This analytic looks for the presence of suspicious commandline parameters typically present when using Impacket tools. Impacket is a collection of python classes meant to be used with Microsoft network protocols. There are multiple scripts that leverage impacket libraries like
atexec.py used to execute commands on remote endpoints. By default, these scripts leverage administrative shares and hardcoded parameters that can be used as a signature to detect its use. Red Teams and adversaries alike may leverage Impackets tools for lateral movement and remote code execution.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-01-18
- Author: Mauricio Velazco, Splunk
- ID: 8ce07472-496f-11ec-ab3b-3e22fbd008af
Kill Chain Phase
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process = "*/c* \\\\127.0.0.1\\*" OR Processes.process= "*/c* 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`
The SPL above uses the following Macros:
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.
List of fields required to use this analytic.
How To Implement
To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints.
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
|63.0||90||70||Suspicious command line parameters on $dest may represent a lateral movement attack with Impackets tools|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 2