Detection: Suspicious Rundll32 PluginInit

Description

The following analytic identifies the execution of the rundll32.exe process with the "plugininit" parameter. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line arguments. This activity is significant because the "plugininit" parameter is commonly associated with IcedID malware, which uses it to execute an initial DLL stager to download additional payloads. If confirmed malicious, this behavior could lead to further malware infections, data exfiltration, or complete system compromise.

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_rundll32` Processes.process=*PluginInit* by  Processes.process_name Processes.process Processes.parent_process_name Processes.original_file_name Processes.parent_process Processes.process_id Processes.parent_process_id Processes.dest Processes.user 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `suspicious_rundll32_plugininit_filter`

Data Source

Name Platform Sourcetype Source Supported App
CrowdStrike ProcessRollup2 N/A 'crowdstrike:events:sensor' 'crowdstrike' N/A

Macros Used

Name Value
process_rundll32 (Processes.process_name=rundll32.exe OR Processes.original_file_name=RUNDLL32.EXE)
suspicious_rundll32_plugininit_filter search *
suspicious_rundll32_plugininit_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1218 System Binary Proxy Execution Defense Evasion
T1218.011 Rundll32 Defense Evasion
KillChainPhase.EXPLOITAITON
NistCategory.DE_CM
Cis18Value.CIS_10
Lazarus Group
APT19
APT28
APT3
APT32
APT38
APT41
Blue Mockingbird
Carbanak
CopyKittens
FIN7
Gamaredon Group
HAFNIUM
Kimsuky
Lazarus Group
LazyScripter
Magic Hound
MuddyWater
Sandworm Team
TA505
TA551
Wizard Spider

Default Configuration

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

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

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

third party application may used this dll export name to execute function.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
rundll32 process $process_name$ with commandline $process$ in host $dest$ 42 60 70
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation Passing N/A N/A N/A
Unit Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational xmlwineventlog
Integration ✅ Passing Dataset XmlWinEventLog:Microsoft-Windows-Sysmon/Operational xmlwineventlog

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: 3