Azure AD OAuth Application Consent Granted By User

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Description

The following analytic detects when a user in an Azure AD environment grants consent to an OAuth application. It leverages Azure AD audit logs to identify events where users approve application consents. This activity is significant as it can expose organizational data to third-party applications, a common tactic used by malicious actors to gain unauthorized access. If confirmed malicious, this could lead to unauthorized access to sensitive information and resources. Immediate investigation is required to validate the application's legitimacy, review permissions, and mitigate potential risks.

• Type: TTP

• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

• Last Updated: 2024-05-24
• Author: Mauricio Velazco, Splunk
• ID: 10ec9031-015b-4617-b453-c0c1ab729007

Annotations

ATT&CK

ATT&CK

ID	Technique	Tactic
T1528	Steal Application Access Token	Credential Access

Kill Chain Phase

• Exploitation

NIST

• DE.CM

CIS20

• CIS 10

CVE

Search

`azure_monitor_aad` operationName="Consent to application" properties.result=success 
| rename properties.* as *  
| eval permissions_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName', "ConsentAction.Permissions") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName', "ConsentAction.Permissions"), -1) 
| eval permissions = mvindex('targetResources{}.modifiedProperties{}.newValue',permissions_index) 
| rex field=permissions "Scope: (?<Scope>[^,]+)" 
| stats count min(_time) as firstTime max(_time) as lastTime by operationName, user, Scope 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `azure_ad_oauth_application_consent_granted_by_user_filter`

Macros

The SPL above uses the following Macros:

• azure_monitor_aad
• security_content_ctime

azure_ad_oauth_application_consent_granted_by_user_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

• _time
• operationName
• properties.targetResources{}.modifiedProperties{}.displayName
• properties.targetResources{}.modifiedProperties{}.newValue
• user

How To Implement

You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the AuditLog log category.

Known False Positives

False positives may occur if users are granting consents as part of legitimate application integrations or setups. It is crucial to review the application and the permissions it requests to ensure they align with organizational policies and security best practices.

Associated Analytic Story

• Azure Active Directory Account Takeover

RBA

Risk Score	Impact	Confidence	Message
36.0	60	60	User $user$ consented an OAuth application.

The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

• https://attack.mitre.org/techniques/T1528/
• https://www.microsoft.com/en-us/security/blog/2022/09/22/malicious-oauth-applications-used-to-compromise-email-servers-and-spread-spam/
• https://learn.microsoft.com/en-us/azure/active-directory/manage-apps/protect-against-consent-phishing
• https://learn.microsoft.com/en-us/defender-cloud-apps/investigate-risky-oauth
• https://www.alteredsecurity.com/post/introduction-to-365-stealer
• https://github.com/AlteredSecurity/365-Stealer

Test Dataset

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

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