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Description

SharpHound is used as a reconnaissance collector, ingestor, for BloodHound. SharpHound will query the domain controller and begin gathering all the data related to the domain and trusts. For output, it will drop a .zip file upon completion following a typical pattern that is often not changed. This analytic focuses on the default file name scheme. Note that this may be evaded with different parameters within SharpHound, but that depends on the operator. -randomizefilenames and -encryptzip are two examples. In addition, executing SharpHound via .exe or .ps1 without any command-line arguments will still perform activity and dump output to the default filename. Example default filename 20210601181553_BloodHound.zip. SharpHound creates multiple temp files following the same pattern 20210601182121_computers.json, domains.json, gpos.json, ous.json and users.json. Tuning may be required, or remove these json's entirely if it is too noisy. During traige, review parallel processes for further suspicious behavior. Typically, the process executing the .ps1 ingestor will be PowerShell.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2024-03-14
  • Author: Michael Haag, Splunk
  • ID: 42b4b438-beed-11eb-ba1d-acde48001122

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1087.002 Domain Account Discovery
T1069.001 Local Groups Discovery
T1482 Domain Trust Discovery Discovery
T1087.001 Local Account Discovery
T1087 Account Discovery Discovery
T1069.002 Domain Groups Discovery
T1069 Permission Groups Discovery Discovery
Kill Chain Phase
  • Exploitation
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*bloodhound.zip", "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json", "*_ous.json", "*_containers.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.user
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_sharphound_file_modifications_filter`

Macros

The SPL above uses the following Macros:

:information_source: detect_sharphound_file_modifications_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • file_path
  • dest
  • file_name
  • process_id
  • file_create_time

How To Implement

To successfully implement this search you need to be ingesting information on file modifications that include the name of the process, and file, responsible for the changes from your endpoints into the Endpoint datamodel in the Filesystem node.

Known False Positives

False positives should be limited as the analytic is specific to a filename with extension .zip. Filter as needed.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
24.0 30 80 Potential SharpHound file modifications identified on $dest$

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

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: 3