Detection: Bcdedit Command Back To Normal Mode Boot

Description

The following analytic detects the execution of a suspicious bcdedit command that reconfigures a host from safe mode back to normal boot. This detection leverages Endpoint Detection and Response (EDR) data, focusing on command-line executions involving bcdedit.exe with specific parameters. This activity is significant as it may indicate the presence of ransomware, such as BlackMatter, which manipulates boot configurations to facilitate encryption processes. If confirmed malicious, this behavior could allow attackers to maintain control over the boot process, potentially leading to further system compromise and data encryption.

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = bcdedit.exe Processes.process="*/deletevalue*" Processes.process="*{current}*"  Processes.process="*safeboot*" by Processes.process_name Processes.process Processes.parent_process_name Processes.dest Processes.user 
3|`drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `bcdedit_command_back_to_normal_mode_boot_filter`

Data Source

Name Platform Sourcetype Source
CrowdStrike ProcessRollup2 N/A 'crowdstrike:events:sensor' 'crowdstrike'
Sysmon EventID 1 Windows icon Windows 'xmlwineventlog' 'XmlWinEventLog:Microsoft-Windows-Sysmon/Operational'
Windows Event Log Security 4688 Windows icon Windows 'xmlwineventlog' 'XmlWinEventLog:Security'

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
bcdedit_command_back_to_normal_mode_boot_filter search *
bcdedit_command_back_to_normal_mode_boot_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
T1490 Inhibit System Recovery Impact
KillChainPhase.ACTIONS_ON_OBJECTIVES
NistCategory.DE_CM
Cis18Value.CIS_10
Sandworm Team
Wizard Spider

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 Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes node of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.

Known False Positives

unknown

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
bcdedit process with commandline $process$ to bring back to normal boot configuration the $dest$ 35 50 70
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 Passing N/A N/A N/A
Unit Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational XmlWinEventLog
Integration ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational XmlWinEventLog

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