Windows PaperCut NG Spawn Shell
The following analytic is designed to detect instances where the PaperCut NG application (pc-app.exe) spawns a Windows shell, specifically cmd.exe or PowerShell. This behavior may indicate potential malicious activity, such as an attacker attempting to gain unauthorized access or execute harmful commands on the affected system.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2023-05-15
- Author: Michael Haag, Splunk
- ID: a602d9a2-aaea-45f8-bf0f-d851168d61ca
Kill Chain Phase
- CIS 10
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.parent_process_name=pc-app.exe `process_cmd` OR `process_powershell` OR Processes.process_name=java.exe by Processes.dest Processes.user Processes.parent_process_name 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)` | `windows_papercut_ng_spawn_shell_filter`
The SPL above uses the following Macros:
windows_papercut_ng_spawn_shell_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
False positives may be present, but most likely not. Filter as needed.
Associated Analytic Story
|90.0||100||90||The PaperCut NG application has spawned a shell $process_name$ on endpoint $dest$ by $user$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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: 1