This analytic is to detect a suspicious rundll32 commandline to clear shim cache. This technique is a anti-forensic technique to clear the cache taht are one important artifacts in terms of digital forensic during attacks or incident. This TTP is a good indicator that someone tries to evade some tools and clear foothold on the machine.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-10-05
- Author: Teoderick Contreras, Splunk
- ID: a913718a-25b6-11ec-96d3-acde48001122
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.Processes where `process_rundll32` AND Processes.process = "*apphelp.dll,ShimFlushCache*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.original_file_name | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `rundll32_shimcache_flush_filter`
The SPL above uses the following Macros:
rundll32_shimcache_flush_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
|80.0||80||100||rundll32 process execute $process$ to clear shim cache 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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