Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule monitors DNS query logs for connections to common public tunneling and proxy services (such as Cloudflare Tunnel, Ngrok, and others) originating from internal private IP address spaces. These services are frequently abused by adversaries to create unauthorized external access channels (reverse tunnels) into a private network, facilitating command and control or data exfiltration while bypassing firewall restrictions.
This rule detects potential malicious activity by monitoring for known malicious file hashes (MD5) across file and process events, and correlating them with known Command and Control (C2) infrastructure communication patterns, including specific IP addresses and URI paths.
This rule detects potential credential theft by monitoring for file access (creation, modification, or renaming) to common web browser sensitive files (Login Data or Cookies) followed by an outbound network connection from the same process within a 10-minute window. This behavior often indicates an adversary attempting to exfiltrate browser-stored credentials or session cookies.
Detects host-based activity or network communication associated with the SloppyRAT remote access trojan. The rule monitors for specific malicious file hashes and connection attempts to known C2 IP addresses and domains.
Detects anomalous AI orchestration or API gateway activity that suggests potential command and control (C2) communication. The rule monitors for unauthorized external endpoints, high-frequency low-payload beaconing patterns, and unauthorized cross-tenant agent-to-agent calls.
This rule monitors endpoint telemetry for occurrences of known malicious file hashes, C2 IP addresses, and C2 domains. It aggregates file creation/modification events, process execution, and network connections to detect activity associated with known malicious entities.
Detects anomalous high-volume API requests from a single user/key to AI model inference endpoints. The rule tracks query diversity and total volume within an hour to identify behavior consistent with model extraction or cloning (AML.T0024.002) via systematic input/output pair collection.
This rule detects network connections from internal devices to known SpiceRAT Command and Control (C2) infrastructure IPs. The detection filters for successful connections on common ports (80, 443) that meet a volume threshold, indicating potential ongoing beaconing or communication with malicious infrastructure.
Detects network activity associated with the 'ClickFix' campaign, specifically targeting the cdnsuicaches[.]com domain. The rules identify the initial DNS query, the subsequent TLS SNI connection, and the retrieval of malicious JavaScript containing the 'cookie-captcha-complete' overlay signature. This behavior is indicative of a browser-based social engineering attack.
Detects network activity associated with the 'ClickFix' campaign, specifically targeting the cdnsuicaches[.]com domain. The rules identify the initial DNS query, the subsequent TLS SNI connection, and the retrieval of malicious JavaScript containing the 'cookie-captcha-complete' overlay signature. This behavior is indicative of a browser-based social engineering attack.
This rule detects instances where a process associated with a known StealC command-and-control (C2) IP address subsequently spawns a hidden cmd.exe instance. It correlates network connections to known malicious infrastructure with the creation of hidden command shells within the same device session, specifically looking for common parent processes like web browsers or system utilities.
Detects anomalous activity where an AI coding assistant process reads sensitive local credential files (such as .env, .aws/credentials, or SSH keys) followed by an outbound network connection to a destination not associated with known package registries or AI provider APIs. This pattern is indicative of potential prompt-injection attacks targeting autonomous AI coding tools to exfiltrate developer secrets.
Detects the execution of binaries or scripts named with keywords related to AI agent plugins (e.g., 'skill', 'plugin', 'mcp-server') from potentially untrusted locations like Downloads or Temp folders. The rule further correlates this execution with post-exploitation indicators such as persistence establishment (registry keys, scheduled tasks, launch agents) or outbound network activity.
This rule monitors for potential MeshAgent activity associated with the Settra threat actor. It identifies processes that are likely renamed instances of MeshAgent (via metadata inspection), processes attempting to communicate with known C2 IP addresses, and direct network connections to these C2 IP addresses.
Detects instances where an AI agent invokes sensitive administrative or system tools (shell execution, file writing, network communication, or email sending) shortly after ingesting untrusted external content, in the absence of explicit, logged user instructions for those specific actions. This behavior is a primary indicator of indirect prompt injection attacks aimed at achieving command execution or C2-like behavior.
Detects incoming EXTERNAL emails containing subject lines indicative of meeting invitations or call records, which include external URLs pointing to non-standard or unverified cloud storage providers, combined with explicit remote-access/support solicitation language. Requiring the remote-access/support signal and excluding internal-to-internal mail narrows this to callback-phishing style lures rather than routine meeting correspondence.
This rule detects potential unauthorized use of MeshAgent RMM software by correlating process execution events (mvtcs.exe, MeshAgent) with network traffic to specific IP addresses identified as associated with Settra command-and-control infrastructure. It specifically filters for MeshAgent activity and validates the threat by requiring an established network connection to known malicious endpoints.
This rule detects potential unauthorized use of MeshAgent RMM software by correlating process execution events (mvtcs.exe, MeshAgent) with network traffic to specific IP addresses identified as associated with Settra command-and-control infrastructure. It specifically filters for MeshAgent activity and validates the threat by requiring an established network connection to known malicious endpoints.
Detects outbound HTTP requests to the Telegram Bot API associated with the malicious 'indexed-btree' npm package. The rule specifically monitors for hardcoded Telegram bot tokens and chat IDs used for data exfiltration.
Detects outbound HTTP requests to the Telegram Bot API associated with the malicious 'indexed-btree' npm package. The rule specifically monitors for hardcoded Telegram bot tokens and chat IDs used for data exfiltration.
Detects outbound network traffic attempting to exfiltrate data to Slack or Telegram APIs using hardcoded bot tokens or channel identifiers associated with the 'indexed-btree' malware campaign.
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