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Description

The following analytic detects potential unauthorized modifications to Linux cronjobs using text editors like "nano", "vi" or "vim". It identifies this behavior by tracking command-line executions that interact with paths related to cronjob configuration, a common Linux scheduling utility. Cronjob files may be manipulated by attackers for privilege escalation or persistent access, making such changes critical to monitor.\ The identified behavior is significant for a Security Operations Center (SOC) as it could indicate an ongoing attempt at establishing persistent access or privilege escalation, leading to data breaches, system compromise, or other malicious activities.
In case of a true positive, the impact could be severe. An attacker with escalated privileges or persistent access could carry out damaging actions, such as data theft, sabotage, or further network penetration.
To implement this analytic, ensure ingestion of logs tracking process name, parent process, and command-line executions from your endpoints. Utilize the Add-on for Linux Sysmon from Splunkbase if you're using Sysmon.
Known false positives include legitimate administrative tasks, as these commands may also be used for benign purposes. Careful tuning and filtering based on known benign activity in your environment can minimize these instances.

  • Type: Hunting
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2021-12-17
  • Author: Teoderick Contreras, Splunk
  • ID: dcc89bde-5f24-11ec-87ca-acde48001122

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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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name IN("nano","vim.basic") OR Processes.process IN ("*nano *", "*vi *", "*vim *")) 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 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `linux_possible_cronjob_modification_with_editor_filter`

Macros

The SPL above uses the following Macros:

:information_source: linux_possible_cronjob_modification_with_editor_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
  • Processes.dest
  • Processes.user
  • Processes.parent_process_name
  • Processes.process_name
  • Processes.process
  • Processes.process_id
  • Processes.parent_process_id

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

Administrator or network operator can use this commandline for automation purposes. Please update the filter macros to remove false positives.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
6.0 20 30 A commandline $process$ that may modify cronjob file using editor in $dest$

: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

source | version: 1