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The following analytic detects the behavior of dumping credentials from memory, a tactic commonly used by adversaries to exploit the Local Security Authority Subsystem Service (LSASS) in Windows, which manages system-level authentication. The detection is made by monitoring logs with process information from endpoints and identifying instances where the rundll32 process is used in conjunction with the comsvcs.dll and MiniDump. This indicates potential LSASS dumping attempts used by threat actors to obtain valuable credentials. The detection is important because credential theft can lead to broader system compromise, persistence, lateral movement, and escalated privileges. No legitimate use of this technique has been identified yet. This behavior is often part of more extensive attack campaigns and is associated with numerous threat groups that use the stolen credentials to access sensitive information or systems, leading to data theft, ransomware attacks, or other damaging outcomes. False positives can occur since legitimate uses of the LSASS process can cause benign activities to be flagged. Next steps include reviewing the processes involved in the LSASS dumping attempt after triage and inspecting any relevant on-disk artifacts and concurrent processes to identify the attack source.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2023-04-14
  • Author: Patrick Bareiss, Splunk
  • ID: 8943b567-f14d-4ee8-a0bb-2121d4ce3184




ID Technique Tactic
T1003.001 LSASS Memory Credential Access
T1003 OS Credential Dumping Credential Access
Kill Chain Phase
  • Exploitation
  • DE.CM
  • CIS 10
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*comsvcs.dll* Processes.process=*MiniDump* by Processes.user Processes.parent_process_name Processes.process_name Processes.original_file_name Processes.process Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `dump_lsass_via_comsvcs_dll_filter`


The SPL above uses the following Macros:

:information_source: dump_lsass_via_comsvcs_dll_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • Processes.dest
  • Processes.user
  • Processes.parent_process_name
  • Processes.parent_process
  • Processes.original_file_name
  • Processes.process_name
  • Processes.process
  • Processes.process_id
  • Processes.parent_process_path
  • Processes.process_path
  • Processes.parent_process_id

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

None identified.

Associated Analytic Story


Risk Score Impact Confidence Message
80.0 80 100 An instance of $parent_process_name$ spawning $process_name$ was identified accessing credentials using comsvcs.dll on endpoint $dest$ by user $user$.

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.


Test Dataset

Replay any dataset to Splunk Enterprise by using our tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

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