This analytic is developed to detect possible event trigger execution through screensaver registry entry modification for persistence or privilege escalation. This technique was seen in several APT and malware where they put the malicious payload path to the SCRNSAVE.EXE registry key to redirect the execution to their malicious payload path. This TTP is a good indicator that some attacker may modify this entry for their persistence and privilege escalation.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-09-27
- Author: Teoderick Contreras, Splunk
- ID: 58cea3ec-1f6d-11ec-8560-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.Registry where (Registry.registry_path="*\\Control Panel\\Desktop\\SCRNSAVE.EXE*") by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `drop_dm_object_name(Registry)` | `screensaver_event_trigger_execution_filter`
The SPL above uses the following Macros:
screensaver_event_trigger_execution_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 must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
Known False Positives
Associated Analytic Story
|72.0||80||90||modified/added/deleted registry entry $Registry.registry_path$ 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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