Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects rapid termination of specific mobile messenger applications (Max) within seconds of starting, while indicators of the Frida dynamic instrumentation tool are concurrently detected on the device. This pattern suggests the application is identifying the presence of a hooking/instrumentation environment and self-terminating as a defense mechanism. The detection is further correlated with failed connection attempts to the application's backend.
Detects rapid termination of specific mobile messenger applications (Max) within seconds of starting, while indicators of the Frida dynamic instrumentation tool are concurrently detected on the device. This pattern suggests the application is identifying the presence of a hooking/instrumentation environment and self-terminating as a defense mechanism. The detection is further correlated with failed connection attempts to the application's backend.
Detects large and sustained outbound TCP data transfers to external networks, which may indicate mass data exfiltration. The rule monitors established sessions and uses a threshold to identify unusual outbound traffic volume over an extended period.
Detects network activity associated with a VBScript-based dropper attempting to retrieve payloads from known malicious infrastructure (cdn.imageurlgenerator.com) or abusing legitimate file hosting services (filemail.com) to bypass traditional security filters.
Detects network activity associated with the ORAX control-plane panel and management service. The rule identifies HTTP traffic containing OraxManage or OraxRDP service strings, SignalR negotiate handshakes directed to common ORAX management ports, and connections to identified ORAX C2 infrastructure domains and IP addresses.
This rule detects a multi-stage phishing campaign where an attacker uses legitimate Notion infrastructure to host PDF invitation lures. The detection correlates email delivery of a Notion-themed invitation with a subsequent user click on a URL containing device code authorization keywords (such as 'device', 'devicelogin', or 'code') within a 60-minute window, which is indicative of a device-code harvesting attack.
Detects phishing emails purportedly from Docusign that contain malicious links which redirect users through legitimate identity provider domains (e.g., login.microsoftonline.com, accounts.google.com) before landing on non-trusted infrastructure. This redirect-chain pattern is indicative of NovaCookies Adversary-in-the-Middle (AiTM) delivery, used to facilitate credential or session cookie theft.
Detects phishing emails purportedly from Docusign that contain malicious links which redirect users through legitimate identity provider domains (e.g., login.microsoftonline.com, accounts.google.com) before landing on non-trusted infrastructure. This redirect-chain pattern is indicative of NovaCookies Adversary-in-the-Middle (AiTM) delivery, used to facilitate credential or session cookie theft.
This rule detects interaction between an internal host and the Vapi.ai API, typically used for automating AI-based voice phishing (vishing) calls. It identifies the initiation of a call via POST requests to the Vapi.ai API and subsequent callbacks from Vapi.ai to an attacker-controlled webhook endpoint used for tracking call status.
Detects a suspicious pattern where a user browser visits a Google Document and subsequently initiates a connection to the Telegram Bot API within a short timeframe (10 minutes). This sequence is indicative of malicious Google Apps Scripts embedded in documents attempting to exfiltrate victim information (IP, geolocation, device details) as part of a reconnaissance phase before a ClickFix-style payload delivery.
Detects network activity associated with the GhostCode phishing kit, including HTTP requests to specific access-prefixed subdomains and DNS lookups for those domains. Additionally, it detects HTTP responses containing specific spoofed browser tab titles often used in these phishing campaigns.
Detects a suspicious sequence where a Python process is spawned by a 'marimo' notebook process, followed immediately by an outbound SSH connection attempt from the same device. This pattern is indicative of automated or scripted lateral movement where a notebook environment is abused as a jump-point for command execution on remote systems.
Detects a process accessing browser credential files (Login Data) followed by an outbound network connection to a suspicious exfiltration endpoint commonly associated with credential-stealing malware.
This rule detects processes other than known web browsers (Chrome, Edge, Firefox, Brave, Opera) accessing sensitive browser-related files (Login Data, Cookies, Web Data) and initiating an external network connection within 15 minutes. This behavior is highly characteristic of credential and session cookie theft, often utilized by info-stealing malware for subsequent session hijacking.
Detects network activity associated with RatHat FRP (Fast Reverse Proxy), including configuration fetching, deployment notifications, and command-and-control handshakes. These rules monitor for specific HTTP API endpoints and TCP patterns used by the tool to establish unauthorized reverse tunnels.
Detects outbound network requests to Google Gemini AI API endpoints from processes that are not common web browsers or known development tools. This behavior is indicative of unauthorized use of LLM services by non-browser applications, such as the RatHat UI-automation module, for processing sensitive data or automation tasks.
Detects network communication to domains and specific file paths associated with the MacSync macOS stealer, which is used for malware dropper delivery and command-and-control (C2) operations. The rule monitors DeviceNetworkEvents for indicators such as specific malicious URLs, iCloud path abuse, and C2 infrastructure interaction.
Detects outbound network connections to known JWR phishing framework endpoints. The rule identifies suspicious interaction sequences, including initial beaconing via 'addClick' or 'getSyncSettings' followed by exfiltration of sensitive data (payment card info, OTPs) through POST requests to 'the_final_interface' or 'addCvv', or via a specific WebSocket channel pattern.
Detects outbound network requests to VAPI.ai APIs that are associated with the initiation of AI-powered voice agents. This telemetry can indicate the use of automation or malicious scripts, such as those used by AnonyMousKIT to launch persona-based voice phishing (vishing) campaigns.
Detects sustained WebSocket connections to known C2 infrastructure associated with the JWR phishing framework. The rule identifies long-running sessions or frequent reconnection patterns characteristic of a live human operator interacting with a victim, as opposed to automated, one-time form submissions.
This rule detects DNS lookups, email URL clicks, and network connections involving lookalike domains that mimic Singaporean government or infrastructure services (e.g., LTA, OneMotoring, ERP, Gov.sg). It employs a regex-based pattern match for common brand-keywords combined with high-risk top-level domains, identifying potential phishing activity associated with the JWR framework that utilizes algorithmically varied, single-suffix rotated lookalike domains.
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