Detection: CMD Carry Out String Command Parameter

Description

The following analytic detects the use of cmd.exe /c to execute commands, a technique often employed by adversaries and malware to run batch commands or invoke other shells like PowerShell. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process metadata. Monitoring this activity is crucial as it can indicate script-based attacks or unauthorized command execution. If confirmed malicious, this behavior could lead to unauthorized code execution, privilege escalation, or persistence within the environment.

Search

1
2| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_cmd` AND Processes.process="* /c*" by Processes.dest Processes.user Processes.parent_process_name Processes.parent_process Processes.process_name Processes.original_file_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| `cmd_carry_out_string_command_parameter_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

False positives may be high based on legitimate scripted code in any environment. Filter as needed.

Associated Analytic Story

• AsyncRAT

• Azorult

• CISA AA23-347A

• Chaos Ransomware

• DarkCrystal RAT

• DarkGate Malware

• Data Destruction

• Hermetic Wiper

• IcedID

• Living Off The Land

• Log4Shell CVE-2021-44228

• NjRAT

• PlugX

• ProxyNotShell

• Qakbot

• RedLine Stealer

• Rhysida Ransomware

• Warzone RAT

• WhisperGate

• Winter Vivern

Risk Based Analytics (RBA)

References

• https://thedfirreport.com/2021/10/18/icedid-to-xinglocker-ransomware-in-24-hours/

• https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/

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: 5