Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the use of legitimate data transfer utilities (s5cmd, rclone) configured to exfiltrate sensitive document types (.doc, .pdf, .sql, etc.) to S3-compatible cloud storage. The rule correlates this activity with recent access to files containing sensitive credentials or configuration data, a pattern observed in double-extortion campaigns.
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 HTTP and TLS traffic patterns indicative of data exfiltration to Slack, specifically targeting hardcoded Slack API tokens, Slack channel IDs in HTTP request bodies, or traffic directed to hooks.slack.com, commonly associated with malicious npm packages.
Detects high-volume file access or download operations in SharePoint or OneDrive performed by common scripting libraries and client user agents, which may indicate automated data exfiltration.
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 Node.js process pattern characterized by the execution of 'sharedLoad.min.js' or detached child processes, followed by rapid outbound network communication to known messaging platforms (Slack, Telegram). This behavior is indicative of the indexed-btree malware family, which exfiltrates host fingerprint data including architecture, hostname, and system metrics.
Detects HTTP POST requests containing potential credential exfiltration indicators, specifically targeting the '/google.php' endpoint with 'do=login&', 'user=', and 'pass=' parameters. This pattern is associated with FlowerStorm (Storm-1167) AiTM phishing kits.
Detects large and sustained outbound TCP data transfers to external networks, which may indicate mass data exfiltration. The rule monitors established sessions and uses a threshold to identify unusual outbound traffic volume over an extended period.
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 network communication to specific API endpoints associated with the RatHat Android malware, which leverages accessibility services for overlay-based credential harvesting and data exfiltration.
Detects outbound network connections to known JWR phishing framework endpoints. The rule identifies suspicious interaction sequences, including initial beaconing via 'addClick' or 'getSyncSettings' followed by exfiltration of sensitive data (payment card info, OTPs) through POST requests to 'the_final_interface' or 'addCvv', or via a specific WebSocket channel pattern.
Detects rapid mass-cloning or downloading of private GitHub repositories shortly after an OAuth token authorization, originating from IP addresses or countries not previously associated with the user account. This behavior is indicative of unauthorized bulk data extraction using compromised credentials, consistent with threat actor activity (e.g., TeamPCP/UNC6780).
Detects rapid mass-cloning or downloading of private GitHub repositories shortly after an OAuth token authorization, originating from IP addresses or countries not previously associated with the user account. This behavior is indicative of unauthorized bulk data extraction using compromised credentials, consistent with threat actor activity (e.g., TeamPCP/UNC6780).
Detects instances where a removed GitHub organization member performed bulk repository access (cloning or downloading) shortly before their removal, potentially indicating exploitation of retained access by a departed employee or an adversary using a valid account before deprovisioning. The rule identifies activity occurring more than 24 hours prior to removal involving multiple repositories or multiple access actions.
Detects rapid, bulk cloning or downloading of GitHub repositories containing sensitive data (e.g., consensus algorithms, PII, internal blocklists). This behavior is characteristic of unauthorized data exfiltration, specifically tracking activity involving suspicious OAuth token prefixes (gho_) and rapid high-volume access to sensitive organizational assets within a short timeframe.
This rule detects anomalies in outbound network data transfer by comparing the current daily volume of data sent to external, non-private IP addresses against the historical average for those specific sources. It alerts when the daily volume exceeds 500MB and shows a significant spike (3x or more) compared to historical baseline data or when no historical data is available for a new source connection.
This rule monitors for high-frequency download or access events of files associated with AI development, such as model weights (.pt, .ckpt, .safetensors, .h5, .onnx, .pkl, .pb), checkpoints, and research datasets from cloud storage. It triggers when an account accesses or downloads 20 or more unique AI-related files within a 60-minute window, which is indicative of unauthorized data exfiltration of intellectual property.
Detects the transmission of sensitive credential-like patterns (such as API keys, private keys, and connection strings) to known external AI coding assistant and IDE plugin endpoints. This rule monitors cloud application events originating from developer workstations or CI/CD runners where such sensitive information may be inadvertently or maliciously sent to third-party AI services.
This rule detects malicious activity associated with the Bazinga macOS backdoor, specifically focusing on the clearing of TCC privacy permissions, the staging of keychain and browser data using 'ditto', and the subsequent exfiltration of this data to a remote host via 'curl'.
Detects an anomalous burst of git clone, fetch, or download events performed by a single GitHub user account against a large number of private repositories within a short timeframe (15-minute window). This pattern often indicates unauthorized bulk collection or exfiltration of sensitive source code by an actor using a valid account.
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