Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects network exploitation attempts targeting Fortinet FortiOS and FortiProxy vulnerabilities (CVE-2024-55591, CVE-2025-24472) involving authentication bypass via HTTP header manipulation (Forwarded, X-Forwarded-For) and unauthorized access to the /jsconsole endpoint.
Detects network exploitation attempts targeting Fortinet FortiOS and FortiProxy vulnerabilities (CVE-2024-55591, CVE-2025-24472) involving authentication bypass via HTTP header manipulation (Forwarded, X-Forwarded-For) and unauthorized access to the /jsconsole endpoint.
Detects HTTP beaconing activity associated with the MacSync stealer malware on macOS platforms. The rule monitors for specific URI patterns ('curl', 'dynamic', 'gate') combined with macOS-specific User-Agent strings and potential credential exfiltration headers, indicating established communication with a Command and Control (C2) server.
This rule detects network traffic associated with Lumma Stealer information-stealing malware, specifically identifying HTTP POST requests that utilize the 'TeslaBrowser/5.5' user agent. This pattern is indicative of the malware exfiltrating stolen data to its Command and Control (C2) server.
Detects outbound HTTPS traffic to domains using file-sharing or cloud-sync themed keywords in combination with specific top-level domains (TLD) often associated with suspicious infrastructure. The rule specifically excludes well-known legitimate services such as OneDrive, SharePoint, Dropbox, Box, iCloud, and Google, and includes a detection threshold to minimize alerts for incidental traffic.
Detects outbound HTTPS traffic to domains using file-sharing or cloud-sync themed keywords in combination with specific top-level domains (TLD) often associated with suspicious infrastructure. The rule specifically excludes well-known legitimate services such as OneDrive, SharePoint, Dropbox, Box, iCloud, and Google, and includes a detection threshold to minimize alerts for incidental traffic.
Detects network activity associated with the AgtaBackup RAT, specifically identifying custom HTTP headers for agent authentication, beaconing patterns to check-in endpoints, and establishment of WebSocket-based command and control channels.
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 high-volume SSH authentication failures from a single source IP address within a short time frame, which is indicative of credential spraying or brute force activity.
Detects unauthenticated HTTP requests directed at management or schema APIs for common vector databases (Milvus, Weaviate, Qdrant). These requests lack the required Authorization header, potentially indicating unauthorized reconnaissance or attempts to exploit public-facing database management interfaces.
This rule detects attempts to exploit a command injection vulnerability (CVE-2026-49869) in the Kestra workflow automation platform. The rule monitors HTTP POST requests to the '/api/v1/flows' endpoint, identifying payloads that define flow types involving shell or execution plugins and contain suspicious command sequences (e.g., shell operators combined with common system utilities like nc, bash, or curl).
Detects an exploitation attempt against a known SQL injection vulnerability (CVE-2026-62262) in the Piwigo photo gallery application, specifically targeting the 'pwg.images.filteredSearch.create' method via the 'ratings' parameter. The detection monitors inbound HTTP traffic for common SQL injection patterns like UNION SELECT, SLEEP commands, and boolean-based injection payloads.
Detects network traffic patterns associated with a specific Cobalt Strike Malleable C2 profile (jquery-cookie), characterized by GET requests to URIs starting with /jquery- with a specific Cookie header structure and the absence of an Accept header.
Detects HTTP POST requests with 'photo' in the URI and Gzip-compressed body content, which is a known C2 communication pattern used by the IcedID banking trojan.
Detects HTTP traffic patterns associated with the default staging URIs of the PowerShell Empire C2 framework. This rule identifies inbound requests to specific .php files (admin/get, news, login/process) with a 'session=' cookie, which are characteristic of Empire agent beaconing behavior.
Detects DNS tunneling attempts by identifying DNS queries with abnormally long subdomains (greater than 50 characters) that exhibit a high request rate (20 or more queries within 60 seconds). This behavior is characteristic of C2 communication tools such as iodine or dnscat2, which encode data within DNS query labels to bypass traditional network security controls.
Detects network traffic associated with the BotHelper RAT communicating with its C2 domain 'easyllms.xyz'. The rule monitors for TLS SNI connections to this domain and specific HTTP API paths (/api/v1/ping, /api/v1/connect, etc.) indicative of command-and-control heartbeats and task execution.
Detects network activity associated with the Psychedelic Stealer malware, including C2 check-ins, API-based tasking, and communication with known C2 infrastructure using specific URI patterns and headers like 'X-API-Key'.
This rule monitors outbound TLS traffic to detect communication between a potentially compromised internal host and the DeepSeek API. This behavior is associated with the 'CLOSEDQUORUM' malware family, which utilizes LLM-based voting mechanisms for command and control (C2) operations via third-party AI services.
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