Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects malicious instruction-override payloads, hidden HTML comments, or obscured text (e.g., zero-font size) in third-party content ingested into LLM/RAG systems, commonly used for indirect prompt injection.
Detects anomalous DNS traffic patterns characterized by a high volume of unique subdomains, frequent long DNS query labels, or excessive use of TXT/NULL record types. These behaviors are frequently associated with DNS tunneling, a method used by adversaries to exfiltrate data or communicate with Command and Control (C2) servers via the DNS protocol.
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 network traffic (TLS SNI and HTTP Host headers) attempting to connect to known command-and-control infrastructure associated with the NodeRabbit RAT and Mirage Kitten campaign. The rule monitors for unauthorized connectivity to specific domains including 'oracle-challenge.s3.us-east-1.amazonaws.com', 'plugplay.azurewebsites.net', and 'lifespotify.com'.
Detects HTTP POST requests directed at Grav CMS endpoints where the __unique_form_id__ parameter contains directory traversal sequences (e.g., ../). This behavior is indicative of attempted exploitation of CVE-2026-42608, aimed at bypassing file system restrictions to write files to unauthorized locations outside the application's expected form data directory.
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 alerts that reference specific CVEs historically exploited by the threat group QTFY against internet-facing applications, including common vulnerabilities in Pulse Secure, FortiOS, Citrix ADC, Exchange, F5 BIG-IP, Log4Shell, and others, when the alert severity is Medium or High and contains indicators of exploit or initial access.
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 outbound HTTP GET requests for common AI model file formats (.pkl, .pt, .bin) from non-authorized or untrusted external domains. This activity is indicative of downloading potentially malicious pre-trained AI models, which can execute arbitrary code during the unpickling process (Supply Chain Compromise).
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 network traffic containing TLS Server Name Indication (SNI) fields associated with known 'jailbreak-as-a-service' LLM proxies, specifically WormGPT, FraudGPT, and EvilGPT. These services are often used by threat actors to generate malicious content, phishing emails, or exploit code.
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.
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