Linux Octave Privilege Escalation
GNU Octave is a high-level programming language primarily intended for scientific computing and numerical computation. Octave helps in solving linear and nonlinear problems numerically, and for performing other numerical experiments using a language that is mostly compatible with MATLAB. If sudo right is given to the application for the user, then the user can run system commands as root and possibly get a root shell.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-08-11
- Author: Gowthamaraj Rajendran, Splunk
- ID: 78f7487d-42ce-4f7f-8685-2159b25fb477
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.process="*octave-cli*" AND Processes.process="*--eval*" AND Processes.process="*system*" AND Processes.process="*sudo*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `linux_octave_privilege_escalation_filter`
The SPL above uses the following Macros:
linux_octave_privilege_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 logs with the process name, parent process, and command-line executions from your endpoints into the Endpoint datamodel. If you are using Sysmon, you can use the Add-on for Linux Sysmon from Splunkbase.
Known False Positives
False positives may be present, filter as needed.
Associated Analytic Story
|20.0||40||50||An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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
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