Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
771 detections
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Detects successful authentication events using the device code flow for specific end-user applications including Windows App, Azure Virtual Desktop, and Windows 365. This pattern is often utilized in legitimate workflows but can also be abused to gain access to corporate resources.
Detects a sequence of events starting with a user signing in via the OAuth device code flow, followed immediately by mailbox item enumeration and sending of email. This pattern is characteristic of attackers who use device code phishing to steal tokens, subsequently using those tokens to access mailboxes and send malicious emails from the compromised account.
Detects Microsoft Entra OAuth 2.0 device authorization grant flow completion immediately followed by non-interactive API access from automated user agents like python-requests. This behavior is indicative of device code phishing kits, where an attacker tricks a user into entering a code and then programmatically hijacks the resulting session token.
Detects the issuance of a Primary Refresh Token (PRT) to Microsoft Graph occurring in close temporal proximity to a device code authentication flow. This pattern is indicative of device-code phishing attacks, where an adversary intercepts a device code to obtain long-lived, session-persistent access to a tenant.
Detects potentially malicious OAuth device code sign-ins by identifying sign-in events that exhibit anomalous characteristics, such as atypical geographic locations, rare client-application combinations, unmanaged/noncompliant device status, or elevated risk signals associated with the account. This detection helps identify account compromise or malicious OAuth consent activity often associated with 'EvilTokens' or device-code phishing attacks.
Detects potential device code phishing patterns where a successful OAuth device-code authentication is rapidly followed by Microsoft Graph resource access from the same account. The rule identifies suspicious polling sessions (e.g., EvilTokens) by monitoring for successful device-code sign-ins and immediate, subsequent Graph API activity. It filters known first-party applications and incorporates risk-based triggers and reconnaissance behavioral patterns to minimize false positives.
Detects potential device code phishing patterns where a successful OAuth device-code authentication is rapidly followed by Microsoft Graph resource access from the same account. The rule identifies suspicious polling sessions (e.g., EvilTokens) by monitoring for successful device-code sign-ins and immediate, subsequent Graph API activity. It filters known first-party applications and incorporates risk-based triggers and reconnaissance behavioral patterns to minimize false positives.
Detects potential device code phishing patterns where a successful OAuth device-code authentication is rapidly followed by Microsoft Graph resource access from the same account. The rule identifies suspicious polling sessions (e.g., EvilTokens) by monitoring for successful device-code sign-ins and immediate, subsequent Graph API activity. It filters known first-party applications and incorporates risk-based triggers and reconnaissance behavioral patterns to minimize false positives.
Detects potential abuse of stolen OAuth session tokens, where refresh tokens are used to authenticate after a user has performed a password reset. This rule identifies accounts that continue to sign in from previously unseen devices or IP addresses within 24 hours post-password reset, a technique consistent with session persistence mechanisms used by adversary tools and platforms like EvilTokens.
Detects potential abuse of stolen OAuth session tokens, where refresh tokens are used to authenticate after a user has performed a password reset. This rule identifies accounts that continue to sign in from previously unseen devices or IP addresses within 24 hours post-password reset, a technique consistent with session persistence mechanisms used by adversary tools and platforms like EvilTokens.
Detects anomalous outbound email activity from an account that recently performed an OAuth device code sign-in characterized by elevated risk levels. The rule identifies potential token abuse by looking for a high volume of emails with either a significant percentage of external recipients or patterns indicative of bulk mailing, common in phishing campaigns that leverage compromised identities (e.g., EvilTokens).
Detects automated post-compromise mailbox reconnaissance following an anomalous OAuth device code sign-in that exhibits high-risk indicators. The rule identifies programmatic access to a high volume of mail items within a short window following suspicious token issuance, which is characteristic of automated scanning or data exfiltration tooling.
Detects automated post-compromise mailbox reconnaissance following an anomalous OAuth device code sign-in that exhibits high-risk indicators. The rule identifies programmatic access to a high volume of mail items within a short window following suspicious token issuance, which is characteristic of automated scanning or data exfiltration tooling.
Detects automated post-compromise mailbox reconnaissance following an anomalous OAuth device code sign-in that exhibits high-risk indicators. The rule identifies programmatic access to a high volume of mail items within a short window following suspicious token issuance, which is characteristic of automated scanning or data exfiltration tooling.
Detects automated post-compromise mailbox reconnaissance following an anomalous OAuth device code sign-in that exhibits high-risk indicators. The rule identifies programmatic access to a high volume of mail items within a short window following suspicious token issuance, which is characteristic of automated scanning or data exfiltration tooling.
Detects high-frequency, multi-action data access events through the Microsoft Graph API, indicative of automated exfiltration of Exchange, SharePoint/OneDrive, and Teams data. This behavior is consistent with an adversary using stolen OAuth access tokens to perform bulk enumeration and extraction of sensitive cloud-hosted data.
This rule monitors for OAuth device-code authentication flow completions initiated from devices flagged as non-compliant or unmanaged in Azure AD. This behavior is indicative of potential token theft via vishing or SMS-based phishing lures (consistent with Storm-3121/Storm-3032 TTPs), where an adversary tricks a user into completing a device authorization grant to facilitate MFA bypass and persistence.
This rule detects potential identity compromise via abuse of the OAuth device-code grant flow. It monitors for successful user logins leveraging the device code flow, which is a common indicator of AiTM (Adversary-in-the-Middle) phishing campaigns that lure users into authorizing malicious applications under pretexts like security updates or SSO re-authentication.
This rule detects potential identity compromise via abuse of the OAuth device-code grant flow. It monitors for successful user logins leveraging the device code flow, which is a common indicator of AiTM (Adversary-in-the-Middle) phishing campaigns that lure users into authorizing malicious applications under pretexts like security updates or SSO re-authentication.
This rule detects HTTP requests to the Microsoft device login endpoint (/devicelogin) that originate from a non-Microsoft referrer domain. This behavior is highly indicative of Adversary-in-the-Middle (AiTM) phishing attacks targeting OAuth device code flows, where a malicious proxy site redirects users to the legitimate Microsoft login page while attempting to capture session tokens.
Detects successful device code authentication flows involving applications that are not part of a known list of standard Microsoft and Azure client applications. Device code flows are frequently abused in phishing attacks to bypass traditional credential-based authentication.
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