The following analytic identifies the process -
esentutl.exe - being used to capture credentials stored in ntds.dit or the SAM file on disk. During triage, review parallel processes and determine if legitimate activity. Upon determination of illegitimate activity, take further action to isolate and contain the threat.
- Type: Hunting
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-08-18
- Author: Michael Haag, Splunk
- ID: d372f928-ce4f-11eb-a762-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_esentutl` Processes.process IN ("*ntds*", "*SAM*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esentutl_sam_copy_filter`
The SPL above uses the following Macros:
esentutl_sam_copy_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
False positives should be limited. Filter as needed.
Associated Analytic Story
|80.0||80||100||An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user user$ attempting to capture credentials for offline cracking or observability.|
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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