The following analytic identifies modification in the Windows registry to prevent user running specific computer programs that could aid them in manually removing malware or detecting it using security products. This technique was recently identified in Azorult malware where it uses this registry value to prevent several AV products to execute on the compromised host machine.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-06-22
- Author: Teoderick Contreras, Splunk
- ID: 4bc788d3-c83a-48c5-a4e2-e0c6dba57889
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path= "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\Explorer\\DisallowRun*" Registry.registry_value_data="0x00000001" by Registry.registry_key_name Registry.user Registry.registry_path Registry.registry_value_data Registry.action Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_modify_registry_disallow_windows_app_filter`
The SPL above uses the following Macros:
windows_modify_registry_disallow_windows_app_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 process that include the name of the process responsible for the changes from your endpoints into the
Endpoint datamodel in the
Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
Known False Positives
Administrators may enable or disable this feature that may cause some false positive. Filter as needed.
Associated Analytic Story
|49.0||70||70||The registry for DisallowRun settings was modified to enable in $dest$.|
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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