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
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.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`
The SPL above uses the following Macros:
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.
List of fields required to use this analytic.
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
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
|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 $user$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 1