THIS IS A DEPRECATED DETECTION
This detection has been marked deprecated by the Splunk Threat Research team. This means that it will no longer be maintained or supported.
Detect a renamed instance of procdump.exe dumping the lsass process. This query looks for both -mm and -ma usage. -mm will produce a mini dump file and -ma will write a dump file with all process memory. Both are highly suspect and should be reviewed. Modify the query as needed.
During triage, confirm this is procdump.exe executing. If it is the first time a Sysinternals utility has been ran, it is possible there will be a -accepteula on the command line. Review other endpoint data sources for cross process (injection) into lsass.exe.
- Type: Hunting
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2021-02-01
- Author: Michael Haag, Splunk
- ID: 21276daa-663d-11eb-ae93-0242ac130002
Kill Chain Phase
- CIS 10
1 2 3 4 5 `sysmon` OriginalFileName=procdump process_name!=procdump*.exe EventID=1 (CommandLine=*-ma* OR CommandLine=*-mm*) CommandLine=*lsass* | stats count min(_time) as firstTime max(_time) as lastTime by dest, parent_process_name, process_name, OriginalFileName, CommandLine | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `dump_lsass_via_procdump_rename_filter`
The SPL above uses the following Macros:
dump_lsass_via_procdump_rename_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
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the
Endpoint datamodel in the
Known False Positives
Associated Analytic Story
|80.0||80||100||The following $process_name$ has been identified as renamed, spawning from $parent_process_name$ on $dest$, attempting to dump lsass.exe.|
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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