Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects anomalous, high-volume activity from a single user account interacting with known public generative AI platforms (e.g., ChatGPT, Copilot, Claude). It uses a threshold-based approach on CloudAppEvents logs to identify potential reconnaissance or bulk information gathering activities which could signify unauthorized use or automated data scraping via AI interfaces.
This rule detects anomalous, high-volume activity from a single user account interacting with known public generative AI platforms (e.g., ChatGPT, Copilot, Claude). It uses a threshold-based approach on CloudAppEvents logs to identify potential reconnaissance or bulk information gathering activities which could signify unauthorized use or automated data scraping via AI interfaces.
This rule detects anomalous, high-volume activity from a single user account interacting with known public generative AI platforms (e.g., ChatGPT, Copilot, Claude). It uses a threshold-based approach on CloudAppEvents logs to identify potential reconnaissance or bulk information gathering activities which could signify unauthorized use or automated data scraping via AI interfaces.
Detects activity associated with the 'BlueKit' session relay tool, which uses the rrweb library to capture and replay user session interactions. This rule identifies web browser network events involving rrweb-related scripts occurring within 10 minutes of a successful user sign-in, indicating a potential Adversary-in-the-Middle (AiTM) phishing attack where credentials and session cookies are being captured.
Detects instances where a user account registers a new Multi-Factor Authentication (MFA) method (such as TOTP or Authenticator app) shortly (within 30 minutes) after a high-risk or potentially anomalous sign-in event. This pattern is commonly associated with adversary-in-the-middle (AiTM) phishing attacks where an attacker captures an authenticated session and uses it to enroll a secondary, attacker-controlled MFA device for persistent account access.
Detects activity associated with the 'BlueKit' session relay tool, which uses the rrweb library to capture and replay user session interactions. This rule identifies web browser network events involving rrweb-related scripts occurring within 10 minutes of a successful user sign-in, indicating a potential Adversary-in-the-Middle (AiTM) phishing attack where credentials and session cookies are being captured.
Detects instances where a user account registers a new Multi-Factor Authentication (MFA) method (such as TOTP or Authenticator app) shortly (within 30 minutes) after a high-risk or potentially anomalous sign-in event. This pattern is commonly associated with adversary-in-the-middle (AiTM) phishing attacks where an attacker captures an authenticated session and uses it to enroll a secondary, attacker-controlled MFA device for persistent account access.
Detects the creation of applications, service principals, or delegated permission grants in Entra ID where the initiating User-Agent string contains 'python-requests'. This pattern is often indicative of programmatic interaction with Microsoft Graph API using standard Python libraries, which may be associated with automated malicious registration chains or unauthorized application persistence.
Detects the creation of an application or service principal followed quickly (within 1 hour) by a tenant-wide delegated permission grant scoped to 'AllPrincipals' with 'openid' and 'profile' permissions. This sequence is a known indicator of automated rogue Enterprise Application setup, often used in consent phishing or persistence campaigns.
Detects modifications to Microsoft Entra authentication policy or external authentication method configurations, which may indicate an adversary attempting to establish a 'TrustSink' or alternate authentication persistence mechanism by registering a malicious OIDC provider or modifying authentication policy.
Detects user sign-ins that utilize an 'External Authentication Method' within 3 days of a password reset event. This behavior may indicate an adversary has successfully established persistence using a rogue authentication provider (e.g., via a TrustSink or similar malicious configuration) that survives password remediation and is capable of capturing or intercepting subsequent authentication tokens.
Detects Azure AD sign-in events where Multi-Factor Authentication (MFA) was reportedly satisfied by an External Authentication Method (EAM) claim without evidence of a traditional secondary authentication challenge. This behavior is indicative of a 'TrustSink' technique where a rogue or compromised OIDC provider injects a spoofed assertion of MFA success into the authentication token.
This rule identifies sign-in events using an External Authentication Method (EAM) that occur within the 2-day cache window immediately following a certificate or key credential update for an application or service principal in Entra ID. This detection highlights a timeframe where an attacker potentially possessing a compromised old signing key could use it to forge tokens before the Entra cache refreshes.
This rule identifies enterprise resource access from OnePlus or OxygenOS devices that lack proper management or compliance. It calculates an exposure score based on factors including device management status, compliance state, device trust type, and whether the device is newly observed. The rule flags high and medium priority exposure incidents, specifically targeting non-managed or non-compliant BYOD devices, which could represent an increased risk profile for corporate data access.
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.
This rule detects potential account hijacking via OAuth device-code authorization flow, specifically identifying patterns associated with phishing kits (e.g., EvilTokens, ARToken). It monitors sign-in events where the device code flow is utilized, specifically flagging successful grants that potentially bypass standard interactive credential prompts.
Detects high or medium severity security alerts related to Kerberos Golden Ticket forgery, as identified by Microsoft Defender for Identity. This rule filters for specific alert names indicative of forged TGTs minted from the krbtgt account, excluding generic or informational ticket anomalies.
Detects a sequence of events where a user performs a device-code authentication followed rapidly by a device enrollment, and subsequently continues to access cloud services (Microsoft Graph, Exchange Online, Intune) shortly after their account password has been reset. This pattern is indicative of persistent access maintained via a registered device or OAuth token that survives credential remediation.
Detects the 'GhostCode' attack pattern where an adversary acquires a Primary Refresh Token (PRT) shortly after a successful OAuth device code authentication. This correlation identifies potential session hijacking or token theft resulting from an adversary-controlled device registration.
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 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.
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