Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
236 detections
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Detects HTTP POST requests directed at common Large Language Model (LLM) chat completion and completion endpoints that are not associated with authorized OpenAI or Azure OpenAI services. This behavior may indicate an attempt to exfiltrate potentially sensitive corporate or proprietary data to third-party or unauthorized Shadow-AI platforms.
Detects inbound HTTP POST requests directed at common paths associated with self-hosted OpenAI-compatible LLM inference APIs, such as LocalAI or vLLM. This rule identifies potentially unauthorized or unintended public exposure of internal LLM infrastructure, which could lead to unauthorized model usage, information disclosure, or exploitation of the inference engine.
Detects outbound HTTP GET requests for common AI model file formats (.pkl, .pt, .bin) from non-authorized or untrusted external domains. This activity is indicative of downloading potentially malicious pre-trained AI models, which can execute arbitrary code during the unpickling process (Supply Chain Compromise).
Detects anomalous POST requests to the RubyGems API endpoint '/api/v1/gems' that include an 'Authorization' header and a 'Content-Type' of 'application/octet-stream'. This pattern suggests the automated upload of binary files, potentially indicating exfiltration of data disguised as Ruby gems.
Detects outbound HTTP PUT requests characteristic of data exfiltration associated with Qilin ransomware. The rule monitors for the use of command-line tools like s5cmd or rclone for uploading data, often used during the double extortion phase where stolen data is uploaded to leak sites.
Detects outbound HTTP GET requests containing an 'auth=' parameter with a long string of alphanumeric characters, characteristic of an OAuth token exfiltration attempt by a malicious Twitch extension (JeetBot). The rule flags requests to external destinations that are not official Twitch, Twitch Video Network, or JTV domains.
Detects network traffic indicating the use of Rclone to exfiltrate data to MEGA cloud storage, a technique associated with Akira ransomware campaigns.
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 HTTP POST requests directed to OpenAI or Anthropic API endpoints originating from non-browser user agents (e.g., Python requests, curl, Go-http-client). This activity may indicate automated interactions, potentially for malicious purposes such as script-based interaction with LLMs or unauthorized API usage.
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 outbound network traffic from internal servers to known Remote Monitoring and Management (RMM) platforms (AnyDesk, ScreenConnect, Atera). This activity is often used by adversaries to establish covert command and control (C2) channels and remote access to internal resources.
Detects anomalous large-scale data transfers to external endpoints that exhibit patterns characteristic of the ShinyHunters group's SaaS bulk export and data exfiltration methods. The rule identifies suspicious HTTP requests to Salesforce API endpoints (e.g., batch jobs, bulk queries) accompanied by unusually large content payloads, followed by subsequent encrypted communication to non-whitelisted external destinations.
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 high-frequency outbound TLS connections to 'api-gost.oneme.ru', which is indicative of potential bulk contact list uploading, possibly related to unauthorized data collection by the Max Messenger application.
Detects high-frequency outbound TLS connections to 'api-gost.oneme.ru', which is indicative of potential bulk contact list uploading, possibly related to unauthorized data collection by the Max Messenger application.
Detects high-frequency outbound TLS connections to 'api-gost.oneme.ru', which is indicative of potential bulk contact list uploading, possibly related to unauthorized data collection by the Max Messenger application.
Detects high-frequency outbound TLS connections to 'api-gost.oneme.ru', which is indicative of potential bulk contact list uploading, possibly related to unauthorized data collection by the Max Messenger application.
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 outbound network traffic directed to Bitbucket (via TLS/443) or using the git protocol (via port 9418). Such connections from internal systems to unverified or unauthorized external Git repositories may indicate malicious activity, including the potential installation of malicious plugins or code via supply chain compromises (e.g., Plugin4Shell).
Detects repeated large HTTP PUT or POST requests (exceeding 5MB) from a internal host to external destinations. This pattern is indicative of potential bulk data exfiltration over web services.
Detects network activity (DNS queries, TLS SNI, and HTTP requests) associated with identified 'ShipmentsFree' scam domains. These domains are typically used in phishing or credential harvesting campaigns to trick users into visiting fake account or tracking pages.
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