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Description

The following analytic detects the creation of new user accounts on Linux systems using commands like "useradd" or "adduser." It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries often create new user accounts to establish persistence on compromised hosts. If confirmed malicious, this could allow attackers to maintain access, escalate privileges, and further compromise the system, posing a severe security risk.

  • Type: Anomaly

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

  • Last Updated: 2024-09-04
  • Author: Teoderick Contreras, Splunk
  • ID: aae66dc0-74b4-4807-b480-b35f8027abb4

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1136.001 Local Account Persistence
T1136 Create Account Persistence
Kill Chain Phase
  • Installation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`linux_auditd` `linux_auditd_normalized_proctitle_process`
| rename host as dest 
| where LIKE (process_exec, "%useradd%") OR LIKE (process_exec, "%adduser%") 
| stats count min(_time) as firstTime max(_time) as lastTime by process_exec proctitle dest  
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `linux_auditd_add_user_account_filter`

Macros

The SPL above uses the following Macros:

:information_source: linux_auditd_add_user_account_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
  • proctitle

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

Administrator or network operator can execute this command. Please update the filter macros to remove false positives.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 A [$process_exec$] event occurred on host - [$dest$] to add a user account.

: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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