Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous network polling patterns consistent with 'EvilTokens' or similar adversary-in-the-middle (AiTM) OAuth phishing kits. The rule identifies web browsers performing high-frequency, low-interval polling (3-6s) against non-reputable/unseen domains (the attacker-controlled status endpoint) immediately preceding or following successful Microsoft OAuth device-code authentication sequences.
Detects process and file creation activity matching known MovieReaper loader and downstream module file hashes (MD5/SHA256). C2 IP/domain network indicators for this campaign require a separate network_connection-category rule, since Sigma logsource categories cannot be mixed within a single rule.
Detects browser-based network requests to 'script.google.com'. This can indicate usage of Google Apps Scripts, which are sometimes abused by attackers for Command and Control (C2) infrastructure, data exfiltration, or as a proxy for malicious payloads, by blending in with legitimate Google traffic.
This rule detects the installation or operation of browser extensions that request overly broad host permissions (e.g., all URLs) or specifically target popular AI services (like Gemini, Copilot, or Claude). It also monitors the use of the declarativeNetRequest API to interact with these same AI service domains, which is a technique used by malicious extensions to intercept or manipulate web traffic.
Detects activity associated with the 'MovieReaper' threat campaign by identifying known malicious file hashes, command and control (C2) IP addresses, and communication with identified malicious domains. This rule monitors process execution, file operations, and network connections within the campaign's active timeframe.
Detects internal SMB traffic indicative of P2P beaconing using the IPC$ named pipe, which is often a sign of lateral movement or command-and-control communication between compromised hosts.
Detects unusual network connections over port 445 involving SMB named pipes commonly associated with lateral movement tools or P2P beaconing. The rule identifies communication through suspicious or generic pipe naming conventions that deviate from standard system process behavior (e.g., lsass, services, svchost, System).
Detects network connections to known FLATROOF and ROOFDECK C2 infrastructure used by Jade Sleet/TraderTraitor (UNC4899, Lazarus) in campaigns targeting IT services and DevOps engineers.
Detects network connections to known FLATROOF and ROOFDECK C2 infrastructure used by Jade Sleet/TraderTraitor (UNC4899, Lazarus) in campaigns targeting IT services and DevOps engineers.
Detects HTTP responses containing a file named 'ClaudeDesktop.exe' served from paths containing '/artifacts/' on the 'claude.ai' host where the request referrer is not 'claude.ai' or 'anthropic.com', suggesting a malvertising or supply chain delivery attempt of a malicious installer.
Detects HTTP responses containing a file named 'ClaudeDesktop.exe' served from paths containing '/artifacts/' on the 'claude.ai' host where the request referrer is not 'claude.ai' or 'anthropic.com', suggesting a malvertising or supply chain delivery attempt of a malicious installer.
This rule detects activities associated with the AMOS (Atomic macOS Stealer) malware family by monitoring for known malicious file hashes on endpoints and network communication with domains linked to its command-and-control infrastructure.
This rule detects activities associated with the AMOS (Atomic macOS Stealer) malware family by monitoring for known malicious file hashes on endpoints and network communication with domains linked to its command-and-control infrastructure.
This rule detects potential Adversary-in-the-Middle (AiTM) phishing activity by correlating network requests to known malicious lure domains with help-desk or vishing-themed events (such as password resets or unsolicited IT support) for the same user within a one-hour window. This behavior is indicative of a help-desk impersonation campaign where attackers direct users to credential harvesting sites.
This rule monitors for network connections, firewall traffic, and Entra ID authentication events involving known malicious IP addresses (the 'Wall of Shame'). The rule specifically flags interactions occurring over common VPN ports, suggesting potential unauthorized access or persistence attempts via VPN services.
This rule monitors for indicators of compromise (IOCs) associated with the Rapuncel/Cruciferra 'Bring Your Own Vulnerable Driver' (BYOVD) malware-as-a-service (MaaS) campaign. It detects malicious file hashes (associated with dropped binaries or drivers), connections to known malicious command-and-control (C2) IP addresses, and network requests to domains used by the infrastructure.
This rule monitors for indicators of compromise (IOCs) associated with the Rapuncel/Cruciferra 'Bring Your Own Vulnerable Driver' (BYOVD) malware-as-a-service (MaaS) campaign. It detects malicious file hashes (associated with dropped binaries or drivers), connections to known malicious command-and-control (C2) IP addresses, and network requests to domains used by the infrastructure.
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 high-frequency queries to machine learning model inference endpoints involving probability or confidence score outputs. This behavior is indicative of a membership inference attack, where an adversary attempts to determine if specific data samples were included in a model's training set by harvesting confidence scores.
Detects high-frequency, sustained queries to an ML inference endpoint from a single identity, indicative of gradient-free training data reconstruction or model inversion attacks. The rule triggers when a specific user or API key performs over 100 successful requests within a 15-minute window, where the responses include granular confidence, probability, or logit values, which are typical targets for reconstructing training data.
Detects anomalous high-frequency queries to machine learning model inference endpoints involving probability or confidence score outputs. This behavior is indicative of a membership inference attack, where an adversary attempts to determine if specific data samples were included in a model's training set by harvesting confidence scores.
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