Detection: Azure AD OAuth Application Consent Granted By User

Updated Date: 2026-05-13 ID: 10ec9031-015b-4617-b453-c0c1ab729007 Author: Mauricio Velazco, Splunk Type: TTP Product: Splunk Enterprise Security

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.

Search

 1`azure_monitor_aad` operationName="Consent to application" properties.result=success 
 2| rename properties.* as * 
 3| eval permissions_index = if(mvfind('targetResources{}.modifiedProperties{}.displayName', "ConsentAction.Permissions") >= 0, mvfind('targetResources{}.modifiedProperties{}.displayName', "ConsentAction.Permissions"), -1) 
 4| eval permissions = mvindex('targetResources{}.modifiedProperties{}.newValue',permissions_index) 
 5| rex field=permissions "Scope: (?<Scope> [ ^,]+)" 
 6| fillnull 
 7| stats count min(_time) as firstTime max(_time) as lastTime by dest user src vendor_account vendor_product Scope signature 
 8| `security_content_ctime(firstTime)` 
 9| `security_content_ctime(lastTime)` 
10| `azure_ad_oauth_application_consent_granted_by_user_filter`

Data Source

Name	Platform	Sourcetype	Source
Azure Active Directory Consent to application	Azure	`'azure:monitor:aad'`	`'Azure AD'`

Macros Used

Name	Value
security_content_ctime	`convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)`
azure_ad_oauth_application_consent_granted_by_user_filter	`search *`

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

Annotations

- MITRE ATT&CK

+ Kill Chain Phases

+ NIST

+ CIS

- Threat Actors

ID	Technique	Tactic
T1528	Steal Application Access Token	Credential Access

Exploitation

DE.CM

CIS 10

APT29

Leviathan

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting	Value
Disabled	true
Cron Schedule	`0 * * * *`
Earliest Time	`-70m@m`
Latest Time	`-10m@m`
Schedule Window	`auto`
Creates Finding (Notable)	Yes
Rule Title	`%name%`
Rule Description	`%description%`
Notable Event Fields	user, dest
Creates Intermediate Finding (Risk Event)	No

TTP detections generate a Finding (Notable) and may generate Intermediate Findings (Risk Events) for associated entities.

Implementation

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

Finding

Title	Entity Field	Entity Type	Risk Score
User $user$ consented an OAuth application.	user	user	50

References

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	✅ Passing	N/A	N/A	N/A
Unit	✅ Passing	Dataset	`Azure AD`	`azure:monitor:aad`
Integration	✅ Passing	Dataset	`Azure AD`	`azure:monitor:aad`

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

Source: GitHub | Version: 11