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Description

The following analytic is similar to SharpHound file modifications, but this instance covers the use of Invoke-AzureHound. AzureHound is the SharpHound equivilent but for Azure. It's possible this may never be seen in an environment as most attackers may execute this tool remotely. Once execution is complete, a zip file with a similar name will drop 20210601090751-azurecollection.zip. In addition to the zip, multiple .json files will be written to disk, which are in the zip.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2021-06-01
  • Author: Michael Haag, Splunk
  • ID: 1c34549e-c31b-11eb-996b-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
  • Reconnaissance
NIST
CIS20
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 ("*-azurecollection.zip", "*-azprivroleadminrights.json", "*-azglobaladminrights.json", "*-azcloudappadmins.json", "*-azapplicationadmins.json") by Filesystem.file_create_time Filesystem.process_id  Filesystem.file_name Filesystem.file_path Filesystem.dest 
| `drop_dm_object_name(Filesystem)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `detect_azurehound_file_modifications_filter`

Macros

The SPL above uses the following Macros:

:information_source: detect_azurehound_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
63.0 70 90 A file - $file_name$ was written to disk that is related to AzureHound, a AzureAD enumeration utility, has occurred on endpoint $dest$ by user $user$.

: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

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