The following PowerShell Script Block analytic identifies the native ability to add a DLL to the Windows Global Assembly Cache. Each computer where the Common Language Runtime is installed has a machine-wide code cache called the Global Assembly Cache. The Global Assembly Cache stores assemblies specifically designated to be shared by several applications on the computer. By adding a DLL to the GAC, this allows an adversary to call it via any other means across the operating systems. This is native and built into Windows. Per the Microsoft blog, the more high fidelity method may be to look for W3WP.exe spawning PowerShell that includes the same CommandLine as identified in this analytic.
- Type: TTP
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2023-01-18
- Author: Michael Haag, Splunk
- ID: 3fc16961-97e5-4a5b-a079-e4ab0d9763eb
Kill Chain Phase
- CIS 10
1 2 3 4 5 `powershell` EventCode=4104 ScriptBlockText IN("*system.enterpriseservices.internal.publish*") | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer user_id | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powershell_add_module_to_global_assembly_cache_filter`
The SPL above uses the following Macros:
windows_powershell_add_module_to_global_assembly_cache_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 analytic, you will need to enable PowerShell Script Block Logging on some or all endpoints. Additional setup here https://docs.splunk.com/Documentation/UBA/18.104.22.168/GetDataIn/AddPowerShell#Configure_module_logging_for_PowerShell.
Known False Positives
False positives may be present based on developers or third party utilities adding items to the GAC.
Associated Analytic Story
|64.0||80||80||PowerShell was used to install a module to the Global Assembly Cache on $Computer$.|
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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