| ID | Technique | Tactic |
|---|---|---|
| T1078.004 | Cloud Accounts | Initial Access |
| T1556.006 | Multi-Factor Authentication | Persistence |
| T1586.003 | Cloud Accounts | Privilege Escalation |
Detection: Azure AD Temporary Access Pass Created
Description
The following analytic detects the creation of a Temporary Access Pass (TAP) for an Azure AD user. It leverages Azure Active Directory AuditLogs to identify the "Create Temporary Access Pass method for user" operation. TAPs are time-limited passcodes that bypass all authentication requirements including MFA and FIDO2 - an attacker who creates a TAP for a compromised account can sign in from any location without any additional factor. If confirmed malicious, this activity provides an attacker with an unchallenged authentication path into the affected account.
Search
1`azure_monitor_aad`
2category=AuditLogs
3operationName="Create Temporary Access Pass method for user"
4properties.result="success"
5
6
7| rename properties.* as *
8
9| rename initiatedBy.user.userPrincipalName as initiatedBy
10
11| rename initiatedBy.app.displayName as initiatedByApp
12
13
14| eval actor = coalesce(initiatedBy, initiatedByApp)
15
16
17| rename targetResources{}.userPrincipalName as targetUser
18
19| rename userAgent as user_agent
20
21
22| fillnull
23
24
25| stats count min(_time) as firstTime
26 max(_time) as lastTime
27 BY dest user src vendor_account vendor_product user_agent
28 actor targetUser signature
29
30
31| rename actor as user
32
33
34| `security_content_ctime(firstTime)`
35
36| `security_content_ctime(lastTime)`
37
38| `azure_ad_temporary_access_pass_created_filter`
Data Source
| Name | Platform | Sourcetype | Source |
|---|---|---|---|
| Azure Active Directory | 'azure:monitor:aad' |
'Azure AD' |
Macros Used
| Name | Value |
|---|---|
| azure_monitor_aad | sourcetype=azure:monitor:aad |
| azure_ad_temporary_access_pass_created_filter | search * |
azure_ad_temporary_access_pass_created_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
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) | Yes |
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 AuditLogs log category.
Known False Positives
IT administrators legitimately create TAPs for users who are locked out or onboarding a new passwordless authentication method. The key differentiators are the actor and timing -- a helpdesk account creating a TAP for a user who just reported a lockout is expected; an unfamiliar actor creating a TAP outside business hours or for a high-privilege account warrants investigation.
Associated Analytic Story
Finding
| Title | Entity Field | Entity Type | Risk Score |
|---|---|---|---|
| User [$user$] has created Temporary Access Pass for [$targetUser$] from [$src$] | user | user | 50 |
Intermediate Findings
| Message | Entity Field | Entity Type | Risk Score |
|---|---|---|---|
| User [$user$] has created Temporary Access Pass for [$targetUser$] from [$src$] | targetUser | 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: 1