Detection: XSL Script Execution With WMIC

Updated Date: 2024-08-14 ID: 004e32e2-146d-11ec-a83f-acde48001122 Author: Teoderick Contreras, Splunk Type: TTP Product: Splunk Enterprise Security

Description

The following analytic detects the execution of an XSL script using the WMIC process, which is often indicative of malicious activity. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving WMIC and XSL files. This behavior is significant as it has been associated with the FIN7 group, known for using this technique to execute malicious scripts. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to system compromise and further malicious actions within the environment.

Search

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` Processes.process = "*os get*" Processes.process="*/format:*" Processes.process = "*.xsl*" by Processes.parent_process_name Processes.parent_process Processes.process_name Processes.process_id Processes.process Processes.dest Processes.user 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `xsl_script_execution_with_wmic_filter`

Data Source

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

Macros Used

Name	Value
process_wmic	`(Processes.process_name=wmic.exe OR Processes.original_file_name=wmic.exe)`
xsl_script_execution_with_wmic_filter	`search *`

xsl_script_execution_with_wmic_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
T1220	XSL Script Processing	Defense Evasion

KillChainPhase.EXPLOITAITON

NistCategory.DE_CM

Cis18Value.CIS_10

Cobalt Group

Higaisa

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

unknown

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ utilizing wmic to load a XSL script.	49	70	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