Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
5,879 detections
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Detects a suspicious sequence of events where a host performs multiple reconnaissance-style probes (health checks, documentation, or OpenAPI definitions) followed within 10 minutes by access to sensitive artifact-related URLs on the same remote host. This pattern is indicative of an attacker profiling a target server for metadata and then proceeding to exfiltrate files or sensitive artifacts.
Detects a suspicious sequence of events where a host performs multiple reconnaissance-style probes (health checks, documentation, or OpenAPI definitions) followed within 10 minutes by access to sensitive artifact-related URLs on the same remote host. This pattern is indicative of an attacker profiling a target server for metadata and then proceeding to exfiltrate files or sensitive artifacts.
Detects network connections to known SideCopy/ReverseRAT command-and-control infrastructure, including a static C2 IP address and two C2/hosting domains (matched exactly and as proper subdomains to avoid substring false positives).
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 network activity associated with the OnyxC2 malware, including DNS queries to .top TLDs for beaconing, subsequent HTTP check-ins to .top domains, and known TLS SNI matches for OnyxC2 C2 infrastructure.
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 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.
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.
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.
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.
This rule monitors for outbound TCP and HTTP traffic to a known malicious infrastructure hosting a fake or compromised 'Corp MDM' (Mobile Device Management) admin panel, which serves as a Command and Control (C2) channel for spyware.
Detects high-volume database or CRM read activity performed by a user, followed by significant outbound network traffic to known public cloud storage, file-sharing, or messaging services. This pattern is consistent with data staging and exfiltration behaviors often attributed to the ShinyHunters threat actor group.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
This rule detects various malicious indicators including known file hashes, IP addresses, domains, and specific URLs associated with threat activity. It consolidates hits from process, file, and network telemetry to alert on potential compromise or communication with identified command-and-control (C2) infrastructure.
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