Name:Windows Credential Target Information Structure in Commandline id:f79c5d7a-dd99-4263-93e1-49ace5634c82 version:5 date:None author:Raven Tait, Splunk status:production type:TTP 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. Data_source:
-Sysmon EventID 1
search:| 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 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_credential_target_information_structure_in_commandline_filter`
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:Commands with all of these base64 encoded values are unusual in production environments. Filter as needed. References: -https://web.archive.org/web/20250617122747/https://www.synacktiv.com/publications/ntlm-reflection-is-dead-long-live-ntlm-reflection-an-in-depth-analysis-of-cve-2025 -https://www.synacktiv.com/publications/relaying-kerberos-over-smb-using-krbrelayx -https://www.guidepointsecurity.com/blog/the-birth-and-death-of-loopyticket/ drilldown_searches: name:'View the detection results for - "$user$" and "$dest$"' search:'%original_detection_search% | search user = "$user$" dest = "$dest$"' earliest_offset:'$info_min_time$' latest_offset:'$info_max_time$' name:'View risk events for the last 7 days for - "$user$" and "$dest$"' search:'| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`' earliest_offset:'7d' latest_offset:'0' analytic_story:['Compromised Windows Host', 'Suspicious DNS Traffic', 'Local Privilege Escalation With KrbRelayUp', 'Kerberos Coercion with DNS']
