Detection: Detect SharpHound Command-Line Arguments

Description

The following analytic detects the execution of SharpHound command-line arguments, specifically -collectionMethod and invoke-bloodhound. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as SharpHound is commonly used for Active Directory enumeration, which can be a precursor to lateral movement or privilege escalation. If confirmed malicious, this activity could allow an attacker to map out the network, identify high-value targets, and plan further attacks, potentially compromising sensitive information and critical systems.

Search

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*") by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `detect_sharphound_command_line_arguments_filter`

Data Source

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

Macros Used

Annotations

Default Configuration

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

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

False positives should be limited as the arguments used are specific to SharpHound. Filter as needed or add more command-line arguments as needed.

Associated Analytic Story

• BlackSuit Ransomware

• Ransomware

• Windows Discovery Techniques

Risk Based Analytics (RBA)

References

• https://attack.mitre.org/software/S0521/

• https://thedfirreport.com/?s=bloodhound

• https://github.com/BloodHoundAD/BloodHound/tree/master/Collectors

• https://github.com/BloodHoundAD/SharpHound3

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md#atomic-test-2---run-bloodhound-from-local-disk

Detection Testing

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: 4