Detect SharpHound Command-Line Arguments
The following analytic identifies common command-line arguments used by SharpHound
invoke-bloodhound. Being the script is FOSS, function names may be modified, but these changes are dependent upon the operator. In most instances the defaults are used. This analytic works to identify the common command-line attributes used. It does not cover the entirety of every argument in order to avoid false positives.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-06-01
- Author: Michael Haag, Splunk
- ID: a0bdd2f6-c2ff-11eb-b918-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 IN ("*-collectionMethod*","*invoke-bloodhound*") by Processes.dest Processes.user Processes.parent_process 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_command_line_arguments_filter`
The SPL above uses the following Macros:
detect_sharphound_command-line_arguments_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
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
|24.0||30||80||Possible SharpHound command-Line arguments 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.
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replay.py tool or the UI.
Alternatively you can replay a dataset into a Splunk Attack Range
source | version: 1