Detection: Suspicious Rundll32 dllregisterserver

Description

The following analytic detects the execution of rundll32.exe with the DllRegisterServer command to load a DLL. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an attempt to register a malicious DLL, which can be a method for code execution or persistence. If confirmed malicious, an attacker could gain unauthorized code execution, escalate privileges, or maintain persistence within the environment, posing a severe security risk.

Search

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*dllregisterserver* by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.original_file_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `suspicious_rundll32_dllregisterserver_filter`

Data Source

Macros Used

Annotations

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Implementation

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

This is likely to produce false positives and will require some filtering. Tune the query by adding command line paths to known good DLLs, or filtering based on parent process names.

Associated Analytic Story

• IcedID

• Living Off The Land

• Suspicious Rundll32 Activity

Risk Based Analytics (RBA)

References

• https://attack.mitre.org/techniques/T1218/011/

• https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1218.011/T1218.011.md

• https://lolbas-project.github.io/lolbas/Binaries/Rundll32/

• https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/seedworm-apt-iran-middle-east

• https://github.com/pan-unit42/tweets/blob/master/2020-12-10-IOCs-from-Ursnif-infection-with-Delf-variant.txt

• https://www.crowdstrike.com/blog/duck-hunting-with-falcon-complete-qakbot-zip-based-campaign/

• https://docs.microsoft.com/en-us/windows/win32/api/olectl/nf-olectl-dllregisterserver?redirectedfrom=MSDN

Detection Testing

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

Source: GitHub | Version: 4