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Description

The following analytic identifies potential log injection attempts into the Splunk server via specially crafted web URLs. It detects ANSI escape codes within the uri_path field of splunkd_webx logs. This activity is significant as it can lead to log file manipulation, potentially obfuscating malicious actions or misleading analysts. If confirmed malicious, an attacker could manipulate log files to hide their tracks or execute further attacks, compromising the integrity of the logging system and making incident response more challenging.

  • Type: Hunting

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

  • Last Updated: 2024-05-19
  • Author: Rod Soto
  • ID: de3908dc-1298-446d-84b9-fa81d37e959b

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



`splunkd_webx`  uri_path IN ("*\x1B*", "*\u001b*", "*\033*", "*\0x9*", "*\0x8*") 
| stats count by uri_path method host status clientip 
| `splunk_unauthenticated_log_injection_web_service_log_filter`

Macros

The SPL above uses the following Macros:

:information_source: splunk_unauthenticated_log_injection_web_service_log_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.

  • method
  • uri_path
  • host
  • status
  • clientip

How To Implement

This only affects web enabled Splunk instances. The detection does require the ability to search the _internal index.

Known False Positives

This hunting search will produce false positives if ANSI escape characters are included in URLs either voluntarily or by accident. This search will not detect obfuscated ANSI characters.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
9.0 30 30 Possible Splunk unauthenticated log injection web service log exploitation attempt against $host$ from $clientip$

: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