:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

Try in Splunk Security Cloud

Description

The following analytic identifies drivers being loaded across the fleet. It leverages a PowerShell script input deployed to critical systems to capture driver data. This detection is significant as it helps monitor for unauthorized or malicious drivers that could compromise system integrity. If confirmed malicious, such drivers could allow attackers to execute arbitrary code, escalate privileges, or maintain persistence within the environment.

  • Type: Hunting

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

  • Last Updated: 2024-05-23
  • Author: Michael Haag, Splunk
  • ID: f87aa96b-369b-4a3e-9021-1bbacbfcb8fb

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1068 Exploitation for Privilege Escalation Privilege Escalation
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`driverinventory` 
| stats values(Path) min(_time) as firstTime max(_time) as lastTime count by host DriverType 
| rename host as dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `windows_driver_inventory_filter`

Macros

The SPL above uses the following Macros:

:information_source: windows_driver_inventory_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.

  • _time
  • Path
  • host
  • DriverType

How To Implement

To capture the drivers by host, utilize the referenced Gist to create the inputs, props and transforms. Otherwise, this hunt query will not work.

Known False Positives

Filter and modify the analytic as you'd like. Filter based on path. Remove the system32\drivers and look for non-standard paths.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
5.0 50 10 Drivers have been identified on $dest$.

: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