Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects instances where a user grants an OAuth application permissions for high-privilege scopes such as 'mail.read', 'files.readwrite', or 'offline_access'. This behavior is frequently indicative of OAuth consent phishing campaigns targeting Microsoft Entra ID environments to gain unauthorized access to user data and maintain persistence via stolen access tokens.
This rule detects when a single user principal name (UPN) attempts five or more sign-ins from the same IP address within a one-hour window, and each sign-in is flagged with a 'RiskDetail' other than 'none' but still results in a 'Success' status. This could indicate an attacker attempting to bypass security controls or an account compromise where the attacker is repeatedly trying to access the account from a consistent location, possibly with different credentials or methods, until successful.
This rule detects suspicious sign-in attempts to Microsoft services (login.microsoftonline.com, graph.microsoft.com, outlook.office365.com) that occur within 30 minutes of a user clicking on a URL associated with these services. The sign-in is considered suspicious if it originates from a user agent commonly used by automated tools (e.g., axios, curl, python-requests, httpclient, Go-http-client) and is flagged with a 'atRisk' or 'high' risk level by Azure AD. This combination suggests a potential credential compromise following a phishing attempt, where an attacker might be using automated tools to leverage stolen credentials.
Detects potential Microsoft 365 session token replay attacks, often associated with Adversary-in-the-Middle (AiTM) kits. The rule identifies suspicious sign-in activity originating from proxy or anonymized IP addresses where MFA was bypassed or incomplete, coupled with a geographic location that deviates from the user's established 30-day baseline or constitutes a new, unseen location.
Detects automated post-compromise activity associated with Bluekit Browser-in-the-Middle (BitM) attacks. The rule identifies a pattern of a successful user login followed rapidly (within 10 minutes) by a password change and new passkey enrollment, indicating an adversary is securing their persistence after initial session hijacking.
Detects potential Adversary-in-the-Middle (AiTM) phishing activity by identifying successful user sign-ins from a new, historically unseen IP address occurring within 30 minutes of a legitimate sign-in from a known IP, indicating the potential replay of a stolen session or refresh token.
This rule detects anomalous or risky sign-in events for user accounts that have recently configured an active out-of-office (OOF) auto-reply. An attacker may leverage a user's known absence to gain access to their email or other corporate resources using compromised credentials, as the user is less likely to notice suspicious account activity while away.
This rule detects anomalous or risky sign-in events for user accounts that have recently configured an active out-of-office (OOF) auto-reply. An attacker may leverage a user's known absence to gain access to their email or other corporate resources using compromised credentials, as the user is less likely to notice suspicious account activity while away.
This rule detects anomalous or risky sign-in events for user accounts that have recently configured an active out-of-office (OOF) auto-reply. An attacker may leverage a user's known absence to gain access to their email or other corporate resources using compromised credentials, as the user is less likely to notice suspicious account activity while away.
This rule detects instances where a user clicks on a URL identified as potentially malicious (phishing-related) and subsequently has a risky sign-in event in Azure AD. The rule correlates URL click events from email with Azure AD sign-in logs, specifically looking for sign-ins marked as risky or originating from suspicious user agents (e.g., specific axios versions). The phishing URLs are identified by keywords such as 'azureapplicationregistration.pages.dev', 'chrnobinson.com', 'workers.dev', '.pages.dev', '.web.core.windows.net', '/oauth2/v2.0/authorize', 'client_id=', and 'scope=openid'.
Detects attempts to exploit the Zerologon vulnerability (CVE-2020-1472) for privilege escalation and domain compromise. This rule monitors for a high volume of Netlogon secure channel failures (Event ID 5805), machine account password resets to a zeroed/blank state (Event ID 4742 for computer accounts with specific password indicators), anomalous Netlogon service restarts (Event ID 7036), and Azure AD Identity Protection risk events referencing Zerologon or Netlogon anomalies. These indicators collectively suggest an active exploitation attempt of the Zerologon vulnerability.
Detects a non-interactive sign-in using an existing session shortly after the user completes interactive MFA, which can indicate stolen token replay. This rule identifies scenarios where a user successfully completes an interactive multi-factor authentication (MFA) sign-in, and then, within a short time frame (1800 seconds or 30 minutes), a non-interactive sign-in occurs for the same user and session ID. This pattern is indicative of a stolen session token being replayed by an attacker, bypassing subsequent MFA prompts.
Detects successful or failed logon attempts originating from IP addresses identified as associated with botnets, C2 infrastructure, or proxies by threat intelligence feeds. This indicates potential malicious activity or compromised accounts.
Detects potential Adversary-in-the-Middle (AiTM) phishing activity by identifying successful user sign-ins from a new, historically unseen IP address occurring within 30 minutes of a legitimate sign-in from a known IP, indicating the potential replay of a stolen session or refresh token.
Detects session hijacking attempts where a session cookie is used from a different IP address or user-agent immediately following a successful MFA authentication event. This behavior is indicative of an adversary replaying a stolen session token.
Detects a non-interactive sign-in using an existing session shortly after the user completes interactive MFA, which can indicate stolen token replay. This rule identifies scenarios where a user successfully completes an interactive multi-factor authentication (MFA) sign-in, and then, within a short time frame (1800 seconds or 30 minutes), a non-interactive sign-in occurs for the same user and session ID. This pattern is indicative of a stolen session token being replayed by an attacker, bypassing subsequent MFA prompts.
Detects the registration of a new authentication method (phone, authenticator app, passkey, or security key) in Azure Active Directory after a session exhibits unusual location or token behavior, indicated by a high or medium risk level, anonymous proxy usage, or unfamiliar features.
Detects device registration or join activity by an account shortly after a risky sign-in or policy-lure interaction, indicating potential compromise of the account.
Detects suspicious device access patterns and multiple failed authentication attempts from unmanaged devices and outside the corporate network, combined with Azure AD Identity Protection risk states. This rule identifies potential credential access attempts or account compromise.
Detects suspicious device access patterns and multiple failed authentication attempts from unmanaged devices and outside the corporate network, combined with Azure AD Identity Protection risk states. This rule identifies potential credential access attempts or account compromise.
This rule detects suspicious sign-in attempts to Azure AD where Conditional Access policies were not applied, the sign-in was successful, and the user agent string indicates a bot, crawler, headless browser, or common scripting tools like curl, wget, or python. It specifically looks for multiple such logins or logins from multiple unique devices within a 5-minute window for the same user and risk detail.
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