Splunk Edit User Privilege Escalation
Description
A low-privilege user who holds a role that has the edit_user capability assigned to it can escalate their privileges to that of the admin user by providing specially crafted web requests.
- Type: Hunting
-
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2023-05-23
- Author: Rod Soto, Chase Franklin
- ID: 39e1c326-67d7-4c0d-8584-8056354f6593
Annotations
ATT&CK
Kill Chain Phase
- Exploitation
NIST
- DE.AE
CIS20
- CIS 10
CVE
ID | Summary | CVSS |
---|---|---|
CVE-2023-32707 | In versions of Splunk Enterprise below 9.0.5, 8.2.11, and 8.1.14, and Splunk Cloud Platform below version 9.0.2303.100, a low-privileged user who holds a role that has the ‘edit_user’ capability assigned to it can escalate their privileges to that of the admin user by providing specially crafted web requests. | None |
Search
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`audittrail` action IN ("change_own_password","password_change","edit_password") AND info="granted" AND NOT user IN (admin, splunk-system-user)
| stats earliest(_time) as event_time values(index) as index values(sourcetype) as sourcetype values(action) as action values(info) as info by user
| `splunk_edit_user_privilege_escalation_filter`
Macros
The SPL above uses the following Macros:
splunk_edit_user_privilege_escalation_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.
- user
- action
- info
- _time
How To Implement
This detection does not require you to ingest any new data. The detection does require the ability to search the _audit index. This detection may assist in efforts to discover abuse of edit_user privilege.
Known False Positives
This search may produce false positives as password changing actions may be part of normal behavior. Operator will need to investigate these actions in order to discern exploitation attempts.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
64.0 | 80 | 80 | Possible attempt to abuse edit_user function by $user$ |
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
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
source | version: 1