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Description

The following analytic detects the usage of headless browsers within an organization. It identifies processes containing the "–headless" and "–disable-gpu" command line arguments, which are indicative of headless browsing. This detection leverages data from the Endpoint.Processes datamodel to identify such processes. Monitoring headless browser usage is significant as these tools can be exploited by adversaries for malicious activities like web scraping, automated testing, and undetected web interactions. If confirmed malicious, this activity could lead to unauthorized data extraction, automated attacks, or other covert operations on web applications.

  • Type: Hunting
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2024-05-26
  • Author: Michael Haag, Splunk
  • ID: 869ba261-c272-47d7-affe-5c0aa85c93d6

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1564.003 Hidden Window Defense Evasion
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


1
2
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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process="*--headless*" AND Processes.process="*--disable-gpu*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `headless_browser_usage_filter`

Macros

The SPL above uses the following Macros:

:information_source: headless_browser_usage_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.

  • Processes.dest
  • Processes.user
  • Processes.parent_process
  • Processes.process_name
  • Processes.process
  • Processes.process_id
  • Processes.parent_process_id
  • sourcetype

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Processes node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.

Known False Positives

This hunting analytic is meant to assist with baselining and understanding headless browsing in use. Filter as needed.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
15.0 30 50 Behavior related to headless browser usage detected on $dest$ 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