Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects HTTP GET requests to external servers for files identified as 'mscopilot.exe' or '65722.txt', which are associated with the AdaptixC2 framework/malware, indicating potential payload delivery or command and control activity.
Detects network traffic associated with Adversary-in-the-Middle (AiTM) phishing kits that impersonate Microsoft and Google login pages. The rules identify the presence of specific HTML elements (like Cloudflare Turnstile anti-bot gates) and authentication session cookie headers served from non-authorized/non-official domains, indicating a reverse-proxy phishing attack.
Detects Git clone or fetch operations targeting bitbucket.org where the requested branch or ref is formatted as a SHA hash. This specific pattern is identified as a potential precondition for exploitation attempts targeting vulnerabilities like Plugin4Shell, where malicious branch names are used to trigger code execution on the server-side.
Detects Server-Side Request Forgery (SSRF) attempts targeting SonicWall SMA1000 WorkPlace endpoints. The rule monitors both HTTP GET query parameters and HTTP request bodies for patterns indicating attempts to redirect the application to internal loopback addresses or local network ranges (e.g., 127.0.0.1, 169.254.169.254, RFC1918 private subnets).
Detects a multi-stage phishing attack targeting cryptocurrency users, specifically Ledger wallet holders. The detection identifies an initial request to a Vercel-hosted domain for a BIP-39 wordlist (a common recovery phrase standard) followed by a POST request to a similar Vercel-hosted endpoint, suggesting the exfiltration of a secret recovery phrase.
Detects HTTP POST requests characteristic of the AgtaBackup RAT, specifically exfiltrating browser profiles or system information via the '/api/agents/' endpoint. The rule monitors for established connections targeting this specific URI structure.
Detects outbound network traffic from internal hosts to external destinations containing a specific, hard-coded User-Agent string (firefox/21.0). This behavior is characteristic of the NeedyMantis malware family, which utilizes this string to identify its own C2 communications, and is a strong indicator of compromise.
Detects outbound HTTP requests to api.github.com targeting the 'contents' API with patterns indicative of RUSTYSHADE malware command-and-control (C2) communication, including file extensions and filenames typical of its tasking mechanisms.
Detects anomalous HTTP traffic patterns identified in the 'Ted' backdoor used by North Korean-affiliated threat actors (APT37/Kimsuky/Lazarus). The rule specifically monitors the 'Accept-Language' HTTP header for a specific construction containing 'mrt=' followed by 'msc=' or 'mst=', which is used as a covert mechanism for authentication or command signaling.
This rule detects HTTP POST requests characteristic of the curlRAT malware used by DPRK-affiliated threat actors. The detection looks for a specific POST body format containing 'name=', '&value=', and '&type=' parameters, which indicates C2 communication patterns commonly associated with this specific backdoor.
Detects potential NTLM relay attacks targeting Active Directory Certificate Services (AD CS) web enrollment endpoints, including subsequent certificate requests for sensitive templates. This pattern identifies attackers attempting to relay NTLM authentication to AD CS and potentially requesting certificates for high-privilege templates (e.g., DomainController, SubCA, Machine, or User) to facilitate privilege escalation.
This rule detects suspicious HTTP POST requests to a /collect endpoint that contain tracking parameters (visitorId, stage, variant, events) associated with 'ClickFix' social engineering campaigns. These campaigns typically trick users into copying and pasting malicious code by simulating error prompts or CAPTCHAs.
Detects unauthorized HTTP requests to sensitive Cisco Identity Services Engine (ISE) API and administrative paths (/ers/, /admin/, /api/). The rule specifically looks for incoming requests that lack both an Authorization header and a Cookie header, which is indicative of potential exploitation attempts against vulnerabilities such as CVE-2026-76460, where authentication mechanisms might be bypassed or ignored.
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 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.
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