Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
205 detections
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Detects potential reconnaissance or port scanning activities against firewall or UTM management interfaces from external IP addresses. The rule identifies sources that trigger a high volume of blocked or denied attempts across multiple management ports (e.g., SSH, HTTP/S, GUI ports) within a one-hour window.
Detects anomalous web request patterns against Citrix NetScaler ADC or Gateway devices, characterized by excessively long URLs or request contexts combined with high-frequency parameter variation from a single source. This behavior is indicative of potential fuzzing or exploit attempts targeting web application vulnerabilities, specifically input validation flaws in edge infrastructure.
Detects potential reconnaissance or port scanning activities against firewall or UTM management interfaces from external IP addresses. The rule identifies sources that trigger a high volume of blocked or denied attempts across multiple management ports (e.g., SSH, HTTP/S, GUI ports) within a one-hour window.
This rule monitors network device logs for patterns indicative of reconnaissance or vulnerability scanning against management and VPN interfaces of common firewall and network appliances (FortiGate, Meraki, WatchGuard, Sophos). It flags sources that either request multiple sensitive paths related to system status and configuration or employ known scanning tools (Nmap, masscan, Nuclei, zgrab, curl) in the user-agent string.
This rule detects anomalous, high-frequency unauthenticated POST requests targeting NetScaler ADC and Gateway management or service endpoints (/vpn/, /logon/, /nitro/). Such patterns are characteristic of automated reconnaissance and exploitation attempts, specifically targeting vulnerabilities like CVE-2026-88771 and CVE-2026-88772.
This rule detects anomalous increases in inbound network connection volume or unique remote IP address counts associated with a specific process running on a Linux host. This behavior can be indicative of automated scanning, brute-forcing attempts, or a Denial of Service (DoS) attack targeting an exposed API service.
This rule detects external reconnaissance attempts targeting MLflow Tracking Servers by monitoring for inbound HTTP GET requests to the default MLflow API path '/api/2.0/mlflow/'. This behavior is indicative of an adversary scanning for exposed machine learning infrastructure, potentially to facilitate subsequent unauthorized access or exploitation of the tracking server.
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.
This rule monitors network device logs for patterns indicative of reconnaissance or vulnerability scanning against management and VPN interfaces of common firewall and network appliances (FortiGate, Meraki, WatchGuard, Sophos). It flags sources that either request multiple sensitive paths related to system status and configuration or employ known scanning tools (Nmap, masscan, Nuclei, zgrab, curl) in the user-agent string.
Detects unauthenticated network requests targeting MLflow Model Registry and Ajax API endpoints on port 5000. These requests are characteristic of unauthorized reconnaissance against exposed machine learning management services.
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 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.
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 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 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.
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