Detection: Windows Credential Target Information Structure in Commandline

Description

Detects DNS-based Kerberos coercion attacks where adversaries inject marshaled credential structures into DNS records to spoof SPNs and redirect authentication such as in CVE-2025-33073. This detection leverages process creation events looking for specific CREDENTIAL_TARGET_INFORMATION structures.

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process="*1UWhRCA*" Processes.process="*AAAAA*" Processes.process="*YBAAAA*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `windows_credential_target_information_structure_in_commandline_filter`

Data Source

Name Platform Sourcetype Source
Sysmon EventID 1 Windows icon Windows 'XmlWinEventLog' 'XmlWinEventLog:Microsoft-Windows-Sysmon/Operational'

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
windows_credential_target_information_structure_in_commandline_filter search *
windows_credential_target_information_structure_in_commandline_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
T1557.001 LLMNR/NBT-NS Poisoning and SMB Relay Collection
T1187 Forced Authentication Credential Access
T1071.004 DNS Credential Access
Command and Control
Exploitation
DE.CM
CIS 10

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 Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

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

Commands with all of these base64 encoded values are unusual in production environments. Filter as needed.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message:

An instance of CREDENTIAL_TARGET_INFORMATION magic string was identified in a command on endpoint $dest$ by user $user$.

Risk Object Risk Object Type Risk Score Threat Objects
user user 44 No Threat Objects
dest system 44 No Threat Objects

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: 1