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Description

The following analytic detects the execution of a Unix shell command designed to wipe root directories on a Linux host. It leverages data from Linux Auditd, focusing on the 'rm' command with force recursive deletion and the '–no-preserve-root' option. This activity is significant as it indicates potential data destruction attempts, often associated with malware like Awfulshred. If confirmed malicious, this behavior could lead to severe data loss, system instability, and compromised integrity of the affected Linux host. Immediate investigation and response are crucial to mitigate potential damage.

  • Type: TTP

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

  • Last Updated: 2024-09-04
  • Author: Teoderick Contreras, Splunk
  • ID: 4da5ce1a-f71b-4e71-bb73-c0a3c73f3c3c

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1485 Data Destruction Impact
Kill Chain Phase
  • Actions On Objectives
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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`linux_auditd` `linux_auditd_normalized_execve_process` 
| rename host as dest 
| where LIKE (process_exec, "%rm %") AND LIKE (process_exec, "% -rf %") AND LIKE (process_exec, "%--no-preserve-root%") 
| stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `linux_auditd_data_destruction_command_filter`

Macros

The SPL above uses the following Macros:

:information_source: linux_auditd_data_destruction_command_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
  • argc
  • process_exec

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

unknown

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
90.0 100 90 A [$process_exec$] event occurred on host - [$dest$] to destroy data.

: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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