: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.

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Description

The following analytic identifies the bootloader paths on Windows endpoints. It leverages a PowerShell Scripted input to capture this data, which is then processed and aggregated using Splunk. Monitoring bootloader paths is significant for a SOC as it helps detect unauthorized modifications that could indicate bootkits or other persistent threats. If confirmed malicious, such activity could allow attackers to maintain persistence, bypass security controls, and potentially control the boot process, leading to full system compromise.

  • Type: Hunting

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

  • Last Updated: 2024-05-15
  • Author: Michael Haag, Splunk
  • ID: 4f7e3913-4db3-4ccd-afe4-31198982305d

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1542.001 System Firmware Persistence, Defense Evasion
T1542 Pre-OS Boot Defense Evasion, Persistence
Kill Chain Phase
  • Installation
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`bootloader_inventory` 
| stats count min(_time) as firstTime max(_time) as lastTime values(_raw) by host 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `windows_bootloader_inventory_filter`

Macros

The SPL above uses the following Macros:

:information_source: windows_bootloader_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
  • _raw

How To Implement

To implement this analytic, a new stanza will need to be added to a inputs.conf and deployed to all or some Windows endpoints. https://gist.github.com/MHaggis/26518cd2844b0e03de6126660bb45707 provides the stanza. If modifying the sourcetype, be sure to update the Macro for this analytic. Recommend running it daily, or weekly, depending on threat model.

Known False Positives

No false positives here, only bootloaders. Filter as needed or create a lookup as a baseline.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
81.0 90 90 A list of BootLoaders are present 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