The following analytic identifies the use of default or publicly known named pipes used by RMX remote admin tool. A named pipe is a named, one-way or duplex pipe for communication between the pipe server and one or more pipe clients. RMX Tool uses named pipes in many way as part of its communication for its server and client component. This tool was abuse by several adversaries and malware like Azorult to collect data to the targeted host. This TTP is a good indicator that this tool was install in production premise and need to check if the user has a valid reason why it need to install this legitimate application.
- Type: TTP
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2022-06-24
- Author: Teoderick Contreras, Splunk
- ID: b62a6040-49f4-47c8-b3f6-fc1adb952a33
Kill Chain Phase
- Command and Control
- CIS 10
1 2 3 4 5 `sysmon` EventCode IN (17, 18) EventType IN ( "CreatePipe", "ConnectPipe") PipeName IN ("\\RManFUSServerNotify32", "\\RManFUSCallbackNotify32", "\\RMSPrint*") | stats min(_time) as firstTime max(_time) as lastTime count by Image EventType ProcessId PipeName Computer UserID | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_application_layer_protocol_rms_radmin_tool_namedpipe_filter`
The SPL above uses the following Macros:
windows_application_layer_protocol_rms_radmin_tool_namedpipe_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. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
Known False Positives
False positives may be present. Filter based on pipe name or process.
Associated Analytic Story
|81.0||90||90||possible RMS admin tool named pipe was created in $dest$|
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: 1