Splunk HTTP Response Splitting Via Rest SPL Command
Description
A low-privileged user, using a specially crafted search command, can trigger an HTTP response splitting vulnerability with the rest SPL command that lets them potentially access other REST endpoints in the system arbitrarily, including accessing restricted content such as password files. This is because the user is able to inject the rest SPL command into the q parameter of an HTTP GET web request. The vulnerability requires the attacker to phish the victim by tricking them into initiating a request within their browser. The attacker cannot exploit the vulnerability at will.
- Type: Hunting
-
Product: Splunk Enterprise
- Last Updated: 2023-05-23
- Author: Rod Soto, Chase Franklin
- ID: e615a0e1-a1b2-4196-9865-8aa646e1708c
Annotations
Kill Chain Phase
- Exploitation
NIST
- DE.AE
CIS20
- CIS 10
CVE
Search
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`audit_searches` AND search IN ("*
|*rest*POST*","*
|*rest*PUT*","*
|*rest*PATCH*","*
|*rest*DELETE*") AND NOT search="*audit_searches*"
| table user info has_error_msg search _time
| `splunk_http_response_splitting_via_rest_spl_command_filter`
Macros
The SPL above uses the following Macros:
splunk_http_response_splitting_via_rest_spl_command_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.
- search
- testing_endpoint
- info
- has_error_msg
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 search may assist in detecting possible http response splitting exploitation attemptss.
Known False Positives
This search may have produce false positives as malformed or erroneous requests made to this endpoint may be executed willingly or erroneously by operators.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
25.0 | 50 | 50 | Suspicious access 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