The following hunting analytic is designed to detect the usage of headless browsers in an organization. Headless browsers are web browsers without a graphical user interface and are operated via a command line interface or network requests. They are often used for automating tasks but can also be utilized by adversaries for malicious activities such as web scraping, automated testing, and performing actions on web pages without detection. The detection is based on the presence of "–headless" and "–disable-gpu" command line arguments which are commonly used in headless browsing.
- Type: Hunting
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2023-09-08
- Author: Michael Haag, Splunk
- ID: 869ba261-c272-47d7-affe-5c0aa85c93d6
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 | 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`
The SPL above uses the following Macros:
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.
List of fields required to use this analytic.
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
|15.0||30||50||Behavior related to headless browser usage detected on $dest$ 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.
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