Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule performs a multi-source correlation (network, DNS, file, and process telemetry) to detect artifacts associated with the Coruna campaign. It looks for known malicious file hashes (SHA1), C2 IP addresses, and specific domain/URL patterns in process command lines and network connections.
Detects network activity associated with DPRK-affiliated threat actors, specifically connections to known hardcoded C2 IP addresses and DNS queries for malicious o-r.kr subdomains utilized for command and control.
Detects indicators associated with the ChatGPT Custom GPT ClickFix campaign across process, network connection, DNS, and HTTP telemetry: known malicious file hashes, C2 IP addresses, the chattypetty.com domain, and known payload-hosting URLs.
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.
IOC hunt across DNS, network, and file-hash telemetry for the Ledger Google Ads phishing campaign. Vercel redirect domains are matched by exact hostname (DNS query name / parsed URL host) rather than substring, eliminating false positives from unrelated strings that merely contain a look-alike domain fragment. GCS bucket and Google Sites path IOCs remain substring-matched since the bucket IDs and page slugs are already highly specific.
Detects the installation of unauthorized AI-related browser extensions followed by outbound network communication from the browser process to known external AI service API endpoints within one hour. This behavior is indicative of potential data exfiltration via shadow AI tools, bypassing standard enterprise DLP controls.
This rule detects potential post-compromise activity related to a campaign using MSP360-masqueraded installers and ScreenConnect remote access tools. It performs an IOC sweep across device file events for known malicious file hashes (associated with installers and post-compromise utilities) and device network events for connections to known malicious domains used for command and control.
This rule detects potential post-compromise activity related to a campaign using MSP360-masqueraded installers and ScreenConnect remote access tools. It performs an IOC sweep across device file events for known malicious file hashes (associated with installers and post-compromise utilities) and device network events for connections to known malicious domains used for command and control.
This rule monitors for known malicious file hashes, command and control (C2) domains, and specific download URLs associated with identified threats. It correlates these indicators across process execution, file activity, and network connection logs to identify potential compromises or malicious activity related to the aware-cr1 infrastructure.
This rule monitors for known malicious file hashes, command and control (C2) domains, and specific download URLs associated with identified threats. It correlates these indicators across process execution, file activity, and network connection logs to identify potential compromises or malicious activity related to the aware-cr1 infrastructure.
Detects a sequential pattern where a browser process (Chrome, Edge, or Safari) accesses an AI chat platform's conversation API, followed within 5 minutes by a network connection to a known unauthorized exfiltration destination (api.pbapi.xyz). This pattern suggests the potential use of a browser-based tool or extension (e.g., Poper Blocker or similar) to intercept and exfiltrate AI chat history.
Detects TLS connections to a specific, potentially adversary-controlled ScreenConnect relay infrastructure endpoint. Adversaries frequently abuse legitimate remote access tools like ScreenConnect for command and control purposes.
Detects DNS lookups and outbound network connections to known command and control (C2) and staging domains associated with the STAC4924 campaign. The rule performs strict matching on domain names to ensure that subdomains are identified while avoiding false positives from partial string matches within URLs.
Detects attempts by potentially malicious processes, such as web-browsing tools, scripts, or agents, to access the cloud instance metadata service (169.254.169.254) or associated metadata paths. This behavior is indicative of Server-Side Request Forgery (SSRF) exploitation aimed at stealing cloud instance credentials.
Detects network connection attempts by known AI agent or development-related runtime processes (python, node, etc.) to external endpoints that are not contained within a defined allowlist of LLM and infrastructure providers. This is intended to identify potential data exfiltration or unauthorized command and control communication originating from LLM-powered applications.
Detects outbound TCP traffic on port 443 destined for a hardcoded IP address associated with ValleyRAT C2 activity. The rule specifically monitors for established connections with a small payload size (5 bytes) that lack standard TLS handshake characteristics (no SNI), and applies a threshold to mitigate noise. This behavior is indicative of C2 heartbeats from malicious implants using DLL side-loading techniques.
A July 2026 incident used Polygon smart contracts (EtherHiding) for dynamic payload routing/C2 following a ClickFix/FileFix-style lure driven by compromised WordPress credentials.
This rule monitors for indicators of compromise (IOCs) associated with known malware and C2 infrastructure, including specific IP:port combinations, malicious domains, URLs for file downloads, and SHA256 hashes of known malware. The rule correlates data across device network events, file events, and process execution events to identify potential infections or communication with malicious infrastructure.
This rule detects known malicious activity including network connections to a specific C2 IP address (69.48.229.140), downloading files from specific C2 URLs, the presence of known malicious file hashes (SHA256), and communications involving specific actor-associated email addresses.
Detects HTTP GET requests for files with a .hta extension, utilizing a legacy Internet Explorer User-Agent string. This pattern is commonly associated with SideCopy threat group activities where mshta.exe is used to proxy the execution of remotely hosted malicious HTA files, often as part of an initial infection chain.
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.
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