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Description

The following analytic detects the execution of a specially crafted query using the 'collect' SPL command in Splunk Enterprise versions lower than 8.2.12, 9.0.6, and 9.1.1. It leverages audit logs to identify searches containing both 'makeresults' and 'collect' commands. This activity is significant because it can indicate an attempt to serialize untrusted data, potentially leading to arbitrary code execution. If confirmed malicious, this could allow an attacker to execute code within the Splunk environment, leading to unauthorized access and control over the system.

  • Type: Hunting

  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2024-05-26
  • Author: Chase Franklin, Rod Soto, Eric McGinnis, Splunk
  • ID: d1d8fda6-874a-400f-82cf-dcbb59d8e4db

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1190 Exploit Public-Facing Application Initial Access
Kill Chain Phase
  • Delivery
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


1
2
3
4
5



`audit_searches` file=* (search="*makeresults*" AND search="*collect*") 
| stats count min(_time) as firstTime max(_time) as lastTime by action file user splunk_server search 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `splunk_rce_via_serialized_session_payload_filter`

Macros

The SPL above uses the following Macros:

:information_source: splunk_rce_via_serialized_session_payload_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
  • info
  • user
  • search_type
  • count

How To Implement

Requires access to the _audit index.

Known False Positives

There are numerous many uses of the 'makeresults' and 'collect' SPL commands. Please evaluate the results of this search for potential abuse.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 Potential abuse of the 'collect' SPL command against $splunk_server$ by detected by $user$

:information_source: 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: 2