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Description

The following analytic detects modifications to the SCRNSAVE.EXE registry entry, indicating potential event trigger execution via screensaver settings for persistence or privilege escalation. It leverages registry activity data from the Endpoint data model to identify changes to the specified registry path. This activity is significant as it is a known technique used by APT groups and malware to maintain persistence or escalate privileges. If confirmed malicious, this could allow an attacker to execute arbitrary code with elevated privileges, leading to further system compromise and persistent access.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2024-05-13
  • Author: Teoderick Contreras, Splunk
  • ID: 58cea3ec-1f6d-11ec-8560-acde48001122

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1546 Event Triggered Execution Privilege Escalation, Persistence
T1546.002 Screensaver Privilege Escalation, Persistence
Kill Chain Phase
  • Exploitation
  • Installation
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.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`

Macros

The SPL above uses the following Macros:

:information_source: 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.

Required fields

List of fields required to use this analytic.

  • _time
  • Registry.dest
  • Registry.user
  • Registry.registry_path
  • Registry.registry_key_name
  • Registry.registry_value_name

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

unknown

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
72.0 80 90 Registry path $registry_path$ was modified, added, or deleted in $dest$.

: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: 2