This search detects the process execution and arguments required to silently create a shim database. The sdbinst.exe application is used to install shim database files (.sdb). A shim is a small library which transparently intercepts an API, changes the parameters passed, handles the operation itself, or redirects the operation elsewhere.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2020-11-23
- Author: David Dorsey, Splunk
- ID: 404620de-46d8-48b6-90cc-8a8d7b0876a3
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 | tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_filter`
The SPL above uses the following Macros:
shim_database_installation_with_suspicious_parameters_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
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
Associated Analytic Story
|63.0||70||90||A process $process_name$ that possible create a shim db silently in host $dest$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 4