The following analytic is designed to detect potential tampering with cronjob files on a Linux system. It specifically searches for command lines that may be used to append code to existing cronjob files, a technique often employed by adversaries, malware, and red teamers for persistence or privilege escalation. Altering existing or sometimes normal cronjob script files allows malicious code to be executed automatically.
The analytic operates by monitoring logs for specific process names, parent processes, and command-line executions from your endpoints. It specifically checks for any 'echo' command which modifies files in directories commonly associated with cron jobs such as '/etc/cron*', '/var/spool/cron/', and '/etc/anacrontab'. If such activity is detected, an alert is triggered.
This behavior is worth identifying for a SOC because malicious cron jobs can lead to system compromises and unauthorized data access, impacting business operations and data integrity.
- Type: Hunting
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-12-17
- Author: Teoderick Contreras, Splunk
- ID: b5b91200-5f27-11ec-bb4e-acde48001122
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 | tstats `security_content_summariesonly` count from datamodel=Endpoint.Processes where Processes.process = "*echo*" AND Processes.process IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*") 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_possible_append_cronjob_entry_on_existing_cronjob_file_filter`
The SPL above uses the following Macros:
linux_possible_append_cronjob_entry_on_existing_cronjob_file_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 may arise from legitimate actions by administrators or network operators who may use these commands for automation purposes. Therefore, it's recommended to adjust filter macros to eliminate such false positives.
Associated Analytic Story
|49.0||70||70||A commandline $process$ that may modify cronjob file 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.
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