Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects anomalous authentication attempts where a single source IP validates a high volume of distinct Service Principal or App Client IDs within a short timeframe with a high failure rate. This pattern is indicative of attackers performing bulk validation of stolen API keys or OAuth credentials, likely harvested through mobile application decompilation (APK/IPA analysis).
This rule detects potential Server-Side Request Forgery (SSRF) activity where web application or runtime processes attempt to access the Cloud Instance Metadata Service (IMDS) or container task metadata endpoints. By monitoring network connections and application logs, the rule filters out known legitimate metadata clients and identifies suspicious processes frequently associated with web-based vulnerabilities that are repeatedly querying sensitive metadata paths.
This rule detects potential Server-Side Request Forgery (SSRF) activity where web application or runtime processes attempt to access the Cloud Instance Metadata Service (IMDS) or container task metadata endpoints. By monitoring network connections and application logs, the rule filters out known legitimate metadata clients and identifies suspicious processes frequently associated with web-based vulnerabilities that are repeatedly querying sensitive metadata paths.
Detects anomalous, large-scale cloning or mirroring of code repositories combined with targeted access to infrastructure-as-code configuration files (e.g., .tfvars, .tfstate) or automated exfiltration of CI/CD build artifacts. This behavior is indicative of an adversary attempting to exfiltrate proprietary source code or sensitive secrets stored within repositories.
Detects anomalous, large-scale cloning or mirroring of code repositories combined with targeted access to infrastructure-as-code configuration files (e.g., .tfvars, .tfstate) or automated exfiltration of CI/CD build artifacts. This behavior is indicative of an adversary attempting to exfiltrate proprietary source code or sensitive secrets stored within repositories.
Detects potential device-code phishing abuse where a successful device code authentication is followed by scripted API activity using common automation user agents within a short time window (10 minutes). This behavior is indicative of an attacker who has successfully phished a user's device code and is immediately leveraging the stolen token to interact with Microsoft Graph, Azure, or Intune services.
Detects potential device-code phishing abuse where a successful device code authentication is followed by scripted API activity using common automation user agents within a short time window (10 minutes). This behavior is indicative of an attacker who has successfully phished a user's device code and is immediately leveraging the stolen token to interact with Microsoft Graph, Azure, or Intune services.
Detects potential device-code phishing abuse where a successful device code authentication is followed by scripted API activity using common automation user agents within a short time window (10 minutes). This behavior is indicative of an attacker who has successfully phished a user's device code and is immediately leveraging the stolen token to interact with Microsoft Graph, Azure, or Intune services.
Detects high-risk account modifications, such as adding MFA methods or OAuth consent grants, occurring shortly after a user sign-in. This pattern is indicative of post-AiTM session theft (e.g., NovaCookies) where an adversary uses a stolen session token to modify account settings for persistence or MFA bypass.
Detects Microsoft 365 sign-in events where a session token is reused without a fresh MFA challenge, originating from known residential proxy service providers. This behavior is indicative of threat actors using stolen session tokens (Session Replay) to bypass MFA protections.
This rule detects potential OAuth token theft resulting from device code phishing (e.g., N0va phishkit). It monitors for a successful OAuth device-code authentication followed within 5 minutes by a secondary successful sign-in for the same user from a different IP address, different browser client, or different user agent, which is a common indicator of an adversary-in-the-middle (AiTM) session hijacking attempt.
Detects anomalous non-interactive sign-ins that bypass multi-factor authentication (MFA) requirements from a geographical location not previously seen in the user's historical sign-in baseline. This pattern is indicative of attackers using stolen OAuth access or refresh tokens to maintain persistence and bypass standard authentication controls.
Detects anomalous non-interactive sign-ins that bypass multi-factor authentication (MFA) requirements from a geographical location not previously seen in the user's historical sign-in baseline. This pattern is indicative of attackers using stolen OAuth access or refresh tokens to maintain persistence and bypass standard authentication controls.
Detects potential N0va-style phishing or token-exchange abuse where a user completes a device-code authentication flow, followed immediately by an access token usage from a different IP address or device. This sequence indicates an adversary has captured the resulting token/session and is attempting to bypass multi-factor authentication (MFA) to gain unauthorized access to cloud resources.
Detects instances where a user account experiences multiple failed sign-in attempts followed by a successful sign-in from a different, geographically distant location within a short timeframe, indicating impossible travel and potential credential compromise.
Detects instances where a user account experiences multiple failed sign-in attempts followed by a successful sign-in from a different, geographically distant location within a short timeframe, indicating impossible travel and potential credential compromise.
Detects anomalous, high-frequency access to sensitive Microsoft Graph API endpoints by a single service principal or user identity. The rule flags identities that interact with five or more distinct categories of sensitive resources (e.g., users, groups, applications, sites) within a 30-minute window, suggesting automated reconnaissance or environment discovery.
Detects anomalous, non-interactive sign-in activity in Microsoft 365 that suggests session cookie replay (Pass-the-Cookie). The rule correlates successful, MFA-validated interactive sign-ins against subsequent, non-interactive sign-ins that lack MFA, occur from a different device and IP address within a short time window, and bypass standard interactive re-authentication.
Detects modifications to a user's multi-factor authentication (MFA) settings, including changes to registered phone numbers, authenticator app configurations, or the registration of new authentication methods. This activity can indicate an attempt to weaken security defenses or gain persistent, unauthorized access to an account via MFA bypass.
This rule detects when a user account updates their MFA information in Azure AD or Microsoft 365, resulting in an increase in the number of associated MFA devices. This is a common indicator of an attacker registering a new MFA device (such as a hardware token or phone app) to a compromised account to establish persistence or bypass existing MFA requirements.
Detects multiple failed multifactor authentication (MFA) attempts from the same user on an unmanaged device. This behavior is indicative of an adversary attempting to bypass MFA or perform MFA fatigue attacks using stolen credentials.
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