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Description

The following analytic detects suspicious preload hijacking via the preload file, which may indicate an attacker's attempt to intercept or manipulate library loading processes. The preload file can be used to force the loading of specific libraries before others, potentially allowing malicious code to execute or alter application behavior. By monitoring for unusual or unauthorized modifications to the preload file, this analytic helps identify attempts to hijack preload mechanisms, enabling security teams to investigate and address potential threats to system integrity and security.

  • Type: TTP

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

  • Last Updated: 2024-09-04
  • Author: Teoderick Contreras, Splunk
  • ID: c1b7abca-55cb-4a39-bdfb-e28c1c12745f

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1574.006 Dynamic Linker Hijacking Persistence, Privilege Escalation, Defense Evasion
T1574 Hijack Execution Flow Persistence, Privilege Escalation, Defense Evasion
Kill Chain Phase
  • Installation
  • Exploitation
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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`linux_auditd` type=PATH name="/etc/ld.so.preload*" 
| rename host as dest 
| stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID type dest 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `linux_auditd_preload_hijack_via_preload_file_filter`

Macros

The SPL above uses the following Macros:

:information_source: linux_auditd_preload_hijack_via_preload_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

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

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
81.0 90 90 A [$type$] event has occured on host - [$dest$] to modify the preload 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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