Windows AD DSRM Account Changes
Description
The following analytic identifies changes to the Directory Services Restore Mode (DSRM) account behavior via registry modifications. It detects alterations in the registry path "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior" with specific values indicating potential misuse. This activity is significant because the DSRM account, if misconfigured, can be exploited to persist within a domain, similar to a local administrator account. If confirmed malicious, an attacker could gain persistent administrative access to a Domain Controller, leading to potential domain-wide compromise and unauthorized access to sensitive information.
Query · spl
| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\System\\CurrentControlSet\\Control\\Lsa\\DSRMAdminLogonBehavior" Registry.registry_value_data IN ("*1","*2") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_ad_dsrm_account_changes_filter`
Implementation guide
The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes node of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
Known false positives
- Disaster recovery events.
Analyst notes
Known false positives: Disaster recovery events.