Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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.
This rule detects anomalous network traffic patterns characteristic of the JWR phishing framework. It identifies live, operator-driven exfiltration where an attacker issues specific instruction sequences (e.g., to_card, to_sms, to_2fa) via HTTP polling or WebSockets to facilitate the character-by-character capture of sensitive information like payment card details and OTPs from a victim.
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.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
This rule monitors DeviceNetworkEvents and DnsEvents for connections to known malicious domains and IP addresses associated with phishing campaigns. It alerts on any network communication or DNS resolution attempt targeting these indicators, providing visibility into potential user interaction with phishing infrastructure.
This rule monitors DeviceNetworkEvents and DnsEvents for connections to known malicious domains and IP addresses associated with phishing campaigns. It alerts on any network communication or DNS resolution attempt targeting these indicators, providing visibility into potential user interaction with phishing infrastructure.
This rule monitors DeviceNetworkEvents and DnsEvents for connections to known malicious domains and IP addresses associated with phishing campaigns. It alerts on any network communication or DNS resolution attempt targeting these indicators, providing visibility into potential user interaction with phishing infrastructure.
This rule monitors DeviceNetworkEvents for any outbound network connections initiated from Windows devices to the domain 'files.surveillance-online.com', which is associated with malicious activity.
Detects a suspected malicious sequence associated with RatHat activity. The rule identifies a device fetching an injection template configuration from a remote URL, followed by the exfiltration of credentials or form data to specific API endpoints on the same device within a 15-minute window.
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.
This rule detects a two-stage network redirect sequence indicative of an Adversary-in-the-Middle (AiTM) phishing campaign. The first stage identifies traffic interacting with a Google Ads click-tracker, and the second stage identifies a subsequent request to an AWS S3 bucket, often used to host malicious landing pages or phishing proxies.
Detects network traffic indicating a potential adversary-in-the-middle (AiTM) phishing proxy attempting to relay authentication requests to the legitimate Microsoft login service. The rule triggers when an external host acts as a reverse proxy for login.microsoftonline.com endpoints without being the legitimate Microsoft service itself.
Detects network activity associated with Adversary-in-the-Middle (AiTM) phishing kits. This includes detection of kit-specific fingerprinting scripts (class_identifier.php), use of specific server banners (openresty), unauthenticated geolocation lookups (api.country.is), setting of geolocation cookies, and proxying of requests to Microsoft OAuth endpoints.
Detects communication patterns associated with the OnyxC2 malware, including host-fingerprint synchronization over HTTP, potential reverse SOCKS5 proxy tunnel initialization, and inbound HTTP responses containing command tasking such as cmd.exe.
Detects a specific adversarial pattern used to bypass authentication protections, where a browser process first navigates to an attacker-controlled identity verification or CAPTCHA challenge page, followed by an immediate navigation to legitimate Microsoft device code authentication endpoints within the same process session.
Detects a specific adversarial pattern used to bypass authentication protections, where a browser process first navigates to an attacker-controlled identity verification or CAPTCHA challenge page, followed by an immediate navigation to legitimate Microsoft device code authentication endpoints within the same process session.
Detects a specific adversarial pattern used to bypass authentication protections, where a browser process first navigates to an attacker-controlled identity verification or CAPTCHA challenge page, followed by an immediate navigation to legitimate Microsoft device code authentication endpoints within the same process session.
Detects a specific adversarial pattern used to bypass authentication protections, where a browser process first navigates to an attacker-controlled identity verification or CAPTCHA challenge page, followed by an immediate navigation to legitimate Microsoft device code authentication endpoints within the same process session.
Detects a multi-stage phishing redirect chain used in 'EvilTokens' attacks. The rule identifies communication with common serverless hosting platforms (Vercel, Cloudflare Workers, AWS Lambda/Execute-API) followed by interaction with anti-bot/CAPTCHA gates (Cloudflare Turnstile, hCaptcha, Google reCAPTCHA) and finally reaching known Microsoft device code phishing endpoints (/devicelogin or /common/oauth2/deviceauth).
Detects anomalous network polling patterns consistent with 'EvilTokens' or similar adversary-in-the-middle (AiTM) OAuth phishing kits. The rule identifies web browsers performing high-frequency, low-interval polling (3-6s) against non-reputable/unseen domains (the attacker-controlled status endpoint) immediately preceding or following successful Microsoft OAuth device-code authentication sequences.
Detects anomalous network polling patterns consistent with 'EvilTokens' or similar adversary-in-the-middle (AiTM) OAuth phishing kits. The rule identifies web browsers performing high-frequency, low-interval polling (3-6s) against non-reputable/unseen domains (the attacker-controlled status endpoint) immediately preceding or following successful Microsoft OAuth device-code authentication sequences.
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