Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects instances where a non-browser process initiates network connections to multiple AI service providers and Discord within a 15-minute correlation window, potentially indicating automated data exfiltration or unauthorized interaction with AI platforms using tools other than standard web browsers.
Detects DNS queries to major public LLM API endpoints (OpenAI, Anthropic, Google Generative Language). This may indicate potential use of these services as a command and control (C2) channel or for data exfiltration by insiders or compromised systems, which may bypass traditional security controls.
Detects Node.js processes establishing network connections to common webhook relay and collection services, which may indicate data exfiltration or credential theft.
Detects potential exfiltration of sensitive DevOps credentials or configuration files (such as AWS credentials, SSH keys, Kubernetes configs, or cryptocurrency wallets) by monitoring for file access events followed by outbound network connections to known exfiltration channels like Slack or specific C2 infrastructure within a 30-minute window.
Detects the use of data archival utilities (7zip, WinRAR) or command-line cloud synchronization tools (Rclone) to stage files in common temporary directories, immediately followed by network connections to known cloud storage endpoints. This pattern is characteristic of pre-encryption exfiltration activities often seen in ransomware campaigns.
Detects large HTTP POST requests (exceeding 100KB) directed to public LLM API endpoints such as OpenAI or Anthropic. This behavior may indicate potential bulk data exfiltration or unauthorized sharing of sensitive data with external AI providers.
Detects instances where a non-browser process initiates connections to commercial LLM provider APIs (DeepSeek, OpenRouter, Mistral) followed by a connection to Discord CDN/Webhook endpoints within a 15-minute window. This behavior is indicative of a process acting as an autonomous C2 agent that exfiltrates data after receiving instructions or processing information via an LLM.
Detects a non-browser process initiating network connections to four distinct commercial LLM API providers (DeepSeek, OpenRouter, Mistral, and Google Gemini) within a 5-minute interval. This behavior is indicative of a C2 pattern where an adversary uses multiple LLM backends to perform plurality-vote or redundant queries, bypassing typical browser-based AI aggregation services.
Detects the installation of unauthorized AI-related browser extensions followed by outbound network communication from the browser process to known external AI service API endpoints within one hour. This behavior is indicative of potential data exfiltration via shadow AI tools, bypassing standard enterprise DLP controls.
Detects anomalous DNS queries that utilize long labels or non-standard record types (TXT, NULL, CNAME) which are frequently associated with DNS tunneling and command-and-control communication. The rule flags endpoints with a high volume of these suspicious requests or exceptionally long domain labels.
Detects the execution of the 'gitshot' binary, which is known to automatically create public repositories and release tags to host screenshots or screen recordings. This behavior can be abused by insiders or attackers to exfiltrate sensitive data outside of corporate control.
Detects the execution of the 'gitshot' binary, which is known to automatically create public repositories and release tags to host screenshots or screen recordings. This behavior can be abused by insiders or attackers to exfiltrate sensitive data outside of corporate control.
Detects the execution of the 'gitshot' binary, which is known to automatically create public repositories and release tags to host screenshots or screen recordings. This behavior can be abused by insiders or attackers to exfiltrate sensitive data outside of corporate control.
Detects the creation or exposure of public repositories, releases, or gists under a personal GitHub account that utilize naming conventions associated with the 'PixelLeak' workaround. This technique allows adversaries to make previously private screenshots, recordings, or internal assets publicly accessible by hosting them in public personal repositories or releases.
Detects the creation or exposure of public repositories, releases, or gists under a personal GitHub account that utilize naming conventions associated with the 'PixelLeak' workaround. This technique allows adversaries to make previously private screenshots, recordings, or internal assets publicly accessible by hosting them in public personal repositories or releases.
Detects the creation or exposure of public repositories, releases, or gists under a personal GitHub account that utilize naming conventions associated with the 'PixelLeak' workaround. This technique allows adversaries to make previously private screenshots, recordings, or internal assets publicly accessible by hosting them in public personal repositories or releases.
This rule detects potentially malicious network activity by monitoring for specific query parameters ('bmodule', 'smodule', 'lmodule', 'task', 'upload.php') in web requests or communication with a specific known-malicious IP address ('62.60.226.50'). This pattern is characteristic of C2 (Command and Control) traffic or unauthorized file staging/upload operations.
Detects anomalous POST requests to the RubyGems webhook API endpoint (/api/v1/web_hooks) containing specific indicators ('A000' or 'ZZEND') in the request body, which suggests the misuse of legitimate webhooks for chunked data exfiltration.
Detects anomalous, high-volume file download or export activity originating from a single identity across multiple downstream cloud applications or tenants within a one-hour window. This behavior is indicative of a compromised shared service provider, MSP, or clearinghouse account being leveraged to exfiltrate data from multiple client environments simultaneously. The detection excludes pre-approved service accounts and relies on a dynamic baseline comparison rather than a static threshold.
Detects anomalous, high-volume file download or export activity originating from a single identity across multiple downstream cloud applications or tenants within a one-hour window. This behavior is indicative of a compromised shared service provider, MSP, or clearinghouse account being leveraged to exfiltrate data from multiple client environments simultaneously. The detection excludes pre-approved service accounts and relies on a dynamic baseline comparison rather than a static threshold.
Detects anomalous, high-volume file download or export activity originating from a single identity across multiple downstream cloud applications or tenants within a one-hour window. This behavior is indicative of a compromised shared service provider, MSP, or clearinghouse account being leveraged to exfiltrate data from multiple client environments simultaneously. The detection excludes pre-approved service accounts and relies on a dynamic baseline comparison rather than a static threshold.
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