Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects Entra ID Privileged Identity Management (PIM) role activations that occur with missing or generic justification, correlated with a sign-in session that has been flagged as risky (high/medium risk) or non-interactive. This combination suggests a potential session hijacking scenario where an adversary is attempting to elevate privileges using a compromised, legitimate user session.
Detects non-interactive sign-in events (including refresh-token or PRT-based authentication) where the originating device is not managed by Intune/Jamf, not marked as compliant, and lacks a registered trust type. This behavior is indicative of potential stolen session token replay or unauthorized access using intercepted artifacts.
Detects non-interactive sign-in events (including refresh-token or PRT-based authentication) where the originating device is not managed by Intune/Jamf, not marked as compliant, and lacks a registered trust type. This behavior is indicative of potential stolen session token replay or unauthorized access using intercepted artifacts.
Detects anomalous authentication behavior where a single session identifier is used to access three or more distinct SaaS applications within a one-hour window. This behavior often indicates the unauthorized use of a stolen session cookie or OAuth token (Pass-the-Cookie) across multiple SSO-integrated platforms.
Detects a single user identity authenticating into three or more distinct SaaS applications within a 10-minute window. This behavior is consistent with the replay of a stolen session cookie (Pass-the-Cookie) or the abuse of an active authenticated session to move laterally across a federated SaaS estate.
Detects anomalous, high-velocity authentication activity where a single identity accesses multiple high-value SaaS applications (such as CRM, DevOps, or admin consoles) in a short time frame, correlated with risk signals indicative of token reuse or 'pass-the-cookie' attacks.
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 Azure AD sign-in events where the user-agent matches common scripting or automation libraries such as python-requests. This behavior is often indicative of automated token replay attacks, where attackers attempt to leverage stolen session cookies or tokens (e.g., following AiTM phishing) via proxy infrastructure.
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 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 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.
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 the reuse of session identifiers (SessionId, DeviceId, or RefreshTokenJti) across different device fingerprints (e.g., changes in User-Agent, OS, or Browser) within a short timeframe. This behavior is indicative of session hijacking or adversary-in-the-middle (AiTM) attacks, where stolen session tokens or cookies are replayed by an attacker from a different endpoint than the original user.
Detects potential theft and reuse of session or refresh tokens for Microsoft 365 services (Exchange/SharePoint) by identifying multiple authentication events for the same user account from different geographic locations and IP addresses within a short timeframe, specifically when no new MFA challenge was performed.
Detects potential persistence establishment in M365 environments following anomalous or risky sign-in events. This pattern is characteristic of Adversary-in-the-Middle (AiTM) attacks where a session cookie is stolen, followed by immediate adversary actions to secure long-term access, such as MFA method registration, OAuth app consent, or mailbox rule configuration.
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