Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
1,310 detections
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Detects unauthorized attempts to access exposed Chroma or Weaviate vector database APIs via standard HTTP requests (GET/POST) targeting known API endpoints for collections or objects. This may indicate an adversary conducting reconnaissance on publicly accessible database infrastructure.
Detects a potential automated exploit sweep or brute-force attempt, identified by a high frequency of HTTP requests (GET or POST) directed to the same URI from a single source IP address within a short time frame.
Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
Detects high-frequency access requests directed at authentication or API endpoints originating from known Tor exit-node IP addresses. This activity is indicative of credential stuffing or automated scanning patterns that attempt to evade IP-based reputation or rate-limiting filters.
This rule detects potential adversarial reconnaissance activities targeting AI agents via multiple communication channels (e.g., email, collaboration tools). It identifies patterns where a sender transmits messages containing common 'jailbreak' or 'instruction-override' phrases (e.g., 'ignore previous instructions', 'bypass approval') across at least three distinct channels within a 24-hour window, indicating a concerted effort to manipulate or probe agent logic.
Detects a sequence of potentially malicious activity targeting LLM-related API endpoints. The rule identifies a client IP performing rapid, distinct endpoint discovery, followed by a POST request to sensitive paths, and concluding with a spike in 5xx server-side errors, indicating potential exploitation attempts or fuzzing against Large Language Model interfaces.
Detects a sequence of potentially malicious activity targeting LLM-related API endpoints. The rule identifies a client IP performing rapid, distinct endpoint discovery, followed by a POST request to sensitive paths, and concluding with a spike in 5xx server-side errors, indicating potential exploitation attempts or fuzzing against Large Language Model interfaces.
Detects high-volume web crawling or reconnaissance activities originating from autonomous AI agent frameworks, such as AutoGPT, BabyAGI, LangChain, or CrewAI. These activities are identified by analyzing User-Agent strings and applying threshold-based rate limiting to identify potential automated reconnaissance patterns.
Detects HTTP requests where the User-Agent header matches known strings associated with AI-powered penetration testing or reconnaissance agents, such as PentestGPT, AutoGPT, or AI-Recon tools.
Detects high-frequency POST requests to API endpoints, potentially indicative of automated fuzzing or brute-force parameter mutation attacks, by identifying 50 requests from a single source within a 10-second window.
Detects modifications to AWS S3 bucket policies, access control lists (ACLs), or public access block configurations that result in public bucket exposure. The rule specifically flags broad permissions such as 'AllUsers' or wildcard principal entities, and correlates these findings with AWS Config or Access Analyzer data to confirm public reachability.
Detects high-volume account enumeration activity associated with the TeamFiltration tool, characterized by the use of legacy Microsoft Teams desktop client User-Agent strings (specific versions of Teams, Electron, and Chrome) within a short time window.
Detects an abnormally high volume of unique DNS subdomain queries originating from a single source IP address within a short time window. This behavior is often indicative of automated reconnaissance techniques, such as subdomain brute-forcing or dictionary attacks, used to discover infrastructure or hidden services.
Detects high-frequency web requests from a single source IP containing URI patterns associated with known vulnerability exploits (e.g., directory traversal, remote code execution, or path injection). This rule identifies potential mass exploitation attempts against public-facing web applications by monitoring firewall logs for a threshold of suspicious URI activity.
Detects web requests targeting sensitive Jenkins/CI-CD administrative and build-triggering endpoints, such as /script, /manage, and /job/*/build. These paths are high-value targets for reconnaissance and automated exploitation efforts. This rule identifies potentially malicious activity by monitoring for access to these specific paths, which should be correlated with frequency and source data to differentiate automated scanning from authorized administrative or system activities.
Detects accounts showing a spike in requests to public generative-AI services compared to their historical baseline, combined with anomalous indicators like multiple source IPs, regions, or off-hours activity.
Detects incoming HTTP GET requests to common Jupyter Notebook endpoints ('/tree' or '/api/kernels') that do not include a token in the URI. This behavior is indicative of an adversary attempting to interact with or explore an insecurely configured Jupyter Notebook server that lacks authentication.
Detects high-frequency requests from automated scripting tools like 'python-requests', 'aiohttp', or 'Scrapy' targeting sensitive directories or files often associated with administrative panels or application configurations (e.g., admin, .env, wp-admin). The rule triggers when a threshold of 10 requests is reached within a 60-second window, indicating potential automated vulnerability or information gathering activity.
Detects multiple inbound HTTP requests targeting known vulnerable application paths (e.g., cgi-bin, .env files, actuator gateways, path traversal, WordPress plugins, PHP eval files, and various server administration consoles) from a single source within a short timeframe. This behavior is indicative of an automated scanner or exploit chain attempting to identify and compromise public-facing applications.
Detects unauthenticated HTTP POST requests targeting the '/v1/graphql' endpoint of a Weaviate vector database. This pattern is indicative of reconnaissance or exploitation attempts against exposed Weaviate API instances.
This rule detects reconnaissance activity targeting Azure App Service instances, specifically looking for attempts to access web-shell-like files, WordPress administration paths, PHP-CGI endpoints, or LangFlow code validation paths. It identifies potential Storm-3168 actor infrastructure through known IOCs or through patterns of high-volume, path-diverse HTTP requests that deviate from typical noise, helping to distinguish targeted probing from common internet background scanning.
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