Name:Shim Database Installation With Suspicious Parameters id:404620de-46d8-48b6-90cc-8a8d7b0876a3 version:7 date:2024-11-28 author:David Dorsey, Splunk status:production type:TTP Description:The following analytic detects the execution of sdbinst.exe with parameters indicative of silently creating a shim database. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line arguments. This activity is significant because shim databases can be used to intercept and manipulate API calls, potentially allowing attackers to bypass security controls or achieve persistence. If confirmed malicious, this could enable unauthorized code execution, privilege escalation, or persistent access to the compromised system. Data_source:
-Sysmon EventID 1
-Windows Event Log Security 4688
-CrowdStrike ProcessRollup2
search:| tstats `security_content_summariesonly` values(Processes.process) as process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = sdbinst.exe NOT Processes.process IN ("\"C:\\Windows\\System32\\sdbinst.exe\"", "C:\\Windows\\System32\\sdbinst.exe", "*-mm", "*-?") by Processes.process_name Processes.parent_process_name Processes.dest Processes.user | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `shim_database_installation_with_suspicious_parameters_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:None identified References: drilldown_searches: name:'View the detection results for - "$dest$" and "$user$"' search:'%original_detection_search% | search dest = "$dest$" user = "$user$"' earliest_offset:'$info_min_time$' latest_offset:'$info_max_time$' name:'View risk events for the last 7 days for - "$dest$" and "$user$"' search:'| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") starthoursago=168 | 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:'$info_min_time$' latest_offset:'$info_max_time$' tags: analytic_story: - 'Windows Persistence Techniques' - 'Compromised Windows Host' asset_type:Endpoint confidence:90 impact:70 message:A process $process_name$ that possible create a shim db silently in host $dest$ mitre_attack_id: - 'T1546.011' - 'T1546' observable: name:'dest' type:'Hostname' - role: - 'Victim' name:'user' type:'User' - role: - 'Victim' product: - 'Splunk Enterprise' - 'Splunk Enterprise Security' - 'Splunk Cloud' required_fields: - '_time' - 'Processes.process_name' - 'Processes.parent_process_name' - 'Processes.dest' - 'Processes.user' risk_score:63 security_domain:endpoint
