Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
379 detections
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Detects outbound network traffic to Microsoft Graph API originating from a user-agent string identified as 'python-httpx'. This behavior is characteristic of automated scripts or malicious tools using Python libraries to interact with Microsoft cloud services, potentially for data exfiltration or unauthorized API interaction.
Detects Node.js processes executing system-level reconnaissance (e.g., CPU, memory, hostname, uptime enumeration) followed by network connections to known exfiltration endpoints (Slack, Telegram API). This behavior aligns with identified npm malware campaigns using host fingerprinting for target selection and data exfiltration.
Detects a suspicious pattern where a user browser visits a Google Document and subsequently initiates a connection to the Telegram Bot API within a short timeframe (10 minutes). This sequence is indicative of malicious Google Apps Scripts embedded in documents attempting to exfiltrate victim information (IP, geolocation, device details) as part of a reconnaissance phase before a ClickFix-style payload delivery.
Detects a process accessing browser credential files (Login Data) followed by an outbound network connection to a suspicious exfiltration endpoint commonly associated with credential-stealing malware.
Detects instances where AI coding assistants or tools (e.g., Claude, Copilot, Gemini) invoke git.exe to clone or fetch repositories from non-standard (non-GitHub) hosts, followed by a checkout operation within a short timeframe. This behavior may indicate an attacker using automated tools to stage or exfiltrate sensitive code repositories to unauthorized infrastructure.
Detects individual file access events performed by non-browser python processes. This rule is designed as a proxy to identify automated file collection activities, such as those performed by 'python-httpx', that may be indicative of low-volume data exfiltration when aggregated over time.
Detects network activity associated with the Rapuncel malware, including raw TCP exfiltration of files via POST requests to /upload, connections to known malicious C2 infrastructure, and repeated exfiltration retry patterns.
Detects outbound SMTP communication to Gmail servers containing specific indicators of compromise (sender, recipient, or subject line) associated with the LucidKnight (LucidRook) malware campaign, indicating potential reconnaissance data exfiltration.
This rule detects network upload operations (HTTP POST or similar requests ending in '/upload') to a specific external IP address (2.26.126.50). It specifically monitors for processes other than common web browsers, and filters for connections where significant data volume (greater than 10KB) is transferred or the data size is unknown.
This rule detects the execution of the Windows FTP client (ftp.exe) using a command script file (via the -s: parameter). It monitors for process creation of ftp.exe with this flag, excluding known legitimate paths. It then correlates this with any child processes initiated by the FTP client to identify potential data exfiltration or automated file transfers.
Detects outbound HTTP/POST requests from checkout or payment page environments to known malicious domains associated with GrelosGTM digital skimmer infrastructure. This behavior is indicative of unauthorized data exfiltration, where payment information is sent to an attacker-controlled server instead of the legitimate payment gateway.
Detects instances where a service principal or entity identified as an AI agent, bot, or assistant accesses files in SharePoint or OneDrive that are likely to contain sensitive credentials, secrets, or configuration files (e.g., .pem, .pfx, .env, id_rsa).
This rule detects network upload operations (HTTP POST or similar requests ending in '/upload') to a specific external IP address (2.26.126.50). It specifically monitors for processes other than common web browsers, and filters for connections where significant data volume (greater than 10KB) is transferred or the data size is unknown.
This rule detects network upload operations (HTTP POST or similar requests ending in '/upload') to a specific external IP address (2.26.126.50). It specifically monitors for processes other than common web browsers, and filters for connections where significant data volume (greater than 10KB) is transferred or the data size is unknown.
Detects instances where AI coding assistants or tools (e.g., Claude, Copilot, Gemini) invoke git.exe to clone or fetch repositories from non-standard (non-GitHub) hosts, followed by a checkout operation within a short timeframe. This behavior may indicate an attacker using automated tools to stage or exfiltrate sensitive code repositories to unauthorized infrastructure.
Detects instances where AI coding assistants or tools (e.g., Claude, Copilot, Gemini) invoke git.exe to clone or fetch repositories from non-standard (non-GitHub) hosts, followed by a checkout operation within a short timeframe. This behavior may indicate an attacker using automated tools to stage or exfiltrate sensitive code repositories to unauthorized infrastructure.
Detects instances where AI coding assistants or tools (e.g., Claude, Copilot, Gemini) invoke git.exe to clone or fetch repositories from non-standard (non-GitHub) hosts, followed by a checkout operation within a short timeframe. This behavior may indicate an attacker using automated tools to stage or exfiltrate sensitive code repositories to unauthorized infrastructure.
Detects a suspicious sequence of Git operations ('fetch' followed by 'checkout') executed within a 5-minute window, where the initiating process is a known AI-assisted development tool (Claude, Codex, Copilot, or Gemini). This behavioral pattern may indicate an attempt to use AI tooling to facilitate the unauthorized access or exfiltration of code repositories.
Detects anomalous data transfer patterns characteristic of exfiltration by identifying a high frequency of relatively small, uniform data chunks sent to an external destination. By aggregating these chunks, the rule identifies cumulative data transfers that exceed the defined organizational threshold for a single connection, potentially bypassing security controls that monitor only individual file transfer sizes.
This rule detects a multi-stage attack pattern originating from a web-based service. It identifies a web application process (like w3wp.exe, httpd.exe, or java.exe) spawning suspicious child processes (cmd.exe, powershell.exe, etc.), followed by the creation of new files and external network connections. This chain of behavior is highly indicative of web shell exploitation, post-exploitation command execution, and data exfiltration or C2 communication.
Detects network connections to command-and-control infrastructure associated with the JeetBot/Twitch Enhanced Viewer malicious browser extension. The rule triggers on connections to known malicious domains or IPs, as well as specific API endpoints on ambiguous domains that are used for exfiltrating Twitch OAuth tokens.
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