Detection: Linux Auditd 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 Linux Auditd, 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`linux_auditd` type=PATH name IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*") 
2| rename host as dest 
3| stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID  dest 
4| `security_content_ctime(firstTime)`
5| `security_content_ctime(lastTime)`
6| `linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file_filter`

Data Source

Name	Platform	Sourcetype	Source
Linux Auditd Path	Linux	'linux:audit'	'/var/log/audit/audit.log'

Macros Used

Annotations

Default Configuration

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

Implementation

To implement this detection, the process begins by ingesting auditd data, that consist SYSCALL, TYPE, EXECVE and PROCTITLE events, which captures command-line executions and process details on Unix/Linux systems. These logs should be ingested and processed using Splunk Add-on for Unix and Linux (https://splunkbase.splunk.com/app/833), which is essential for correctly parsing and categorizing the data. The next step involves normalizing the field names to match the field names set by the Splunk Common Information Model (CIM) to ensure consistency across different data sources and enhance the efficiency of data modeling. This approach enables effective monitoring and detection of linux endpoints where auditd is deployed

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

• Compromised Linux Host

• 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: 2