Name:FodHelper UAC Bypass id:909f8fd8-7ac8-11eb-a1f3-acde48001122 version:10 date:2025-04-22 author:Michael Haag, Splunk status:production type:TTP Description:The following analytic detects the execution of fodhelper.exe, which is known to exploit a User Account Control (UAC) bypass by leveraging specific registry keys. The detection method uses Endpoint Detection and Response (EDR) telemetry to identify when fodhelper.exe spawns a child process and accesses the registry keys. This activity is significant because it indicates a potential privilege escalation attempt by an attacker. If confirmed malicious, the attacker could execute commands with elevated privileges, leading to unauthorized system changes and potential full system compromise. Data_source:
-Sysmon EventID 1
-Windows Event Log Security 4688
-CrowdStrike ProcessRollup2
search:| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=fodhelper.exe 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)` | `fodhelper_uac_bypass_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:Limited to no false positives are expected. References: -https://blog.malwarebytes.com/malwarebytes-news/2021/02/lazyscripter-from-empire-to-double-rat/ -https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1548.002/T1548.002.md -https://github.com/gushmazuko/WinBypass/blob/master/FodhelperBypass.ps1 -https://attack.mitre.org/techniques/T1548/002/ 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 ("$user$", "$dest$") 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: - 'IcedID' - 'ValleyRAT' - 'Compromised Windows Host' - 'Windows Defense Evasion Tactics' asset_type:Endpoint mitre_attack_id: - 'T1112' - 'T1548.002' product: - 'Splunk Enterprise' - 'Splunk Enterprise Security' - 'Splunk Cloud' security_domain:endpoint
