Awk is mostly used for processing and scanning patterns. It checks one or more files to determine whether any lines fit the specified patterns, and if so, it does the appropriate action. If sudo right is given to AWK binary 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: 2022-07-31
- Author: Gowthamaraj Rajendran, Splunk
- ID: 4510cae0-96a2-4840-9919-91d262db210a
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="*sudo*" AND Processes.process="*awk*" AND Processes.process="*BEGIN*system*" 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_awk_privilege_escalation_filter`
The SPL above uses the following Macros:
linux_awk_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
The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the
Processes node of the
Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
Known False Positives
False positives are present based on automated tooling or system administrative usage. Filter as needed.
Associated Analytic Story
|30.0||60||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.
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