Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the creation of anonymous or guest-access sharing links for SharePoint items that are classified as sensitive (having a sensitivity label applied). This activity could indicate potential data exfiltration or unauthorized sharing of protected corporate information.
Detects anomalous authentication patterns where a user account accesses a large number of distinct SaaS/SSO resources within a short time frame from devices or IP addresses not previously associated with the user's historical baseline. This behavior is indicative of a stolen session cookie being replayed by an adversary to perform rapid lateral movement across the victim's cloud footprint.
Detects non-interactive sign-in events in Entra ID that occur without a preceding interactive sign-in session on the same device that successfully satisfied a multi-factor authentication (MFA) challenge. This behavior is indicative of token replay attacks or session hijacking where an adversary attempts to use harvested session cookies or tokens to access resources.
Detects anomalous Entra ID sign-in patterns indicating potential session hijacking or stolen session cookie replay. The rule identifies pairs of successful sign-ins where the geographic distance and time elapsed suggest impossible travel, or where the device/browser context changes without a fresh MFA challenge, suggesting the bypass of authentication requirements via persistent session tokens.
This rule detects potential session hijacking by monitoring for the reuse of session or refresh tokens from unrecognized devices, browsers, or user agents, particularly when such activity occurs without an interactive MFA challenge. This behavior is indicative of an attacker attempting to use stolen session cookies or tokens to access cloud resources.
Detects user authentication events to recruitment, career, or applicant-tracking applications where the connection originates from an anonymizing proxy, VPN, or datacenter IP address. This behavior has been observed as a precursor to North Korean IT worker recruitment fraud, where attackers mask their true geographic location to gain unauthorized employment.
Detects the reuse of session tokens from distinct network locations, devices, or user agents shortly after a successful MFA-protected login. This behavioral pattern is a primary indicator of Adversary-in-the-Middle (AiTM) phishing, where attackers capture session tokens via reverse proxy to bypass authentication controls.
This rule detects inbound Microsoft Teams calls originating from external or federated tenants where the caller's display name attempts to mimic internal IT support, help desk, or departmental roles. This behavior is a hallmark of vishing (voice phishing) attacks, often used as a secondary phase in social engineering campaigns following initial contact via email.
This rule detects potentially malicious Microsoft 365 sign-in events characterized by the use of residential proxy network providers commonly associated with session hijacking activities. It specifically flags sessions that were allowed without a Multi-Factor Authentication (MFA) challenge, suggesting the use of an already authenticated stolen session token.
Detects rapid mass-cloning or downloading of private GitHub repositories shortly after an OAuth token authorization, originating from IP addresses or countries not previously associated with the user account. This behavior is indicative of unauthorized bulk data extraction using compromised credentials, consistent with threat actor activity (e.g., TeamPCP/UNC6780).
Detects instances where a user account triggers a risky sign-in event (flagged by identity protection) and subsequently uses the same session or token to authenticate successfully from a different network location or IP address within two hours. This behavior suggests that a malicious actor may be leveraging a compromised session token that should have been revoked, potentially indicating a failure of continuous access evaluation or token revocation mechanisms.
Detects potential Adversary-in-the-Middle (AiTM) activity by identifying successful multi-factor authenticated sign-in events from the same user across different geographic regions and IP addresses within a one-hour timeframe, which is characteristic of session cookie replay attacks.
Detects OAuth token requests associated with the 'PDF Identity Verifier' application or specific client IDs, often used by malicious browser extensions or phishing applications to exfiltrate sensitive Google account data, specifically Gmail and user profile information.
This rule detects potential illicit consent grants to OAuth applications within Azure AD. It identifies applications requesting high-risk scopes (such as Mail.Read, Files.ReadWrite.All, etc.) that are not verified by a publisher, are granted by non-admin users, and subsequently exhibit high volumes of graph API calls (suggestive of unauthorized data access or exfiltration).
Detects instances where a user grants consent to an unverified OAuth application with high-privilege scopes (e.g., Mail.Read, Directory.Read.All), followed by an authentication event using that same application ID from a different IP address within 24 hours. This behavior is indicative of potential consent phishing or OAuth token abuse, where an adversary harvests tokens to maintain persistent, remote access to email or directory services.
Detects a sequence of potentially malicious local command execution (via PowerShell, cmd, or mshta) that mirrors the 'ClickFix' social engineering pattern, followed by an OAuth application consent grant or device-code authorization by the same user within a short timeframe. This behavior is indicative of an adversary attempting to bypass MFA by tricking a user into authorizing a malicious application after establishing local execution on their endpoint.
Detects potential session hijacking where an Entra ID session is reused by a non-managed/non-compliant device from a different IP and country shortly after an initial authentication from a managed device. This pattern is consistent with infostealer malware stealing session tokens and replaying them on attacker-controlled infrastructure to bypass MFA.
Detects potential session hijacking where an Entra ID session is reused by a non-managed/non-compliant device from a different IP and country shortly after an initial authentication from a managed device. This pattern is consistent with infostealer malware stealing session tokens and replaying them on attacker-controlled infrastructure to bypass MFA.
Detects potential session hijacking where an Entra ID session is reused by a non-managed/non-compliant device from a different IP and country shortly after an initial authentication from a managed device. This pattern is consistent with infostealer malware stealing session tokens and replaying them on attacker-controlled infrastructure to bypass MFA.
Detects potential poisoning of an AI agent's RAG knowledge base or vector store through suspicious bulk ingestion or injection of imperative instruction-style content. The rule correlates these ingestion events with subsequent anomalous tool usage patterns by the same agent session, indicating a possible prompt injection or data manipulation attack aimed at altering agent behavior.
Detects potential poisoning of an AI agent's RAG knowledge base or vector store through suspicious bulk ingestion or injection of imperative instruction-style content. The rule correlates these ingestion events with subsequent anomalous tool usage patterns by the same agent session, indicating a possible prompt injection or data manipulation attack aimed at altering agent behavior.
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