Net Localgroup Discovery

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Description

The following hunting analytic will identify the use of localgroup discovery using net localgroup. During triage, review parallel processes and identify any further suspicious behavior.

• Type: Hunting
• Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
• Datamodel: Endpoint
• Last Updated: 2021-09-14
• Author: Michael Haag, Splunk
• ID: 54f5201e-155b-11ec-a6e2-acde48001122

Annotations

ATT&CK

ATT&CK

ID	Technique	Tactic
T1069	Permission Groups Discovery	Discovery

T1069.001

Local Groups

Discovery

Kill Chain Phase

• Exploitation

NIST

• DE.AE

CIS20

• CIS 10

CVE

Search

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=net.exe OR Processes.process_name=net1.exe (Processes.process="*localgroup*") by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.original_file_name Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `net_localgroup_discovery_filter`

Macros

The SPL above uses the following Macros:

• security_content_ctime
• security_content_summariesonly

net_localgroup_discovery_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

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 Processes node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.

Known False Positives

False positives may be present. Tune as needed.

Associated Analytic Story

• Active Directory Discovery
• Windows Discovery Techniques
• Azorult
• Windows Post-Exploitation
• Prestige Ransomware
• Volt Typhoon
• IcedID

RBA

Risk Score	Impact	Confidence	Message
15.0	30	50	Local group discovery on $dest$ by $user$.

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

Reference

• https://attack.mitre.org/techniques/T1069/001/
• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1069.001/T1069.001.md
• https://media.defense.gov/2023/May/24/2003229517/-1/-1/0/CSA_Living_off_the_Land.PDF
• https://thedfirreport.com/2023/05/22/icedid-macro-ends-in-nokoyawa-ransomware/

Test Dataset

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

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