Detection: System User Discovery With Query

Description

The following analytic detects the execution of query.exe with command-line arguments aimed at discovering logged-in users. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries may use query.exe to gain situational awareness and perform Active Directory discovery on compromised endpoints. If confirmed malicious, this behavior could allow attackers to identify active users, aiding in further lateral movement and privilege escalation within the network.

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="query.exe") (Processes.process=*user*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `system_user_discovery_with_query_filter`

Data Source

Name Platform Sourcetype Source
CrowdStrike ProcessRollup2 N/A 'crowdstrike:events:sensor' 'crowdstrike'
Sysmon EventID 1 Windows icon Windows 'xmlwineventlog' 'XmlWinEventLog:Microsoft-Windows-Sysmon/Operational'
Windows Event Log Security 4688 Windows icon Windows 'xmlwineventlog' 'XmlWinEventLog:Security'

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
system_user_discovery_with_query_filter search *
system_user_discovery_with_query_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1033 System Owner/User Discovery Discovery
KillChainPhase.EXPLOITAITON
NistCategory.DE_AE
Cis18Value.CIS_10
APT19
APT3
APT32
APT37
APT38
APT39
APT41
Aquatic Panda
Chimera
Dragonfly
Earth Lusca
FIN10
FIN7
FIN8
GALLIUM
Gamaredon Group
HAFNIUM
HEXANE
Ke3chang
Lazarus Group
LuminousMoth
Magic Hound
Moonstone Sleet
MuddyWater
OilRig
Patchwork
Sandworm Team
Sidewinder
Stealth Falcon
Threat Group-3390
Tropic Trooper
Volt Typhoon
Windshift
Winter Vivern
Wizard Spider
ZIRCONIUM

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Risk Event False
This configuration file applies to all detections of type hunting.

Implementation

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

Administrators or power users may use this command for troubleshooting.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
System user discovery on $dest$ 15 30 50
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation Passing N/A N/A N/A
Unit Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational xmlwineventlog
Integration ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational xmlwineventlog

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


Source: GitHub | Version: 2