Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Duo User Marked Fraud
YARA-L
This rule detects when a user marks an authentication request as fraudulent within Duo Security. This indicates a potential attempt to gain unauthorized access to a user's account, possibly through MFA fatigue or other social engineering tactics. The rule specifically looks for 'user_marked_fraud' in the security result summary of Duo authentication events.
This rule detects potential MFA push fatigue attacks by identifying a high volume of failed multi-factor authentication challenges for a single user within a short time window. It aggregates sign-in logs, specifically looking for result types indicative of MFA failures, and alerts if a user receives more than a defined threshold of MFA challenges within a 30-minute period.
Detects Okta MFA device enrollment or life cycle changes that occur shortly after a successful authentication or password reset for the same user. This pattern is a strong indicator of a post-vishing account takeover, as seen in ShinyHunters campaigns.
Detects when an administrator or help desk agent resets a user's password or deactivates/resets their MFA factors. This activity, while often legitimate, is a key tactic used by social engineering groups like Muddled Libra (aka Scattered Spider) who are known to 'vishing' (voice-phishing) IT help desks to take over accounts.
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 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 a user password change synced from on-premises Active Directory, immediately followed by the same user registering a new MFA method. This sequence is highly suspicious for existing privileged accounts and was used by the threat actor Storm-0501 to compromise a Global Administrator account that previously lacked MFA.
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.
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.
KQL Query from file: Hunting fasthttp Bruteforce Campaign
KQL Query from file: User Sign-in Activity
'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 look for accounts that have not been successfully logged into recently who then add or update an MFA method 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. This query uses the Microsoft Sentinel UEBA features.
Ref: [LINK TO BLOG]'
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. This query uses the Microsoft Sentinel UEBA features.
Ref: [LINK TO BLOG]'
'Identifies modifications to user's MFA settings. An attacker could use access to modify MFA settings to bypass MFA requirements or maintain persistence.
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'
The analytic detects the registration of a new Multi-Factor Authentication (MFA) method for an Azure AD account. It leverages Azure AD AuditLogs to identify when a user registers new security information. This activity is significant because adversaries who gain unauthorized access to an account may add their own MFA method to maintain persistence. If confirmed malicious, this could allow attackers to bypass existing security controls, maintain long-term access, and potentially escalate their privileges within the environment.
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