Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
40,063 detections
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Detects a malicious payload masquerading as a ClaudeDesktop installer that uses DLL sideloading via a tampered libcef.dll and a repurposed JetBrains binary to execute the SectopRAT .NET RAT.
Detects the creation of a new local account that is immediately followed by (or associated with) execution from a process identified as an AI development or agent-based tool (e.g., Cursor, GitHub Copilot, Ollama). The rule correlates process creation events with Windows Security Event ID 4720 (User account created) and optionally checks for subsequent addition to the local Administrators group. This logic aims to surface potential unauthorized credential creation triggered by automated AI coding assistants or LLM-driven workflows.
This rule detects network activity and cloud API events indicative of an adversary utilizing the VAPI.ai platform to orchestrate automated voice-phishing (vishing) campaigns. It correlates outbound network connections to VAPI.ai endpoints with specific webhook callback patterns used to track call status for victim records.
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.
This rule monitors for indicators of compromise (IOCs) associated with the threat actor SideCopy and their associated malware, such as ReverseRAT. It hunts for specific malicious SHA256 file/process hashes, connections to known malicious C2 infrastructure (IPs and domains), requests to known malicious URLs, and user interaction with these URLs via email clicks.
This rule detects malicious activity by monitoring for specific known indicators, including hashes of malicious files (ProcessRollup2), network connections to known C2 infrastructure (NetworkConnectIP4), and URL clicks (UrlClick) associated with malicious domains or paths. It acts as a multi-stage indicator correlation rule to identify execution or communication with known threats.
Detects the use of a malicious 'borlndmm.dll' file intended for DLL side-loading to deploy the OnyxC2 stealer payload. The rule identifies files masquerading as NVIDIA graphics libraries or Borland memory managers that contain embedded encrypted payloads, matching known indicators for OnyxC2 activity.
Detects OnyxC2 RunPE injection behavior where a process drops or loads a suspicious sideloaded DLL (specifically borlndmm.dll) and immediately initiates a secondary process instance, indicative of process hollowing or memory unpacking of a malicious payload.
Detects the specific behavior associated with the OnyxC2 C2 framework where a process re-spawns a child instance of itself, followed by a rapid registry modification by that child process. This pattern serves as a marker for the ONYXC2 loader configuration stage.
Detects unauthorized processes attempting to access the memory of the Local Security Authority Subsystem Service (LSASS) with high-privilege access masks, a technique commonly used for credential dumping to harvest passwords and authentication tokens.
Detects the use of WinRAR to open or extract password-protected ZIP archives containing 'Setup_File' in the filename, immediately followed by the execution of a file extracted into a WinRAR temporary directory. This behavior is associated with the OnyxC2 delivery chain, where attackers use password-protected archives to bypass security scanning.
Detects the use of PowerShell processes that employ command-line encoding (e.g., -enc, -EncodedCommand) in conjunction with download-related cmdlets (e.g., DownloadString, IEX, Invoke-WebRequest), or instances of PowerShell spawned as a child process of another PowerShell process. This pattern is indicative of a loader or stage-one script fetching additional malicious payloads.
This rule detects network communication with known malicious infrastructure (C2 IPs and URLs), presence of malicious files identified by SHA256 hashes on disk, and execution of known malicious files. It also correlates these activities with specific operator email addresses involved in the malicious campaign.
This rule detects network communication with known malicious infrastructure (C2 IPs and URLs), presence of malicious files identified by SHA256 hashes on disk, and execution of known malicious files. It also correlates these activities with specific operator email addresses involved in the malicious campaign.
Detects a suspected EvilTokens attack sequence where a user clicks a phishing URL and subsequently initiates a browser session to the Microsoft device-code authorization page (devicelogin) within 15 minutes, with the phishing lure domain appearing in the browser context.
Detects a suspected EvilTokens attack sequence where a user clicks a phishing URL and subsequently initiates a browser session to the Microsoft device-code authorization page (devicelogin) within 15 minutes, with the phishing lure domain appearing in the browser context.
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.
Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
Detects the anomalous behavior of a Node.js process acting as a typosquatting agent. The detection identifies a node.exe process initiating a network connection to a known malicious C2 IP and port, followed by the termination of that same process within 45 seconds. This pattern is consistent with the agent crashing due to an unhandled exception (e.g., os.userInfo() failure) after beaconing.
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