Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects HTTP POST requests to a specific API endpoint (/api/adbk/upload) containing references to 'cert.pem' and 'private.key', indicating the exfiltration of sensitive cryptographic material by the RatHat malware.
Detects anomalous data exfiltration activity to Dropbox API, specifically utilizing a naming convention in the file path ('_lns/') often associated with Kimsuky threat actor tools. The rule monitors HTTP POST requests to content.dropboxapi.com with characteristic 'Dropbox-API-Arg' header values and TLS SNI indicators.
This rule monitors for PowerShell commands that combine common data collection flags ('-Recurse', 'Documents') with 'Invoke-WebRequest' to perform a POST method upload. This pattern is commonly associated with the staging and exfiltration of sensitive files from local directories to a remote destination via HTTP/S.
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 network traffic associated with Hannibal Stealer, including communication with known hardcoded C2 infrastructure, suspicious URI patterns for data exfiltration, and the use of the Telegram Bot API for potential C2 or exfiltration.
This rule detects potential information stealer activity by identifying the staging of files (.html, .txt, .log) in common user directories like AppData or Temp, combined with process command lines querying sensitive system information (e.g., BIOS, wallets, screen resolution) and network connections to the Telegram API for likely exfiltration.
Detects network communication to specific Gusercontent endpoints consistent with malicious browser extensions exfiltrating captured session tokens or cookies. The rule monitors for POST requests containing parameters like leadId, email, and data, often used to transmit stolen authentication material.
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 internal users submitting potentially sensitive data to public generative AI applications (e.g., ChatGPT, Claude, Gemini) where Microsoft Purview DLP or a comparable inspection service has triggered a policy match. It correlates cloud application outbound signals with content inspection logs to identify high-risk exfiltration events while excluding sanctioned enterprise-licensed AI instances.
This rule detects internal users submitting potentially sensitive data to public generative AI applications (e.g., ChatGPT, Claude, Gemini) where Microsoft Purview DLP or a comparable inspection service has triggered a policy match. It correlates cloud application outbound signals with content inspection logs to identify high-risk exfiltration events while excluding sanctioned enterprise-licensed AI instances.
This rule detects internal users submitting potentially sensitive data to public generative AI applications (e.g., ChatGPT, Claude, Gemini) where Microsoft Purview DLP or a comparable inspection service has triggered a policy match. It correlates cloud application outbound signals with content inspection logs to identify high-risk exfiltration events while excluding sanctioned enterprise-licensed AI instances.
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.
This rule identifies sustained, low-volume activity (low-and-slow) against Microsoft 365 services (SharePoint, OneDrive, and Exchange) which may indicate an adversary is using automated methods via the Graph API to collect sensitive files or emails over an extended period. The rule buckets events by user and time, requiring a minimum of three hours of activity with consistent but low-volume interactions to identify potential data collection behavior that avoids traditional volume-based detection thresholds.
Detects high-volume, potentially automated data access, collection, or staging activities within Microsoft 365 services (SharePoint, OneDrive, and Exchange). The rule flags user identities performing a high count of operations (downloads, accesses, deletions, or export requests) across a wide range of unique objects or sites within a one-hour window, which is indicative of post-compromise mass data collection or exfiltration preparation.
Detects anomalous, high-volume file download activity from SharePoint or OneDrive initiated through identified anonymous proxy services. This behavior suggests potential data exfiltration by an adversary attempting to mask their source IP address while offloading large amounts of data.
This rule detects high-volume interactions with the Exchange Online REST API originated from the 'One Outlook Web' application ID, specifically filtering for events with associated token IDs. This pattern may indicate automated mailbox data exfiltration or unauthorized bulk email access, which has been observed in recent phishing-based identity compromise campaigns.
Detects high-volume, automated access and download activity targeting sensitive data within Microsoft 365 services (SharePoint, OneDrive, and Exchange) using the Microsoft Graph API. The rule identifies suspicious patterns of URI access associated with file downloading and mail item access, which may indicate automated data exfiltration by an adversary using compromised credentials or OAuth tokens.
Detects anomalous, high-volume file access and download activity targeting SharePoint or OneDrive. This behavior is identified by the use of the python-httpx User-Agent, which is often associated with automated scripts for data collection and exfiltration in cloud-based intrusions.
Detects the Comet browser process, typically associated with AI-agent operations, accessing sensitive browser profile files (e.g., History, Bookmarks, Login Data, Cookies) followed by outbound network communication within a 15-minute window, excluding known legitimate paths.
Detects the MovieReaper payload behavior where a specific file manager process masquerading as a legitimate system utility (msedge.exe) performs mass file enumeration or access followed by suspicious outbound network connections, indicative of data staging and exfiltration.
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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