Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
11,847 detections
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Detects the installation or first execution of common remote monitoring and management (RMM) software, specifically AnyDesk, TeamViewer, or ScreenConnect, shortly after an identity change or MFA reset event, indicative of social engineering vishing-to-remote-access techniques often employed by threat actors such as Scattered Spider (UNC3944).
Detects processes attempting to enumerate Active Directory objects (users, groups, trusts) while simultaneously establishing a network connection to Active Directory Web Services (ADWS) on port 9389. This behavior is characteristic of tools like SharpADWS, which performs AD enumeration via the ADWS protocol rather than traditional LDAP, potentially bypassing legacy detection mechanisms.
Detects the use of PowerShell to modify Microsoft Defender exclusion paths by adding root drive letters or temporary directory paths. This behavior is indicative of defense evasion techniques often employed by ransomware families, such as Nova or Gentlemen, to prevent the scanning of files immediately prior to encryption.
Detects attempts to terminate security-related processes (AV/EDR) using common administrative tools including taskkill.exe, wmic.exe, and PowerShell Stop-Process. This behavior is frequently observed during the pre-encryption phase of ransomware attacks, such as Nova and Gentlemen, to evade detection.
Detects attempts to terminate security-related processes (AV/EDR) using common administrative tools including taskkill.exe, wmic.exe, and PowerShell Stop-Process. This behavior is frequently observed during the pre-encryption phase of ransomware attacks, such as Nova and Gentlemen, to evade detection.
Detects the manual enablement of network discovery services (fdrespub, fdPHost, SSDPSRV, upnphost) and the associated Windows Firewall rules. This activity is a known tactic used by Gentlemen ransomware to facilitate lateral movement and network share enumeration by making the compromised host visible to others and allowing it to discover peers.
Detects instances where Node.js, Python, or Go processes spawn command shell interpreters (cmd.exe, powershell.exe, etc.). This behavior is often indicative of C2 command execution by malware, such as the Graphalgo RAT, which may use these languages to execute shell-level commands after initial infection or payload execution.
This rule detects when common web server processes or service accounts spawn unexpected interpreter, shell, or reconnaissance tools, which is a common indicator of post-exploitation activity following a successful Remote Code Execution (RCE) attempt.
Detects a sequence of events where a potential multimedia lure (PDF, image, or media file) is created or downloaded via a web browser or communication client, followed by an AI agent (e.g., ChatGPT, Claude) accessing the file, and subsequently initiating a shell execution process (PowerShell, CMD, etc.) involving suspicious command-line patterns or references to the original file. This behavior is indicative of an AI-assisted prompt injection attack where malicious content is processed and executed through agent tooling.
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.
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.
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.
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 the execution of known Remote Monitoring and Management (RMM) software installers or command-line arguments when invoked by PowerShell processes. This behavior is often indicative of malicious post-compromise activity where an adversary uses legitimate remote access tools to establish persistent command-and-control access.
Detects activity associated with the SharePoint ToolShell exploitation chain, specifically monitoring the w3wp.exe process for spawning suspicious child processes like cmd.exe or powershell.exe, and the creation of unexpected .aspx files within the SharePoint LAYOUTS directory.
Detects instances where the Windows command shell (cmd.exe) is used to execute 'dotnet --list-runtimes' with output redirected to sensitive locations (e.g., NUL, temp folders, or text/log files). This pattern is often observed during post-exploitation activities by RMM agents or unauthorized processes attempting to profile the .NET environment while hiding their command output.
This rule detects potential unauthorized or suspicious installation of an RMM (Remote Monitoring and Management) agent on a Windows host. It flags cases where a process performs multiple suspicious actions within a 24-hour window, specifically combining the installation or starting of a service related to an RMM agent, the creation of a Windows Firewall rule for the RMM agent executable, and/or the creation of log events related to the installer.
The following analytic identifies `node.exe` directly executing a single `.js` script located in the same directory it was launched from, where that directory is an unusual location such as a user profile, PerfLogs, ProgramData, or Temp folder.
Legitimate Node.js application, module, and package manager directories are excluded.
This pattern is commonly associated with malware loaders and second-stage payloads that leverage the Node.js runtime to execute JavaScript outside of a typical development or application context.
Legitimate Node.js application, module, and package manager directories are excluded.
This pattern is commonly associated with malware loaders and second-stage payloads that leverage the Node.js runtime to execute JavaScript outside of a typical development or application context.
This analytic detects the macOS osascript utility executing JavaScript for Automation (JXA) code, identified by
the "-l JavaScript" interpreter flag, with references to the Objective-C bridge. Adversaries may abuse JXA and
Objective-C APIs to interact with macOS applications, access native system functionality, execute commands, or
perform post-exploitation activity.
the "-l JavaScript" interpreter flag, with references to the Objective-C bridge. Adversaries may abuse JXA and
Objective-C APIs to interact with macOS applications, access native system functionality, execute commands, or
perform post-exploitation activity.
Detects potential remote code execution triggered by loading untrusted AI model files (e.g., pickle, .pt, .pth, .ckpt) in Python-based environments. The rule identifies suspicious child process execution or the presence of known dangerous deserialization/payload indicators (like os.system, subprocess, or socket) within the command line of common model-loading applications, suggesting a model-based supply chain compromise.
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