Detect SharpHound Usage
The following analytic identifies SharpHound binary usage by using the original filena,e. In addition to renaming the PE, other coverage is available to detect command-line arguments. This particular analytic looks for the original_file_name of
SharpHound.exe and the process name. It is possible older instances of SharpHound.exe have different original filenames. Dependent upon the operator, the code may be re-compiled and the attributes removed or changed to anything else. During triage, review the metadata of the binary in question. Review parallel processes for suspicious behavior. Identify the source of this binary.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-05-27
- Author: Michael Haag, Splunk
- ID: dd04b29a-beed-11eb-87bc-acde48001122
Kill Chain Phase
1 2 3 4 5 6 | 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 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `detect_sharphound_usage_filter`
The SPL above uses the following Macros:
detect_sharphound_usage_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Supported Add-on (TA)
List of Splunk Add-on’s tested to work with the analytic.
List of fields required to use this analytic.
How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the
Endpoint datamodel in the
Processes node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
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
|24.0||30||80||Potential SharpHound binary identified on $dest$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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