Detection: Linux Possible Append Cronjob Entry on Existing Cronjob File

Description

The following analytic detects potential tampering with cronjob files on a Linux system by identifying 'echo' commands that append code to existing cronjob files. It leverages logs from Endpoint Detection and Response (EDR) agents, focusing on process names, parent processes, and command-line executions. This activity is significant because adversaries often use it for persistence or privilege escalation. If confirmed malicious, this could allow attackers to execute unauthorized code automatically, leading to system compromises and unauthorized data access, thereby impacting business operations and data integrity.

Search

1
2| 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 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `linux_possible_append_cronjob_entry_on_existing_cronjob_file_filter`

Data Source

Name	Platform	Sourcetype	Source
Sysmon for Linux EventID 1	Linux	'sysmon:linux'	'Syslog:Linux-Sysmon/Operational'

Macros Used

Annotations

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Implementation

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

• Linux Living Off The Land

• Linux Persistence Techniques

• Linux Privilege Escalation

• Scheduled Tasks

Risk Based Analytics (RBA)

References

• https://attack.mitre.org/techniques/T1053/003/

• https://blog.aquasec.com/threat-alert-kinsing-malware-container-vulnerability

• https://www.intezer.com/blog/research/kaiji-new-chinese-linux-malware-turning-to-golang/

Detection Testing

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: GitHub | Version: 3