Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects suspicious outbound HTTPS POST traffic to major generative AI API endpoints (OpenAI, Anthropic, Google) originating from scripting hosts like powershell.exe or python.exe. The detection is triggered by either unusually large outbound payload sizes or the use of unauthorized scripting tools, which is indicative of data exfiltration using AI infrastructure as a relay.
This rule detects potential DNS tunneling activity by analyzing DNS query logs for high entropy in subdomains, elevated volumes of TXT/NULL record types, and frequent NXDOMAIN responses. This pattern is characteristic of command-and-control (C2) or data exfiltration techniques using tools like dnscat2 or iodine, which encode data into DNS query labels.
Detects a sequence of activity indicative of data exfiltration preparation. This rule identifies the creation of multiple large archive files (.zip, .rar, .7z) in temporary or staging directories on an endpoint, followed by a network connection transferring a significant volume of data to an external, non-private IP address within a 30-minute window.
Detects a suspected ransomware double-extortion sequence: the bulk creation of password-protected archives using compression utilities (e.g., 7z, RAR), followed by significant outbound network connections to common cloud storage or anonymization endpoints, and concluding with mass file modifications indicative of encryption activity.
Detects the installation of known AI-powered coding assistant extensions (e.g., Copilot, Tabnine, Cursor) on a device, followed by significant source-code repository cloning or archival activity by Git within two hours. This pattern is potentially indicative of intellectual property theft or sensitive source code exfiltration facilitated by a newly introduced AI tool.
Detects the execution of command-line archiving utilities (e.g., 7z, rar, zip) to create potentially password-protected archives within common staging directories (Temp, Public, ProgramData), followed by an outbound network connection to an external destination. This behavior is consistent with data staging and subsequent exfiltration.
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 high-risk GitHub events indicating data or asset exposure, such as creating public repositories with specific naming conventions, changing private repositories to public, or creating new gists, which could indicate exfiltration of sensitive assets or information.
Detects high-risk GitHub events indicating data or asset exposure, such as creating public repositories with specific naming conventions, changing private repositories to public, or creating new gists, which could indicate exfiltration of sensitive assets or information.
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 HTTP POST requests containing an aggregated 'LOOT' variable in the body. The rule looks for markers indicating the exfiltration of Kubernetes-related credentials, such as service account tokens, bearer tokens, or configuration secrets, as well as common environment variable dumps like API keys.
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.
This rule detects network activity associated with the GemStuffer malware, specifically monitoring for DNS queries to known OAST callback domains, HTTP requests to specific C2 paths, and unauthorized data exfiltration attempts to the webhook.site service.
Detects a multi-stage communication pattern involving an initial request to a URL-mirroring or shortening service with a long base64-encoded path, followed by a request to a screenshot/rendering API, and culminating in the reception of an image file. This chain is indicative of command and control (C2) communication where command output or exfiltrated data is encoded into images rendered by external web services to bypass traditional network security filters.
Detects outbound network connections to known SectopRAT C2 infrastructure, specifically targeting the hardcoded IP 98.142.252.140 or the non-standard port 15847 often used for encrypted exfiltration.
Detects command-line operations indicative of bulk data export, dumping, or unauthorized scanning against vector databases (e.g., Pinecone, Chroma, Milvus, Qdrant) and caching backends (e.g., Redis). Such activities may indicate an adversary attempting to exfiltrate training data, embeddings, or context stored within an AI agent's memory backend.
Detects an AI agent or automated process that accesses sensitive data sources (such as vaults, databases, or documentation platforms) followed by an outbound HTTP POST request to known high-risk SaaS or web services (e.g., paste sites, file sharing, or webhook services) with a suspicious or large payload.
Detects instances where an AI agent's output contains sensitive credential patterns such as AWS access keys, JWTs, private keys, or Bearer tokens. This rule monitors user communication, generic events, and file creation events to identify potential security compromises or unintended data leakage from the AI model.
This rule detects potential data exfiltration by monitoring corporate user activities involving consumer generative AI services (e.g., ChatGPT, Gemini, Claude). It identifies suspicious patterns such as large volume submissions, repeated chunked prompts, and the presence of sensitive content (e.g., PII markers, classification labels) or obfuscated payloads (base64/rot13) that suggest an attempt to bypass DLP or detection controls.
Detects network activity associated with the LONGTALE malware family (attributed to APT31/JungleBamboo). This includes identification of specific malicious browser extension IDs in HTTP traffic and signature-based detection of periodic HTTPS/HTTP beaconing patterns indicative of session or cookie exfiltration.
Detects a suspicious sequence of events where a host performs multiple reconnaissance-style probes (health checks, documentation, or OpenAPI definitions) followed within 10 minutes by access to sensitive artifact-related URLs on the same remote host. This pattern is indicative of an attacker profiling a target server for metadata and then proceeding to exfiltrate files or sensitive artifacts.
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