The following analytic identifies the decompile parameter with the HTML Help application, HH.exe. This is a uncommon command to see ran and behavior. Most recently this was seen in a APT41 campaign where a CHM file was delivered and a script inside used a technique for running an arbitrary command in a CHM file via an ActiveX object. This unpacks an HTML help file to a specified path for launching the next stage.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-08-31
- Author: Michael Haag, Splunk
- ID: 2acf0e19-4149-451c-a3f3-39cd3c77e37d
Kill Chain Phase
- CIS 3
- CIS 5
- CIS 16
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_hh` Processes.process=*-decompile* by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_system_binary_proxy_execution_compiled_html_file_decompile_filter`
The SPL above uses the following Macros:
windows_system_binary_proxy_execution_compiled_html_file_decompile_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
Processes node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
Known False Positives
False positives should be limited, filter as needed.
Associated Analytic Story
|90.0||100||90||$process_name$ has been identified using decompile against a CHM on $dest$ under user $user$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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