Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
226 detections
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Detects post-exploitation activities associated with FortiMail vulnerability CVE-2026-104286 (FG-IR-26-175). The rule identifies potential unauthorized persistence and data exfiltration, including the creation or modification of archive accounts with remote destinations, execution of 'migadmin' via root cron, and specific admin logout anomalies.
Detects when a user performs a bulk data download (large volume of data) shortly after accessing restricted or sensitive SAP objects such as sensitive tables, ABAP programs, or transactions.
Detects outbound network traffic associated with the Azazel operation, including connections to identified C2 infrastructure, open directory/upload listeners, domain-specific staging servers, and exfiltration attempts to MEGA cloud storage endpoints.
Detects outbound network traffic associated with the Azazel operation, including connections to identified C2 infrastructure, open directory/upload listeners, domain-specific staging servers, and exfiltration attempts to MEGA cloud storage endpoints.
Detects outbound network traffic associated with the Azazel operation, including connections to identified C2 infrastructure, open directory/upload listeners, domain-specific staging servers, and exfiltration attempts to MEGA cloud storage endpoints.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
Detects outbound network traffic associated with the Azazel operation, including connections to identified C2 infrastructure, open directory/upload listeners, domain-specific staging servers, and exfiltration attempts to MEGA cloud storage endpoints.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
Detects a suspicious pattern where an AI coding agent or user creates a public GitHub repository with specific naming conventions (e.g., 'sweeper-demo', 'gitshot-images') and subsequently pushes content containing sensitive terms like 'screenshot' within a short timeframe. This behavior is indicative of potential data exfiltration of internal development screenshots to a public repository.
Detects anomalous Salesforce API export patterns where a user or connected application maintains a daily record export volume below typical burst thresholds, yet accumulates a high total volume over a rolling 7-day period. This pattern mimics 'low-and-slow' data exfiltration techniques used by actors like ShinyHunters to evade standard volume-based DLP alerts.
Detects high-frequency requests directed at common model-serving API endpoints, which may indicate an attacker attempting to perform model extraction or model inversion attacks by repeatedly querying the model to infer its parameters or training data.
Detects a specific command and control (C2) communication pattern characteristic of RatHat malware, involving the retrieval of HTML overlay templates followed by the submission of captured form or credential data from the same device within a one-hour window.
Detects unauthorized export and outbound exfiltration of MikroTik RouterOS .rif diagnostic files. These files contain sensitive device configuration data and are often targeted during the final stage of the 'MikroTrick' attack chain, following administrative credential compromise.
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.
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.
Detects activity associated with the Sauron Loader malware, specifically monitoring for the execution of rnpkeys.exe or loading of tdwp.dll, followed by a high volume of HTTPS POST requests to the same destination host, which is characteristic of chunked screenshot exfiltration.
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.
Detects high-frequency, automated interactions with popular AI assistant web interfaces (e.g., Gemini, Perplexity, Claude, Copilot) from common browser processes. This pattern often indicates unauthorized automated data submission or scraping, which may follow or facilitate automated exfiltration of browser-resident data.
Detects high-frequency, automated interactions with popular AI assistant web interfaces (e.g., Gemini, Perplexity, Claude, Copilot) from common browser processes. This pattern often indicates unauthorized automated data submission or scraping, which may follow or facilitate automated exfiltration of browser-resident data.
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