Splunk Unauthorized Experimental Items Creation

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Description

This hunting search provides information on finding possible creation of unauthorized items against /experimental endpoint.

• Type: Hunting

• Product: Splunk Enterprise

• Last Updated: 2024-07-01
• Author: Rod Soto, Chase Franklin
• ID: 84afda04-0cd6-466b-869e-70d6407d0a34

Annotations

ATT&CK

ATT&CK

ID	Technique	Tactic
T1189	Drive-by Compromise	Initial Access

Kill Chain Phase

• Delivery

NIST

• DE.AE

CIS20

• CIS 10

CVE

ID	Summary	CVSS

Search

`splunkda` */experimental/* method=POST 
| stats count min(_time) as firstTime max(_time) as lastTime by clientip method uri_path uri status 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `splunk_unauthorized_experimental_items_creation_filter`

Macros

The SPL above uses the following Macros:

• security_content_ctime
• splunkda

splunk_unauthorized_experimental_items_creation_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.

• clientip
• method
• uri_path
• uri
• status

How To Implement

Requires access to internal indexes.

Known False Positives

Not all requests are going to be malicious, there will be false positives, however operator must find suspicious items that might have been created by an unauthorized user.

Associated Analytic Story

• Splunk Vulnerabilities

RBA

Risk Score	Impact	Confidence	Message
5.0	5	100	Possible unauthorized creation of experimental items from $clientip$

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://advisory.splunk.com/SVD-2024-0715

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