Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous, automated non-interactive sign-in activity in Microsoft 365 where a session token is replayed from a new source IP address approximately every 8 hours. This pattern is characteristic of automated token-refresh tooling, such as that associated with Storm-2755, utilizing specific user-agents (Firefox 131.0/151.0 or python-requests) that deviate from the user's typical browser fingerprint.
Detects unauthorized or non-standard processes attempting to access sensitive browser cookie storage files, which is a common technique used by credential-stealing malware to exfiltrate session data and bypass multi-factor authentication.
Detects the reuse of a session or refresh token from a geographic location that contradicts recent user activity (impossible travel) without a new interactive MFA challenge. This activity is indicative of Adversary-in-the-Middle (AiTM) phishing attacks, such as those leveraging Evilginx, where session cookies are captured and replayed from attacker infrastructure to bypass multi-factor authentication.
Detects potential post-authentication persistent actions (e.g., OAuth app grants, MFA changes, mailbox rule updates) performed shortly after a successful Google Workspace login, specifically flagging actions originating from IP addresses different from the login session to identify session hijacking or token replay (AiTM) activity. Includes suppression for known corporate IP ranges, trusted OAuth application consents, and recurring user behavior.
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.
This rule detects potential Adversary-in-the-Middle (AiTM) activity by identifying a pattern where a successful multi-factor authentication sign-in event is quickly followed by another successful sign-in for the same user from a different IP address, device, or geographic location. This behavior is indicative of session cookie theft and replay, commonly associated with phishing toolkits like Evilginx or NovaCookies.
Detects potential Adversary-in-the-Middle (AiTM) session token replay attacks where a user successfully authenticates with MFA and is followed shortly by a subsequent successful login for the same user from a different IP address or device without a fresh MFA challenge, suggesting the use of a stolen session cookie.
Detects potential session hijacking where a user logs into Google Workspace from a new IP or country, followed within an hour by the registration of a new security key or passkey, which is a common technique to establish persistent unauthorized access.
Detects instances where a Claude session token is reused from a new IP address without the completion of an MFA challenge, indicating a potential session hijacking or session theft attempt.
Detects Azure AD/Entra ID authentication events where a session token or cookie is used without performing a fresh interactive MFA challenge, which is characteristic of session hijacking or adversary-in-the-middle (AiTM) relay attacks (e.g., NovaCookies) used to bypass security controls after initial authentication.
Detects session hijacking (AiTM) attempts where a failed or interrupted sign-in (ResultType 50199) is followed by a successful login using the same SessionId but with an Axios user-agent, indicating automated replay of a stolen session token rather than legitimate browser usage.
Detects an adversary-in-the-middle (AiTM) phishing pattern where the legitimate Microsoft authentication URL is embedded as a sub-path within an attacker-controlled proxy domain. This technique is characteristic of AiTM kits, such as Evilginx2, which proxy the legitimate Microsoft authentication flow to capture credentials and session cookies.
This rule hunts for indicators associated with the TrustSink technique, which involves using rogue External Authentication Methods (EAM) or malicious OIDC providers to bypass authentication or maintain persistence within a cloud environment. It monitors Azure AD sign-in and audit logs, alongside DNS and network events, for references to known malicious domains and URLs used for EAM-based attacks.
This rule hunts for indicators associated with the TrustSink technique, which involves using rogue External Authentication Methods (EAM) or malicious OIDC providers to bypass authentication or maintain persistence within a cloud environment. It monitors Azure AD sign-in and audit logs, alongside DNS and network events, for references to known malicious domains and URLs used for EAM-based attacks.
Detects non-interactive sign-in activity using a Primary Refresh Token (PRT) occurring shortly after a risky or AiTM-flagged interactive sign-in for the same user, originating from a device that has not been previously associated with that user account.
Detects the reuse of a single authenticated session or refresh token from multiple distinct IP addresses or ASN origins within a short timeframe following a successful MFA event. This pattern is indicative of session hijacking, where an adversary uses an Adversary-in-the-Middle (AiTM) toolkit (e.g., Evilginx) to capture a valid session cookie from a victim and replay it from their own infrastructure to bypass authentication.
Detects a suspicious authentication pattern involving 3 or more MFA failures within 15 minutes, immediately followed by an MFA success for the same user. This pattern is characteristic of potential Adversary-in-the-Middle (AiTM) activity, where an attacker intercepts a session or MFA token and relays it through separate infrastructure, while the legitimate user's own MFA attempts fail or time out.
Detects instances where a user successfully authenticates to a cloud service (Azure AD) from an unrecognized ASN, device, or country without performing MFA, followed shortly by high-risk actions such as mailbox rule creation, mass file downloads, or OAuth application registration. This pattern is indicative of session cookie theft or OAuth token replay used to bypass MFA.
Detects the use of anomalous session tokens in Azure Active Directory (Azure AD) sign-in events where the token is being used in a non-interactive manner without a corresponding interactive authentication event. This behavior is indicative of token theft or infostealer activity, where an adversary replays a stolen session cookie from a new, untrusted, or non-compliant device to bypass authentication protections.
Detects Azure AD sign-in events indicative of a session token replay attack. The rule identifies successful sign-ins where the traffic source originates from known residential proxy infrastructure (anonymizing services often used by attackers) and the authentication process lacks a fresh interactive MFA challenge, suggesting the use of a stolen, valid session token.
Detects potential post-authentication persistent actions (e.g., OAuth app grants, MFA changes, mailbox rule updates) performed shortly after a successful Google Workspace login, specifically flagging actions originating from IP addresses different from the login session to identify session hijacking or token replay (AiTM) activity. Includes suppression for known corporate IP ranges, trusted OAuth application consents, and recurring user behavior.
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