This analytic identifies a common behavior by Cobalt Strike and other frameworks where the adversary will escalate privileges, either via
jump (Cobalt Strike PTH) or
getsystem, using named-pipe impersonation. A suspicious event will look like
cmd.exe /c echo 4sgryt3436 > \\.\Pipe\5erg53.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-05-20
- Author: Michael Haag, Splunk
- ID: eb277ba0-b96b-11eb-b00e-acde48001122
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 `process_cmd` OR Processes.process=*%comspec%* (Processes.process=*echo* AND Processes.process=*pipe*) by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.original_file_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `cmd_echo_pipe___escalation_filter`
The SPL above uses the following Macros:
cmd_echo_pipe_-_escalation_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 information on process that include the name of the process responsible for the changes from your endpoints into the
Endpoint datamodel in the
Processes node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
Known False Positives
Unknown. It is possible filtering may be required to ensure fidelity.
Associated Analytic Story
|64.0||80||80||An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ by user $user$ potentially performing privilege escalation using named pipes related to Cobalt Strike and other frameworks.|
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