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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.

  • Type: Hunting

  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2024-09-04
  • Author: Teoderick Contreras, Splunk
  • ID: fea71cf0-fa10-4ef6-9202-9682b2e0c477

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1053.003 Cron Execution, Persistence, Privilege Escalation
T1053 Scheduled Task/Job Execution, Persistence, Privilege Escalation
Kill Chain Phase
  • Installation
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`linux_auditd` type=PATH name IN("*/etc/cron*", "*/var/spool/cron/*", "*/etc/anacrontab*") 
| rename host as dest 
| stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID  dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `linux_auditd_possible_append_cronjob_entry_on_existing_cronjob_file_filter`

Macros

The SPL above uses the following Macros:

:information_source: linux_auditd_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.

Required fields

List of fields required to use this analytic.

  • _time
  • name
  • nametype
  • OGID

How To Implement

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

RBA

Risk Score Impact Confidence Message
49.0 70 70 A [$type$] event has occured on host - [$dest$] to append a cronjob entry on an existing cronjob file.

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

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

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