Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects network connections from internal devices to a list of known malicious domains and IP addresses. These indicators are commonly associated with command and control (C2) infrastructure or malicious activity, and alerting on these connections can help identify compromised systems within the environment.
This rule detects network connections from internal devices to a list of known malicious domains and IP addresses. These indicators are commonly associated with command and control (C2) infrastructure or malicious activity, and alerting on these connections can help identify compromised systems within the environment.
Detects DNS queries to known malicious subdomains and direct TCP connections to an IP address associated with the SilkParasite, NodeEdgeRAT, and NomadRAT malware infrastructure. The rule specifically monitors for communication with identified C2 infrastructure hosts and legacy domain registration points.
Detects Node.js processes making outbound network connections to the AWS Instance Metadata Service (IMDS) IP address 169.254.169.254, which may indicate unauthorized data collection or discovery via server-side request forgery (SSRF).
This rule monitors for execution of suspicious files or processes and network communication associated with known malicious indicators (hashes, domains, and IP addresses) typically used by specific threat actors for command and control (C2) and payload delivery.
Detects activity associated with a potential RMM (Remote Monitoring and Management) agent communicating with known malicious infrastructure and accessing sensitive files like credentials or IDs. This often indicates an adversary leveraging legitimate RMM tooling for post-compromise activity or exfiltration.
This rule detects potential 'Quishing' (QR code phishing) attempts by identifying email-based phishing detection or malicious URL indicators followed by a user clicking the URL and subsequently authenticating to a corporate system from a mobile device (Android or iOS) within a 30-minute window. This behavior often suggests an adversary successfully harvesting credentials via a QR code link and then using them to gain unauthorized access.
Detects anomalous activity in a CI/CD environment where a runner process accesses sensitive credential files (such as PyPI tokens or GitHub secrets) followed closely by a network connection to public package registry domains, potentially indicating a supply-chain compromise where stolen credentials are used to publish malicious packages.
Detects MSBuild.exe establishing a network connection to a specific remote IP address (212.34.141.103) on port 4521. This behavior is indicative of MSBuild being used to proxy execution of malicious code, often associated with downloading and executing second-stage payloads or beaconing to a command-and-control (C2) server.
This rule monitors for suspicious network traffic to SMTP services and specific external email addresses, as well as process command lines containing these indicators. Additionally, it tracks email events involving specific sender, recipient, and subject indicators which may be indicative of unauthorized communication or data exfiltration attempts.
Detects outbound network traffic attempting to interact with a specific Polygon smart contract address using eth_call. This behavior is indicative of the EtherHiding technique, where adversaries leverage blockchain infrastructure as a dead-drop resolver for command-and-control (C2) operations to bypass traditional network defenses.
Detects outbound network requests from endpoints to known 'uncensored' or jailbroken LLM-as-a-Service domains (e.g., WormGPT, FraudGPT). These platforms are specifically marketed to threat actors for illicit purposes like generating malware, phishing kits, or exploit code, and identifying access to them serves as a leading indicator of adversarial activity.
Detects outbound network requests from endpoints to known 'uncensored' or jailbroken LLM-as-a-Service domains (e.g., WormGPT, FraudGPT). These platforms are specifically marketed to threat actors for illicit purposes like generating malware, phishing kits, or exploit code, and identifying access to them serves as a leading indicator of adversarial activity.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
This rule detects network connections and DNS queries directed towards specific malicious domains or domains matching a pattern hosted on pages.dev, a service frequently abused for hosting malicious infrastructure such as phishing pages or command-and-control beacons.
Detects instances where a single process on a device communicates with two or more distinct hosted LLM provider APIs (OpenAI, Anthropic, DeepSeek, Google) within a 24-hour window. This behavior is indicative of multi-model orchestration or 'consensus' techniques, which may be employed by sophisticated implants for automated tasking, data processing, or evasion.
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