This search is to detect modification of registry to bypass UAC windows feature. This technique is to add a payload dll path on .NET COR file path that will be loaded by mmc.exe as soon it was executed. This detection rely on monitoring the registry key and values in the detection area. It may happened that windows update some dll related to mmc.exe and add dll path in this registry. In this case filtering is needed.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-02-18
- Author: Teoderick Contreras, Splunk
- ID: 0252ca80-e30d-11eb-8aa3-acde48001122
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where Registry.registry_path= "*\\Environment\\COR_PROFILER_PATH" Registry.registry_value_data = "*.dll" by Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.dest | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `net_profiler_uac_bypass_filter`
The SPL above uses the following Macros:
net_profiler_uac_bypass_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the
Endpoint datamodel in the
Registry node. Also make sure that this registry was included in your config files ex. sysmon config to be monitored.
Known False Positives
limited false positive. It may trigger by some windows update that will modify this registry.
Associated Analytic Story
|63.0||70||90||Suspicious modification of registry $registry_path$ with possible payload path $registry_path$ and key $registry_key_name$ in $dest$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 2