Detection: Detect SharpHound Usage

Description

The following analytic detects the usage of the SharpHound binary by identifying its original filename, SharpHound.exe, and the process name. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process metadata and command-line executions. SharpHound is a tool used for Active Directory enumeration, often by attackers during the reconnaissance phase. If confirmed malicious, this activity could allow an attacker to map out the network, identify high-value targets, and plan further attacks, potentially leading to privilege escalation and lateral movement within the environment.

Search

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name=sharphound.exe OR Processes.original_file_name=SharpHound.exe) by Processes.dest Processes.user Processes.parent_process_name Processes.original_file_name 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_usage_filter`

Data Source

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 this is specific to a file attribute not used by anything else. Filter as needed.

Associated Analytic Story

• 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