Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects DNS queries for the domain 'pdf.gusercontent.com', which is known to be used as a lookalike domain for Google-related services to deceive users. This pattern is often indicative of malicious infrastructure used for credential harvesting, phishing, or malware delivery.
Detects network activity associated with the Psychedelic Stealer malware, including C2 check-ins, API-based tasking, and communication with known C2 infrastructure using specific URI patterns and headers like 'X-API-Key'.
Detects HTTP traffic patterns associated with the Psychedelic Stealer malware command and control (C2) agent. The rule matches specific URI paths used for pinging, configuration retrieval, and tasking, while verifying the presence of the required 'X-API-Key' header, indicating established C2 communication.
Detects HTTP traffic patterns associated with the Psychedelic Stealer malware command and control (C2) agent. The rule matches specific URI paths used for pinging, configuration retrieval, and tasking, while verifying the presence of the required 'X-API-Key' header, indicating established C2 communication.
Detects network communication associated with the CARBONATO Hermes Agent, including direct C2 beaconing to a hardcoded IP on port 8989 and TLS connections to Vercel-hosted proxy infrastructure identifying as 'carbonato-proxy'.
Detects a suspected anti-debugging and anti-instrumentation sequence on Android devices. This rule identifies instances where 'getprop' is used to query system properties related to device security (ro.debuggable, ro.secure) followed shortly by a local network probe to the standard Frida instrumentation server port (27042). This combination is indicative of malware gating its execution to ensure it is not running within a controlled or debugged environment.
Detects the staging of a potential RatHat malware component by observing a native Go binary being moved into /data/local/tmp via ADB shell (uid 2000), followed by an associated service binding to local port 7912 within a 5-minute window. This sequence is characteristic of the deployment process for this specific Android threat.
Detects a specific Android sandbox escape technique where a malicious app enables Accessibility Services and Developer Options/Wireless Debugging to initiate a self-pairing ADB connection over localhost. This bypasses typical app sandbox restrictions, allowing the execution of processes with the UID 2000 (shell) context.
Detects network connection attempts to known public DNS-over-HTTPS (DoH) resolvers from specific binaries identified as host processes for SectopRAT, a malware often associated with ClickFix social engineering campaigns.
Detects network connections from workstation subnets to domain controllers using administrative protocols such as SMB (445), RPC (135), RDP (3389), or WinRM (5985/5986). This activity is indicative of lateral movement attempts by an adversary attempting to reach critical infrastructure from a compromised workstation in a flat or poorly segmented environment.
Detects signs of Zerologon (CVE-2020-1472) exploitation by correlating Netlogon (MS-NRPC) authentication anomalies involving known exploitation tools/process names with subsequent machine-account password reset events (Event ID 4742) occurring on the same device within a short time window.
Detects a network activity pattern from a single host characterized by a high volume of connections to a wide range of remote hosts and ports, combined with an exceptionally low coefficient of variation in inter-request timing. This pattern is indicative of an automated, scriptable agent or AI-driven reconnaissance tool rather than a human-operated scanner, likely performing automated asset discovery or network enumeration.
Detects a single source IP address rapidly attempting connections to multiple distinct devices and ports within a short time window. This behavior is indicative of an automated agent or scanner performing reconnaissance and potentially attempting to chain exploitation across multiple targets.
Detects web reconnaissance activity characterized by the use of User-Agent strings associated with automated AI agent frameworks and headless browser libraries (e.g., LangChain, AutoGPT, Playwright, Selenium, python-requests). These tools are frequently utilized in autonomous exploitation scenarios to scan multiple URI paths on a web server in a short timeframe.
Detects HTTP requests containing potential session, handoff, or access tokens within the URI pattern commonly associated with ORAX Adversary-in-the-Middle (AiTM) phishing infrastructure. The rule specifically targets requests routed to wildcard subdomains that mimic legitimate deployment or service endpoints, indicating an attempt to harvest authentication tokens from victims.
Detects network activity associated with the JWR Phishing Framework, specifically focusing on the initiation of a WebSocket upgrade request following the delivery of a checkout-themed page load containing a 'cart_data' parameter, which is characteristic of the framework's C2 communication flow.
Detects a sequence of activity indicative of beacon-like behavior: initial domain reconnaissance using commands such as 'net', 'systeminfo', or 'nltest', followed by the execution of 'rundll32.exe' without command-line arguments, and subsequent LDAP (port 389) communication to a domain controller, all occurring within a short timeframe.
Detects endpoint network activity characterized by a host connection to the Telegram Bot API followed by a connection to a Vercel-hosted domain within 30 minutes. This behavior is indicative of a multi-stage infection chain where adversaries use Telegram for command-and-control communication or victim validation, followed by the retrieval of malicious payloads or lures from disposable infrastructure like Vercel.
Detects network activity associated with the JWR/Outsider phishing kit, which leverages legitimate e-commerce platform parameters (e.g., Shopify/WooCommerce 'cart_data') to mimic storefront infrastructure. The rule correlates requests containing these e-commerce parameters with the loading of the phishing kit's client-side engine scripts or WebSocket workers from the same host, indicating the use of a compromised or malicious storefront-mimicry domain.
Detects the specific network-observable behavior of the JWR client engine. The rule identifies a device loading the engine script (main.js) followed within a short time window by either a fallback redirection to a_index.html or the initiation of a WebSocket connection using a specific obfuscated path pattern associated with JWR command and control.
Detects network proxy requests indicative of Adversary-in-the-Middle (AiTM) phishing kits. The rule monitors for specific URL patterns associated with malicious JavaScript delivery, administrative panels, and connections to known malicious infrastructure hosting phishing activities.
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