Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects login events to the 'My Sign-Ins' portal (mysignins.microsoft.com) by users. This portal is a high-value target for adversaries who have gained access to a session via token theft or session hijacking, as it allows them to modify or add authentication methods (such as MFA devices) to establish long-term persistence in the victim's account.
This rule detects a suspicious Azure sign-in sequence, identified by error code 50074 (User failed MFA) followed by 50140 (This error occurred due to 'Keep me signed in' interrupt when the user was authenticating). This pattern is indicative of an Adversary-in-the-Middle (AiTM) phishing attack where the attacker is attempting to bypass MFA or manipulate session persistence after capturing initial credentials.
This rule monitors for ADFS-related security events that may indicate credential theft or authentication bypass attempts. It flags anomalous federated sign-in events (e.g., MFA bypass, unfamiliar location), unauthorized access to the ADFS DKM container in Active Directory, and suspicious attempts to export ADFS token-signing certificates.
Detects a single user account authenticating, performing Graph API reconnaissance, and conducting data exfiltration across three distinct source IP addresses within a one-hour window. This behavior is indicative of threat actors like Storm-3121 or Storm-3032 rotating infrastructure per attack stage to evade network-based detection.
Detects a single user account authenticating, performing Graph API reconnaissance, and conducting data exfiltration across three distinct source IP addresses within a one-hour window. This behavior is indicative of threat actors like Storm-3121 or Storm-3032 rotating infrastructure per attack stage to evade network-based detection.
Detects pull requests created in GitHub repositories by entities labeled as 'Bot', 'App', or 'integration', or containing the term 'codex', specifically targeting repositories where such entities have not performed these actions in the previous 30 days. This baseline approach identifies potentially hijacked or unauthorized automation accounts performing activities outside of their standard repository scope.
Detects pull requests created in GitHub repositories by entities labeled as 'Bot', 'App', or 'integration', or containing the term 'codex', specifically targeting repositories where such entities have not performed these actions in the previous 30 days. This baseline approach identifies potentially hijacked or unauthorized automation accounts performing activities outside of their standard repository scope.
This rule detects a user account performing its first-ever GitHub OAuth or repository access within a short timeframe (30 minutes) after authenticating to a designated low-trust OpenAI community forum. This pattern is designed to identify potential adversary activity where a compromised or newly created account is used to pivot from a low-trust environment to corporate repository access.
This rule detects a user account performing its first-ever GitHub OAuth or repository access within a short timeframe (30 minutes) after authenticating to a designated low-trust OpenAI community forum. This pattern is designed to identify potential adversary activity where a compromised or newly created account is used to pivot from a low-trust environment to corporate repository access.
Detects GitHub OAuth authorization, repository cloning, or ZIP downloads from source IP addresses that have not been observed for the specific actor within the past 30 days. This rule helps identify potential account compromise or token theft where an attacker attempts to access repository data from a new location, while specifically excluding bot/service accounts known for rotating CI infrastructure IPs.
Detects GitHub OAuth authorization, repository cloning, or ZIP downloads from source IP addresses that have not been observed for the specific actor within the past 30 days. This rule helps identify potential account compromise or token theft where an attacker attempts to access repository data from a new location, while specifically excluding bot/service accounts known for rotating CI infrastructure IPs.
Detects anomalous, high-volume mailbox access across multiple distinct Exchange Online mailboxes within a short timeframe (1 hour) initiated by a single user account. This behavior is indicative of automated email reconnaissance, content scraping, or bulk exfiltration, often associated with post-credential compromise activity where an attacker iterates through mailboxes to identify sensitive information such as financial or administrative data.
This rule detects unauthorized GitHub repository access or OAuth activity performed by a user account identified as terminated in the organization's HR system. It uses a watchlist to cross-reference GitHub audit logs against terminated employee records, applying a 2-hour grace period to account for potential log synchronization delays or final offboarding tasks.
This rule detects unauthorized GitHub repository access or OAuth activity performed by a user account identified as terminated in the organization's HR system. It uses a watchlist to cross-reference GitHub audit logs against terminated employee records, applying a 2-hour grace period to account for potential log synchronization delays or final offboarding tasks.
Detects unauthorized cloud authentication attempts in Azure AD where a session token appears to have been replayed. The rule identifies sign-ins flagged by Identity Protection as anomalous or involving token/issuer irregularities, particularly when these occur without a fresh MFA challenge, suggesting a bypass of standard authentication controls.
Detects a sequence of activity where an anonymous AWS token is requested via a specific API path, followed within 15 minutes by a token minting call from the same source IP. This behavior is indicative of a token-theft or privilege escalation chain.
This rule detects potential exploitation activities targeting Atlassian Confluence by monitoring for unauthorized access to administrative configuration actions (e.g., setup/setupadministrator.action) and the subsequent addition of accounts to the 'confluence-administrators' group. It correlates process execution, network request patterns, and local user account modifications to identify successful or attempted privilege escalation and configuration manipulation.
This rule detects scenarios where a device registration event occurs within 15 minutes of a device-code authentication sign-in for the same user. This pattern is indicative of potential account manipulation where an adversary registers their own device to an existing account to maintain persistent access or bypass multi-factor authentication (MFA).
This rule detects scenarios where a device registration event occurs within 15 minutes of a device-code authentication sign-in for the same user. This pattern is indicative of potential account manipulation where an adversary registers their own device to an existing account to maintain persistent access or bypass multi-factor authentication (MFA).
Detects high-volume or multi-mailbox activity in Exchange Online, indicative of automated mailbox collection or unauthorized bulk data access via Graph or REST APIs. The rule monitors for a threshold of operations across multiple mailboxes or high event counts originating from specific API clients.
Detects potential lateral phishing attempts within Microsoft Teams by identifying a single user account sending messages containing common credential-harvesting lures (e.g., 'passkey', 'SSO', 'MFA', 'security update') to multiple internal recipients in a short timeframe.
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