Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous, high-volume file download or export activity originating from a single identity across multiple downstream cloud applications or tenants within a one-hour window. This behavior is indicative of a compromised shared service provider, MSP, or clearinghouse account being leveraged to exfiltrate data from multiple client environments simultaneously. The detection excludes pre-approved service accounts and relies on a dynamic baseline comparison rather than a static threshold.
Detects anomalous, high-volume file download or export activity originating from a single identity across multiple downstream cloud applications or tenants within a one-hour window. This behavior is indicative of a compromised shared service provider, MSP, or clearinghouse account being leveraged to exfiltrate data from multiple client environments simultaneously. The detection excludes pre-approved service accounts and relies on a dynamic baseline comparison rather than a static threshold.
Detects anomalous, high-volume file download or export activity originating from a single identity across multiple downstream cloud applications or tenants within a one-hour window. This behavior is indicative of a compromised shared service provider, MSP, or clearinghouse account being leveraged to exfiltrate data from multiple client environments simultaneously. The detection excludes pre-approved service accounts and relies on a dynamic baseline comparison rather than a static threshold.
Detects anomalous, high-volume file download or export activity originating from a single identity across multiple downstream cloud applications or tenants within a one-hour window. This behavior is indicative of a compromised shared service provider, MSP, or clearinghouse account being leveraged to exfiltrate data from multiple client environments simultaneously. The detection excludes pre-approved service accounts and relies on a dynamic baseline comparison rather than a static threshold.
Detects Kothamine Agent via hardcoded base64 AES-GCM key combined with C2 loop markers and plugin command strings, gated on PE structure to reduce false positives
Detects Kothamine Agent via hardcoded base64 AES-GCM key combined with C2 loop markers and plugin command strings, gated on PE structure to reduce false positives
Detects anomalous credential access behavior by identifying instances where a compromised explorer.exe process (injected with the Kothamine payload DLL MicrosoftEdgeUpdateCore.dll) accesses browser cookie stores and gaming credential files in quick succession.
Detects the ReportDump.exe process initiating a network connection on port 15847 followed by the loading of GDI/User32 libraries shortly thereafter. This behavior is indicative of SectopRAT establishing a command-and-control channel and initializing screen capture capabilities.
Detects an AI agent identity accessing an unusually high number of distinct internal repositories, such as wikis, ticketing systems, code repositories, or customer data stores within a 15-minute window. This behavior is consistent with automated discovery or collection (T1213) by an AI agent.
KQL Query from file: JivaChat Installer - Full Chain
Detects a sequence of potentially malicious activity involving the bulk reading or downloading of sensitive AI-related artifacts (model files, datasets), followed by the staging of these files into a compressed archive, and finally an outbound network transfer to an external IP address. This behavior pattern is indicative of data exfiltration of intellectual property.
This rule detects a multi-stage attack correlation where a user receives multiple inbound emails flagged for high likelihood of being LLM-generated (and containing urgency/credential-harvesting language) and subsequently performs a voice or video authentication attempt that is flagged by deepfake detection systems with a low confidence score, indicating a potential impersonation attempt.
This rule detects a multi-stage Business Email Compromise (BEC) attack involving executive impersonation via look-alike domains, followed by the creation of suspicious inbox rules (forwarding/deletion) by the victim, and concurrent external communication (Teams/Zoom meeting invites) from a newly created account. It correlates these activities to identify sophisticated payment fraud campaigns.
This rule detects a multi-stage Business Email Compromise (BEC) attack involving executive impersonation via look-alike domains, followed by the creation of suspicious inbox rules (forwarding/deletion) by the victim, and concurrent external communication (Teams/Zoom meeting invites) from a newly created account. It correlates these activities to identify sophisticated payment fraud campaigns.
Detects high-privilege OAuth consent grants to applications shortly followed by suspicious mailbox modifications, such as creating new inbox or transport rules. This behavior is indicative of a post-compromise activity where an adversary uses an illicitly granted OAuth application to maintain persistence or exfiltrate data from an Exchange Online environment.
Detects anomalous authentication behavior where the same session token or identifier is utilized by multiple distinct IP addresses or User-Agent strings within a short timeframe. This behavior is indicative of Adversary-in-the-Middle (AiTM) attacks, such as those employing Evilginx, where an attacker intercepts and replays stolen session cookies to bypass standard authentication and MFA controls.
Detects network traffic patterns associated with the ORAX/OraxRDP AiTM (Adversary-in-the-Middle) phishing toolkit. The rule monitors for specific SignalR WebSocket upgrade requests and session-related GraphQL commands (such as MirrorChallenge, Init, and Input) used to facilitate MFA relay and web session hijacking.
Detects instances where a process not associated with standard web browsers accesses, copies, or modifies sensitive browser files such as cookies, local storage, or session tokens. This behavior is highly indicative of information-stealer malware (e.g., Vidar, Lumma, RedLine) attempting to exfiltrate session data for account hijacking.
Detects a specific phishing pattern where a legitimate, authenticated DocuSign notification is followed by a user clicking a link within that document that redirects to known malicious infrastructure or OAuth-abuse sites (e.g., .vu domains, credential harvesting proxies). This heuristic identifies the link as being embedded in the shared document rather than the email body.
Detects network indicators and session patterns associated with the JWR Phishing Framework, specifically targeting its WebSocket-based command and control (C2) communication, client engine (main.js/ws-worker.js) retrieval, and unique session token (JWRCVV) usage.
Detects anomalous, high-volume activity in cloud-based services such as SharePoint, OneDrive, and Exchange, specifically tracking file access, downloads, and mailbox exports. This behavior may indicate bulk data collection, unauthorized exfiltration, or malicious data staging by a compromised account.
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