Name:Windows Theme File Creation in Unusual Location id:a11f5f36-2c32-4323-8cb0-0fec84b3188d version:2 date:None author:Raven Tait, Splunk status:production type:Anomaly Description:Detects theme files being created in unusual locations. These files, used to customize desktop appearances, have been used for remote code execution and NTLM coercion attacks. Data_source:
-Sysmon EventID 11
search:| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime
from datamodel=Endpoint.Filesystem where
Filesystem.file_path IN ( "*\\Desktop\\*", "*\\Documents\\*", "*\\Downloads\\*", "*\\Temp\\*" ) Filesystem.file_name="*.theme" Filesystem.action="created"
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:Legitimate customization or IT management may use theme files that trigger this detection. Review and allow trusted themes from authorized sources. References: -https://www.darkreading.com/vulnerabilities-threats/recurring-windows-flaw-could-expose-user-credentials drilldown_searches: earliest_offset:'$info_min_time$' latest_offset:'$info_max_time$' name:'View the detection results for - "$user$" and "$dest$"' search:'%original_detection_search% | search user = "$user$" dest = "$dest$"' 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:['Spearphishing Attachments']
