Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
228 detections
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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 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.
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.
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.
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 anomalous, high-frequency, multi-channel probing of AI agent input surfaces. The rule identifies external actors sending instruction-injection style keywords across different communication channels (e.g., email, Teams, calendar) within a short window, which may indicate an attempt to identify and manipulate an organization's autonomous agents. Covers T1595.002
Detection of common web server reconnaissance, enumeration, and brute-force patterns targeting IIS environments. This includes detection of short-name (~1) probes, WebDAV method usage, NTFS alternate data stream requests, and brute-force attempts on login endpoints originating from PowerShell user agents.
Detects repeated attempts to enumerate available model endpoints on a LiteLLM server, a behavior often associated with reconnaissance targeting AI gateway services to identify exposed models.
Detects network activity related to the exploitation of the CVE-2025-31702 vulnerability in Dahua P2P Relay services. The rules monitor for probe attempts, device parameter fetching (randsalt), serial number enumeration bursts, and successful no-auth channel responses, indicating a bypass of authentication mechanisms on vulnerable Dahua devices.
Detects anomalous external traffic patterns indicative of automated API reconnaissance. The rule flags high-frequency requests from a single IP targeting a diverse set of API endpoints, particularly when characterized by a mixture of successful (200), unauthorized (403), or not found (404) status codes, or repeated attempts to access sensitive management/administrative API paths.
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 anomalous, high-frequency, multi-channel probing of AI agent input surfaces. The rule identifies external actors sending instruction-injection style keywords across different communication channels (e.g., email, Teams, calendar) within a short window, which may indicate an attempt to identify and manipulate an organization's autonomous agents. Covers T1595.002
Detects automated API exploitation attempts, specifically Broken Object Level Authorization (BOLA) scanning. The rule triggers when a single source IP or API token exhibits high-frequency request patterns (5+ requests per second) against resource endpoints with sequentially incrementing IDs. It specifically correlates a spike in 401/403 authorization failures followed by a small count of 200 successes, indicating a potential successful bypass of authorization controls.
Detects an abnormally high volume of POST requests from a single source to the Salesforce Aura API endpoint (/s/sfsites/aura) within a short window, which is indicative of automated guest profile enumeration and AuraInspector-style probing activity associated with the ShinyHunters Salesforce campaigns.
Detects a multi-stage attack pattern against publicly exposed AI service endpoints. The detection correlates: (1) Enumeration of sensitive AI/MCP-related API endpoints (minimum 3 distinct hits) in a 10-minute window; (2) Subsequent POST requests to AI service endpoints within 15 minutes of the enumeration; and (3) A spike of 5 or more server errors (HTTP 5xx) within 10 minutes following the POST activity. This pattern suggests automated reconnaissance followed by attempted exploitation of exposed AI model interfaces or orchestration services. Covers T1595.002, T1190
Detects anomalous, high-frequency, multi-channel probing of AI agent input surfaces. The rule identifies external actors sending instruction-injection style keywords across different communication channels (e.g., email, Teams, calendar) within a short window, which may indicate an attempt to identify and manipulate an organization's autonomous agents. Covers T1595.002
Detects a multi-stage attack pattern against publicly exposed AI service endpoints. The detection correlates: (1) Enumeration of sensitive AI/MCP-related API endpoints (minimum 3 distinct hits) in a 10-minute window; (2) Subsequent POST requests to AI service endpoints within 15 minutes of the enumeration; and (3) A spike of 5 or more server errors (HTTP 5xx) within 10 minutes following the POST activity. This pattern suggests automated reconnaissance followed by attempted exploitation of exposed AI model interfaces or orchestration services. Covers T1595.002, T1190
Detects a multi-stage attack pattern against publicly exposed AI service endpoints. The detection correlates: (1) Enumeration of sensitive AI/MCP-related API endpoints (minimum 3 distinct hits) in a 10-minute window; (2) Subsequent POST requests to AI service endpoints within 15 minutes of the enumeration; and (3) A spike of 5 or more server errors (HTTP 5xx) within 10 minutes following the POST activity. This pattern suggests automated reconnaissance followed by attempted exploitation of exposed AI model interfaces or orchestration services. Covers T1595.002, T1190
Detects anomalous, high-frequency requests from a single source IP targeting sensitive NetScaler/Citrix ADC Gateway paths. This behavior is indicative of automated reconnaissance or vulnerability scanning often preceding exploitation attempts.
Detects potential reconnaissance activities targeting Redis services on port 6379, specifically looking for unauthenticated 'PING' commands and subsequent responses indicating either authentication requirements or an open, unauthenticated instance.
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