Detection: Windows BootLoader Inventory

EXPERIMENTAL DETECTION

This detection status is set to experimental. The Splunk Threat Research team has not yet fully tested, simulated, or built comprehensive datasets for this detection. As such, this analytic is not officially supported. If you have any questions or concerns, please reach out to us at research@splunk.com.

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.

1`bootloader_inventory` 
2| stats count min(_time) as firstTime max(_time) as lastTime values(_raw) by host 
3| `security_content_ctime(firstTime)` 
4| `security_content_ctime(lastTime)` 
5| `windows_bootloader_inventory_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name Value
bootloader_inventory sourcetype = PwSh:bootloader
windows_bootloader_inventory_filter search *
windows_bootloader_inventory_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1542.001 System Firmware Defense Evasion
T1542 Pre-OS Boot Persistence
KillChainPhase.EXPLOITAITON
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_10

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Risk Event False
This configuration file applies to all detections of type hunting.

Implementation

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
A list of BootLoaders are present on $dest$ 81 90 90
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation Not Applicable N/A N/A N/A
Unit ❌ Failing N/A N/A N/A
Integration ❌ Failing N/A N/A N/A

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: GitHub | Version: 3