook authentication processes), immediately followed by the use of osascript to display a spoofed password dialog to the user. This combined activity suggests an adversary is attempting to harvest local user account credentials.
This rule detects modifications to Group Policy Objects (GPOs) that are either explicitly marked as malicious based on known indicators (GUIDs or 'PAYLOAD' strings) or involve unauthorized modifications to domain-root or organizational unit GPO links. It monitors Active Directory security event logs for object changes.
Detects coordinated usage of 'xattr' to manipulate the com.apple.quarantine attribute and 'chmod' to grant executable permissions on sensitive macOS system or library paths. This behavior is indicative of an adversary attempting to bypass Gatekeeper or bypass execution restrictions on malicious payloads placed in system directories.
Detects the creation or modification of script files (bat, ps1, vbs, cmd) within the Windows Startup folder initiated by processes commonly associated with AI agents or developer frameworks (e.g., dotnet, python, w3wp). This activity indicates an AI agent environment being exploited to establish persistence, potentially facilitating delayed remote code execution upon user logon.
This rule detects the creation or modification of browser native messaging host configuration files (e.g., com.lunex.explorer.json) combined with the simultaneous or subsequent termination of common web browsers. This pattern is indicative of potential browser hijacking, where an adversary sets up a malicious native messaging host to facilitate persistence or intercept browser communications and subsequently restarts or terminates the browser process to reload the configuration.
Detects evidence of persistence mechanisms targeting Docker environments on Linux systems. The rule identifies suspicious modifications using crontab, systemctl (service creation/start), and chattr (to make files immutable), specifically monitoring for artifacts related to '.docker-network-monitor' and 'docker-network-resolver'. It flags systems where multiple such configuration changes occur within a single day.
Detects behavior associated with the TeamFiltration backdoor, where a user account downloads a file from OneDrive, modifies or replaces that file, and re-uploads it within a short timeframe (30 minutes). This pattern is consistent with the 'taint shared content' technique used to distribute malicious files through legitimate cloud storage.
This rule detects the creation, modification, or renaming of files within specific sensitive directories under Local AppData (such as ComponentTask33, INetCache/FilterManager, or Shell/RemoteTempPrimary) that are closely timed with the execution of a PowerShell script named '*_scatter.ps1'. This behavior is indicative of potential malicious staging, data dropping, or modular deployment associated with a specific script-based attack chain.
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