This analytic is to detect the doas tool execution in linux host platform. This utility tool allow standard users to perform tasks as root, the same way sudo does. This tool is developed as a minimalistic alternative to sudo application. This tool can be abused advesaries, attacker or malware to gain elevated privileges to the targeted or compromised host. On the other hand this can also be executed by administrator for a certain task that needs admin rights. In this case filter is needed.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2022-01-05
- Author: Teoderick Contreras, Splunk
- ID: d5a62490-6e09-11ec-884e-acde48001122
Kill Chain Phase
- CIS 3
- CIS 5
- CIS 16
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_name = "doas" 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_doas_tool_execution_filter`
The SPL above uses the following Macros:
linux_doas_tool_execution_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 can use the Add-on for Linux Sysmon from Splunkbase.
Known False Positives
Administrator or network operator can execute this command. Please update the filter macros to remove false positives.
Associated Analytic Story
|49.0||70||70||A doas $process_name$ with commandline $process$ was executed on $dest$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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