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Description

The following analytic identifies the use of suspicious command-line parameters associated with Impacket tools, such as wmiexec.py, smbexec.py, dcomexec.py, and atexec.py, which are used for lateral movement and remote code execution. It detects these activities by analyzing process execution logs from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns. This activity is significant because Impacket tools are commonly used by adversaries and Red Teams to move laterally within a network. If confirmed malicious, this could allow attackers to execute commands remotely, potentially leading to further compromise and data exfiltration.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2024-05-30
  • Author: Mauricio Velazco, Splunk
  • ID: 8ce07472-496f-11ec-ab3b-3e22fbd008af

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1021 Remote Services Lateral Movement
T1021.002 SMB/Windows Admin Shares Lateral Movement
T1021.003 Distributed Component Object Model Lateral Movement
T1047 Windows Management Instrumentation Execution
T1543.003 Windows Service Persistence, Privilege Escalation
Kill Chain Phase
  • Exploitation
  • Installation
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=cmd.exe (Processes.process = "*/Q /c * \\\\127.0.0.1\\*$*" AND Processes.process IN ("*2>&1*","*2>&1*")) by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `impacket_lateral_movement_commandline_parameters_filter`

Macros

The SPL above uses the following Macros:

:information_source: impacket_lateral_movement_commandline_parameters_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • 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_path
  • Processes.process_path
  • Processes.parent_process_id

How To Implement

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

Although uncommon, Administrators may leverage Impackets tools to start a process on remote systems for system administration or automation use cases.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
63.0 90 70 Suspicious command line parameters on $dest$ may represent a lateral movement attack with Impackets tools

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

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 | version: 4