Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous HTTP POST requests to a suspected C2 endpoint ('/beacon/pre-register') on a typosquatted domain ('thecovnresation') characterized by an application/octet-stream content type, which is indicative of encrypted C2 beacon registration activity.
Detects anomalous HTTP POST requests to a suspected C2 endpoint ('/beacon/pre-register') on a typosquatted domain ('thecovnresation') characterized by an application/octet-stream content type, which is indicative of encrypted C2 beacon registration activity.
Detects HTTP POST requests directed to the URI path '/api/credentials' over the non-standard TCP port 8133, which is indicative of AMOS (Atomic macOS) Stealer exfiltrating stolen credentials to a C2 server.
Detects network activity associated with the AMOS (Atomic macOS Stealer) malware, specifically identifying C2 communication patterns such as heartbeat beacons and agent tasking requests occurring over non-standard port 8133.
Detects network activity associated with the AMOS (Atomic macOS Stealer) malware, specifically identifying C2 communication patterns such as heartbeat beacons and agent tasking requests occurring over non-standard port 8133.
Detects network activity associated with the AMOS (Atomic macOS Stealer) malware, specifically identifying C2 communication patterns such as heartbeat beacons and agent tasking requests occurring over non-standard port 8133.
Detects network activity associated with the 'ClickFix' social engineering campaign, specifically targeting the 'Macfinger' variant. This involves detecting the fetching of obfuscated tracker JavaScripts (t.js), the delivery of deceptive 'Bot Protection' overlay HTML prompts used to trick users into executing malicious code, and network connections to known C2 infrastructure.
Detects network activity associated with the 'ClickFix' social engineering campaign, specifically targeting the 'Macfinger' variant. This involves detecting the fetching of obfuscated tracker JavaScripts (t.js), the delivery of deceptive 'Bot Protection' overlay HTML prompts used to trick users into executing malicious code, and network connections to known C2 infrastructure.
Detects web requests to 'wp-admin/theme-install.php' containing suspicious query parameters that indicate an attempt to force an unauthenticated theme installation via a Cross-Site Request Forgery (CSRF) exploit (Click2Shell). The rule identifies requests with malicious payloads in the 'theme' parameter, specifically looking for indicators that attempt to bypass CSRF protections when originating from an external site or a non-admin session context.
Detects potential misuse of the WinGet configuration feature, where remote Desired State Configuration (DSC) YAML files are retrieved using masqueraded file extensions (e.g., .jpg or .txt) or suspicious outbound connections on port 8443, which may indicate an adversary attempting to hide malicious configurations or bypass network filtering.
Detects outbound network activity associated with the ChainScript malware family, including WebSocket C2 communication identified by a specific 'X-Agent-Token' header and TLS connections to known rotating command-and-control domains.
Detects the initialization sequence of the ORAX command-and-control framework, characterized by a handoff via a spoofed Cloudflare verification page followed by a WebSocket upgrade request.
This rule detects an HTTP GET request for 'static/js/main.js', a specific URI path associated with the JWR phishing framework's client-side engine. This indicates an interaction with a phishing infrastructure hosting malicious redirection or credential harvesting scripts.
This rule set detects network communication patterns associated with the AnonyMousKIT phishing kit, specifically targeting the exfiltration of captured user credentials to the Telegram Bot API and communication with known phishing panel infrastructure. It monitors for DNS lookups of 'anomkit.shop', established TCP connections to the associated backend relay, and HTTP requests containing specific credential-related keywords directed towards the Telegram API.
Detects network activity associated with the JWR Phishing-as-a-Service (PhaaS) platform. This includes the retrieval of the client-side JavaScript engine (main.js) from lookalike domains related to courier or government services, and subsequent client-side detection of the 'window.__HOST_MODE' flag, which indicates the browser has been primed for a specific phishing scenario.
Detects HTTP requests directed to the Vapi.ai API from potential phishing panels or malicious infrastructure. This behavior is indicative of an attacker leveraging Vapi.ai's conversational AI services to conduct automated voice phishing (vishing) campaigns by initiating calls or managing AI assistants.
Detects specific URI patterns associated with the 'JWR' phishing framework, which relays live operator instructions to capture pages (e.g., requesting CVV, 2FA codes, or PINs). These patterns are indicative of active adversary-in-the-middle phishing sessions.
These rules detect behavioral patterns associated with GhostCode-style anti-analysis, including URI-based gate redirection (turnstile checks), automated client filtering (User-Agent scanning), and UI-based spoofing (tab title masquerading) often used to protect malicious infrastructure from automated analysis tools and bots.
Detects HTTP GET requests to a specific path structure /scanna/ which includes a token-like identifier and a check=1 parameter. This pattern is indicative of a bot-filtering relay mechanism often used by malicious scripts or malware to validate or redirect traffic while attempting to evade security scanners.
Detects network activity associated with the 'RatHat' Android malware, specifically identifying attempts to retrieve tunnel configuration, deployment confirmation, and the initial reverse tunnel handshake using the fatedier/frp (Fast Reverse Proxy) tool.
This rule detects network activity associated with the NovaCookies Adversary-in-the-Middle (AiTM) phishing framework, specifically looking for traffic to domains with a .vu TLD, matching a specific URI pattern used for phishing lures in HTTP requests/responses, and related DNS queries.
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