Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
226 detections
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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.
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 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.
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 instances where the outbound byte count to external (non-private) IP addresses for a source host significantly exceeds its historical hourly baseline. This detection is designed to identify potential data exfiltration stages occurring after system or network compromise.
Detects instances where the outbound byte count to external (non-private) IP addresses for a source host significantly exceeds its historical hourly baseline. This detection is designed to identify potential data exfiltration stages occurring after system or network compromise.
Detects anomalous, large-scale cloning or mirroring of code repositories combined with targeted access to infrastructure-as-code configuration files (e.g., .tfvars, .tfstate) or automated exfiltration of CI/CD build artifacts. This behavior is indicative of an adversary attempting to exfiltrate proprietary source code or sensitive secrets stored within repositories.
Detects instances where the outbound byte count to external (non-private) IP addresses for a source host significantly exceeds its historical hourly baseline. This detection is designed to identify potential data exfiltration stages occurring after system or network compromise.
Detects instances where the outbound byte count to external (non-private) IP addresses for a source host significantly exceeds its historical hourly baseline. This detection is designed to identify potential data exfiltration stages occurring after system or network compromise.
Detects instances where the outbound byte count to external (non-private) IP addresses for a source host significantly exceeds its historical hourly baseline. This detection is designed to identify potential data exfiltration stages occurring after system or network compromise.
Detects a sequence of events starting with a device code OAuth sign-in, followed within two hours by the creation or modification of inbox rules and high-frequency enumeration of email items (contacts, folders, messages, threads, people) via Microsoft Graph API. This behavior is indicative of post-compromise activity where an adversary uses OAuth device code grants to maintain access and subsequently automates inbox analysis or data harvesting.
This rule detects potential bulk data exfiltration by monitoring for high volumes of file access events (FileAccessed, FileRead, FileModified) originating from the 'doc_helper.aspx' file-management web shell. It summarizes activity by device and user account, flagging instances where over 100 unique files are touched within a short timeframe, which is indicative of automated collection and exfiltration activities.
Detects a sequence of events starting with a device code OAuth sign-in, followed within two hours by the creation or modification of inbox rules and high-frequency enumeration of email items (contacts, folders, messages, threads, people) via Microsoft Graph API. This behavior is indicative of post-compromise activity where an adversary uses OAuth device code grants to maintain access and subsequently automates inbox analysis or data harvesting.
Detects anomalous, large-scale cloning or mirroring of code repositories combined with targeted access to infrastructure-as-code configuration files (e.g., .tfvars, .tfstate) or automated exfiltration of CI/CD build artifacts. This behavior is indicative of an adversary attempting to exfiltrate proprietary source code or sensitive secrets stored within repositories.
Detects anomalous, large-scale cloning or mirroring of code repositories combined with targeted access to infrastructure-as-code configuration files (e.g., .tfvars, .tfstate) or automated exfiltration of CI/CD build artifacts. This behavior is indicative of an adversary attempting to exfiltrate proprietary source code or sensitive secrets stored within repositories.
This rule detects unauthorized or suspicious automated scraping behavior by identifying periodic, non-interactive tasks that perform high-volume outbound network requests to multiple external hosts. It specifically filters for processes launched via scheduling mechanisms (Task Scheduler, cron) that exhibit indicators of anti-bot or proxy-rotation bypass techniques (such as usage of headless browser tools or proxy-related keywords in command lines). The rule is tuned to ignore legitimate system maintenance, update, and monitoring processes.
This rule detects unauthorized or suspicious automated scraping behavior by identifying periodic, non-interactive tasks that perform high-volume outbound network requests to multiple external hosts. It specifically filters for processes launched via scheduling mechanisms (Task Scheduler, cron) that exhibit indicators of anti-bot or proxy-rotation bypass techniques (such as usage of headless browser tools or proxy-related keywords in command lines). The rule is tuned to ignore legitimate system maintenance, update, and monitoring processes.
This rule detects unauthorized or suspicious automated scraping behavior by identifying periodic, non-interactive tasks that perform high-volume outbound network requests to multiple external hosts. It specifically filters for processes launched via scheduling mechanisms (Task Scheduler, cron) that exhibit indicators of anti-bot or proxy-rotation bypass techniques (such as usage of headless browser tools or proxy-related keywords in command lines). The rule is tuned to ignore legitimate system maintenance, update, and monitoring processes.
Detects anomalous, large-scale cloning or mirroring of code repositories combined with targeted access to infrastructure-as-code configuration files (e.g., .tfvars, .tfstate) or automated exfiltration of CI/CD build artifacts. This behavior is indicative of an adversary attempting to exfiltrate proprietary source code or sensitive secrets stored within repositories.
Detects anomalous, large-scale cloning or mirroring of code repositories combined with targeted access to infrastructure-as-code configuration files (e.g., .tfvars, .tfstate) or automated exfiltration of CI/CD build artifacts. This behavior is indicative of an adversary attempting to exfiltrate proprietary source code or sensitive secrets stored within repositories.
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