Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Multiple threat actors are compromising Microsoft 365 accounts in phishing attacks that leverage the OAuth device code authorization mechanism.
Attackers trick victims into entering a device code on Microsoft’s legitimate device login page, unknowingly authorizing an attacker-controlled application and granting them access to the target account without stealing credentials or bypassing multi-factor authentication (MFA).
Attackers trick victims into entering a device code on Microsoft’s legitimate device login page, unknowingly authorizing an attacker-controlled application and granting them access to the target account without stealing credentials or bypassing multi-factor authentication (MFA).
This rule detects instances where a user denies a multi-factor authentication (MFA) prompt. This could indicate that an attacker has successfully obtained the user's password and is attempting to gain unauthorized access, prompting the legitimate user for MFA approval. The user's denial signals that they did not initiate the login attempt.
Identifies user login with multifactor authentication failures, which might be an indication an attacker has the password for the account but can't pass the MFA challenge.
Detects M365 Copilot users with failed authentication attempts, MFA failures, or multi-location access patterns indicating potential credential attacks or account compromise. The detection aggregates M365 Copilot Graph API authentication events per user, calculating metrics like distinct cities/countries accessed, unique IP addresses and browsers, failed login attempts (status containing "fail" or "error"), and MFA failures (error code 50074). Users are flagged when they access Copilot from multiple cities (cities_count > 1), experience any authentication failures (failed_attempts > 0), or encounter MFA errors (mfa_failures > 0), which are indicators of credential stuffing, brute force attacks, or compromised accounts attempting to bypass multi-factor authentication.
This rule detects potential device code phishing attempts by correlating URL click events with sign-in logs using the 'deviceCode' authentication protocol. It specifically looks for successful or failed sign-ins (result types 0, 50074, 50076, 500121) that occur after a user clicks on a URL.
Detects potential Multi-Factor Authentication (MFA) fatigue attacks by identifying a high number of MFA denial events for a single user within a short timeframe. This rule specifically looks for sign-in logs where the user explicitly declined the authentication prompt, indicating a possible attempt by an attacker to bombard the user with MFA requests until they approve one.
This rule identifies risky sign-in attempts in Azure Active Directory where the sign-in originates from an IP address that has not been seen before or has been seen fewer than 3 times in the last 29 days for that specific user. It filters out guest users, non-compliant devices, and unmanaged devices, focusing on sign-ins with an aggregated risk level greater than 1 and a sign-in risk level of 50 or higher. Excluded applications are specified to reduce false positives.
This KQL query analyzes Microsoft Entra ID (formerly Azure AD) sign-in and audit logs to identify potentially risky multi-factor authentication (MFA) registration events. It works by checking for MFA registrations initiated from a new, previously unseen IP address.
The query joins historical sign-in data with recent MFA-related events. It calculates how many unique days an IP address has been used by a specific user over the last 30 days. It then flags any MFA registration event from the last 24 hours where the IP address is a new IP (seen zero days before). The final results are filtered to exclude events from new user accounts (created in the last 60 days) to focus on more established accounts.
The query joins historical sign-in data with recent MFA-related events. It calculates how many unique days an IP address has been used by a specific user over the last 30 days. It then flags any MFA registration event from the last 24 hours where the IP address is a new IP (seen zero days before). The final results are filtered to exclude events from new user accounts (created in the last 60 days) to focus on more established accounts.
Detects multiple denied or failed multi-factor authentication (MFA) prompts for a single user within a short time window. This pattern is commonly associated with MFA fatigue or push bombing attacks, where an attacker attempts to trick the user into approving an MFA request by spamming prompts repeatedly.
This detection uses Azure AD sign-in logs and does not require asset inventory or conditional access policies to be effective.
This detection uses Azure AD sign-in logs and does not require asset inventory or conditional access policies to be effective.
Detects multiple failed Multi-Factor Authentication (MFA) push attempts for a user from a single IP address, followed by a successful attempt within a short time window. This pattern can indicate an MFA push bombing or fatigue attack where an adversary spams a user with MFA requests until they approve one.
This rule detects suspicious Okta login attempts where a user successfully passes the initial authentication factor but then fails or abandons a subsequent Multi-Factor Authentication (MFA) challenge, originating from a new IP address, device (user agent), and city combination. It filters out events from known corporate VPN IP addresses to reduce false positives.
MFA Fatigue Attack
Sigma
Detects a high number of failed or denied Multi-Factor Authentication (MFA) attempts followed by a success for the same user account within a short time frame. This pattern, known as MFA fatigue or push bombing, is a technique actively used by threat actors like Scattered Spider to overwhelm a user with push notifications until they approve one, granting the attacker access.
The analytic detects an unusually high number of denied Multi-Factor Authentication (MFA) requests for a single user within a 10-minute window, specifically when more than nine MFA prompts are declined. It leverages Azure Active Directory (Azure AD) sign-in logs, focusing on "Sign-in activity" events with error code 500121 and additional details indicating "MFA denied; user declined the authentication." This behavior is significant as it may indicate a targeted attack or account compromise attempt, with the user actively declining unauthorized access. If confirmed malicious, it could lead to data exfiltration, lateral movement, or further malicious activities.
This rule detects network connections or DNS queries to domains associated with the Tycoon 2FA phishing kit. The rule leverages an external data source containing a list of known malicious domains used by this phishing kit. Detection occurs if a device attempts to connect to or resolve any of these domains.
KQL Query from file: Hunting fasthttp Bruteforce Campaign
KQL Query from file: User Sign-in Activity
Looks for a new MFA method added to an account that was preceded by medium or high risk sign-in session for the same user within maximum 6h timeframe
'This querys look for users accounts that have not been successfully logged into recently, who then have a MFA method added or updated before logging in.
Threat actors may look to re-activate dormant accounts and use them for access by adding MFA methods in the hope that changes to such dormant accounts may go un-noticed.'
Threat actors may look to re-activate dormant accounts and use them for access by adding MFA methods in the hope that changes to such dormant accounts may go un-noticed.'
' Identifies failed MFA attempts from Privileged accounts. Privileged accounts list can be based on IdentityInfo UEBA table or built-in watchlist.
Ref : https://docs.microsoft.com/azure/active-directory/fundamentals/security-operations-privileged-accounts#things-to-monitor'
Ref : https://docs.microsoft.com/azure/active-directory/fundamentals/security-operations-privileged-accounts#things-to-monitor'
This query helps hunting for Automated email notifications and suspicious sign-in activity
MFA Spamming
KQL
'Identifies list of user impacted by MFA Spamming within a given time window,Default Failure count is 10 with default Time Window is 5 minutes'
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