Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects an authentication pattern characteristic of credential compromise, consisting of a burst of failed authentication attempts against an account within a short window, followed immediately by a successful sign-in from an anomalous or new device/location. This pattern is indicative of brute-force or credential spraying followed by successful validation and potential lateral movement.
This rule detects HTTP GET requests to Vercel subdomains targeting a specific path (/secure.html). This behavior is characteristic of phishing campaigns, specifically those masquerading as RMM-related lures to trick users into providing access codes or credentials.
Detects network activity associated with a malicious NPM package (typosquatting) beaconing to a known C2 server (69.48.229.140) and performing command and control operations, including command polling and data exfiltration, specifically from a Node.js environment.
Detects anomalous execution of the Microsoft Support Diagnostic Tool (msdt.exe) or its host process (sdiagnhost.exe) when spawned by Microsoft Office applications or when invoked with specific command-line arguments indicative of the CVE-2022-30190 (Follina) exploit.
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 anomalous execution of the Microsoft Support Diagnostic Tool (msdt.exe) or its host process (sdiagnhost.exe) when spawned by Microsoft Office applications or when invoked with specific command-line arguments indicative of the CVE-2022-30190 (Follina) exploit.
Detects anomalous authentication patterns where multiple distinct user identities authenticate from the same source IP (often a VPN or proxy) within a short timeframe, characteristic of North Korean IT worker laptop-farm or identity-mule operations where a single operator manages multiple contractor accounts.
Detects HTTP POST requests directed at common Machine Learning inference endpoints (e.g., /predict, /classify, /infer) where the request body claims to be a multipart form-data image upload (Content-Type: image/jpeg) but the actual binary data does not contain the standard JPEG file signature (FF D8 FF). This pattern is indicative of adversarial attempts to bypass security controls or provide malformed payloads to ML models.
Detects outbound HTTP traffic to punycode-encoded domains (typically used to spoof legitimate sites) that contain characteristic boilerplate text often generated by LLMs in their responses. This combination is indicative of an automated phishing kit setup or a landing page generated by an AI assistant.
Detects anomalous execution of the Microsoft Support Diagnostic Tool (msdt.exe) or its host process (sdiagnhost.exe) when spawned by Microsoft Office applications or when invoked with specific command-line arguments indicative of the CVE-2022-30190 (Follina) exploit.
Detects anomalous authentication patterns where multiple distinct user identities authenticate from the same source IP (often a VPN or proxy) within a short timeframe, characteristic of North Korean IT worker laptop-farm or identity-mule operations where a single operator manages multiple contractor accounts.
Detects the reuse of session tokens from distinct network locations, devices, or user agents shortly after a successful MFA-protected login. This behavioral pattern is a primary indicator of Adversary-in-the-Middle (AiTM) phishing, where attackers capture session tokens via reverse proxy to bypass authentication controls.
Detects anomalous execution of the Microsoft Support Diagnostic Tool (msdt.exe) or its host process (sdiagnhost.exe) when spawned by Microsoft Office applications or when invoked with specific command-line arguments indicative of the CVE-2022-30190 (Follina) exploit.
Detects an authentication pattern characteristic of credential compromise, consisting of a burst of failed authentication attempts against an account within a short window, followed immediately by a successful sign-in from an anomalous or new device/location. This pattern is indicative of brute-force or credential spraying followed by successful validation and potential lateral movement.
Detects DNS queries for domains containing specific brand-related strings (such as government agencies or courier services) coupled with high-risk top-level domains (TLDs). This pattern is frequently used by adversaries to register infrastructure for phishing and social engineering campaigns by mimicking legitimate organizational presence.
Detects HTTP and TLS traffic directed toward Vercel subdomains (vercel.app) that are consistent with known patterns for document-lure phishing or gated ZIP file distribution, often used for RMM-phishing campaigns.
Detects network activity associated with a malicious NPM package (typosquatting) beaconing to a known C2 server (69.48.229.140) and performing command and control operations, including command polling and data exfiltration, specifically from a Node.js environment.
Detects network activity associated with a malicious NPM package (typosquatting) beaconing to a known C2 server (69.48.229.140) and performing command and control operations, including command polling and data exfiltration, specifically from a Node.js environment.
Detects instances where AI agents are activated through external communication channels like webhooks, email, or chat, and the provided payload includes potentially malicious imperative instructions (e.g., 'delete', 'execute', 'send credentials'). The detection logic excludes activity originating from trusted, allow-listed internal sources to reduce false positives.
This rule detects administrative or user activity within Azure via AzureActivity logs and web request patterns in AppServiceHTTPLogs that originate from a predefined list of known malicious IP addresses.
Detects non-interactive sign-in events to M365 that claim to be from the Outlook client application but utilize suspicious or unexpected User-Agent strings (e.g., Python requests, specific Firefox versions). This behavior is often indicative of programmatic access or automated tools used by attackers to harvest credentials or interact with mailboxes, bypassing standard browser-based authentication controls.
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