Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects periodic outbound network connections from rnpkeys.exe characteristic of the Sauron Loader C2 beaconing. The rule identifies a consistent heartbeat pattern (approx. 600 seconds) between successful C2 polls to the same remote host, distinguishing it from typical user-driven browser traffic.
This rule monitors for activity associated with specific known malicious file names (killer.exe, kill.exe) and network connections to a set of identified malicious IP addresses. It aggregates events from DeviceFileEvents, DeviceProcessEvents, and DeviceNetworkEvents to identify potential communication or presence of these malicious artifacts.
This rule monitors for activity associated with specific known malicious file names (killer.exe, kill.exe) and network connections to a set of identified malicious IP addresses. It aggregates events from DeviceFileEvents, DeviceProcessEvents, and DeviceNetworkEvents to identify potential communication or presence of these malicious artifacts.
IOC sweep against network telemetry for confirmed BengalSEO/MayaBot campaign infrastructure: the Hostmaza backend IP (5.101.140.80), Matomo tracking domains (stats.us3.org, us3.my), named MayaBot C2 domains (cus.cam, dll.lat, us99.org), and the ~170-domain Traffic Distribution System redirector fleet used to funnel victims to payload delivery. The source intel report contains no file hashes (MD5/SHA1/SHA256) -- only IPs, domains, and URLs -- so this sweep is IP/domain-only. Note the much larger corpus of single-use lure-page domains (~1,000 additional indicators) is excluded as too high-churn for static embedding.
Detects high-frequency requests directed at common model-serving API endpoints, which may indicate an attacker attempting to perform model extraction or model inversion attacks by repeatedly querying the model to infer its parameters or training data.
This rule detects high-frequency HTTP 401 Unauthorized responses directed at API endpoints (specifically matching '/v1/') within a short window, which is indicative of an adversary attempting to brute-force or credential-stuff API keys.
Detects HTTP(S) requests to known SideCopy/ReverseRAT payload-delivery URLs identified in Operation SideCopy spear-phishing campaigns targeting Indian academia.
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).
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).
Detects web-proxy or webserver log activity associated with domains registered within the last 30 days that mimic branding or structure of major identity providers (Microsoft, Okta, Google, Duo). This behavior is characteristic of adversary-in-the-middle (AiTM) phishing infrastructure staging.
Detects known file hashes associated with SideCopy/ReverseRAT.
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 monitors for indicators of compromise (IOCs) associated with Operation SideCopy, including specific file hashes, known command-and-control (C2) IP addresses, malicious domains, and URLs. It triggers on file creation, process execution, and network connections matching these known indicators.
This rule monitors for known string patterns associated with the initialization and command execution of the Metasploit Meterpreter reverse TCP payload. By inspecting network traffic for characteristic C2 handshake and command strings, it identifies active Meterpreter sessions.
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.
Detects an Active Directory Certificate Services (AD CS) Certificate Enrollment Service (CES) SOAP request that includes a 'CertificateTemplate' item in the 'AdditionalContext' body, potentially indicating a directed attempt to request a specific certificate template during enrollment.
Detects DNS-based Kerberos coercion attacks where adversaries inject marshaled credential structures into DNS records to spoof SPNs and redirect authentication such as in CVE-2025-33073. This detection leverages suricata looking for specific CREDENTIAL_TARGET_INFORMATION structures in DNS queries.
Detects non-browser and non-approved application processes establishing outbound network connections to known public LLM/AI API endpoints, such as OpenAI or Azure OpenAI services. This behavior is indicative of potential command-and-control (C2) activity where an attacker abuses legitimate AI APIs as a bidirectional communication channel to poll for instructions.
Detects the presence of known Vidar infostealer samples by matching file hashes against a list of identified malicious artifacts. The rule monitors for file creation events, process execution (both as the primary process and as an initiating process), and network activity originating from these known malicious files.
Detects incoming TLS traffic from an external network where the TLS server certificate's Subject Common Name (CN) field matches 'AsyncRAT Server'. This pattern is a known indicator of a beaconing session initiated by AsyncRAT remote access trojan servers.
Detects DNS queries for the domain 'pdf.gusercontent.com', which is known to be used as a lookalike domain for Google-related services to deceive users. This pattern is often indicative of malicious infrastructure used for credential harvesting, phishing, or malware delivery.
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