Detection: Linux Auditd Preload Hijack Library Calls

Updated Date: 2024-09-30 ID: 35c50572-a70b-452f-afa9-bebdf3c3ce36 Author: Teoderick Contreras, Splunk Type: TTP Product: Splunk Enterprise Security

Description

The following analytic detects the use of the LD_PRELOAD environment variable to hijack or hook library functions on a Linux platform. It leverages data from Linux Auditd, focusing on process execution logs that include command-line details. This activity is significant because adversaries, malware authors, and red teamers commonly use this technique to gain elevated privileges and establish persistence on a compromised machine. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, and maintain long-term access to the system.

1`linux_auditd` `linux_auditd_normalized_execve_process` 
2| rename host as dest 
3| where LIKE (process_exec, "%LD_PRELOAD%")
4| stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest 
5| `security_content_ctime(firstTime)`
6| `security_content_ctime(lastTime)`
7| `linux_auditd_preload_hijack_library_calls_filter`

Data Source

Name Platform Sourcetype Source
Linux Auditd Execve Linux icon Linux 'linux:audit' '/var/log/audit/audit.log'

Macros Used

Name Value
linux_auditd sourcetype="linux:audit"
linux_auditd_preload_hijack_library_calls_filter search *
linux_auditd_preload_hijack_library_calls_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1574.006 Dynamic Linker Hijacking Defense Evasion
T1574 Hijack Execution Flow Persistence
KillChainPhase.EXPLOITAITON
KillChainPhase.INSTALLATION
NistCategory.DE_CM
Cis18Value.CIS_10
APT41
Aquatic Panda
Rocke

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
A [$process_exec$] event occurred on host - [$dest$] to hijack or hook library functions using the LD_PRELOAD environment variable. 81 90 90
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation ✅ Passing N/A N/A N/A
Unit ✅ Passing Dataset /var/log/audit/audit.log linux:audit
Integration ✅ Passing Dataset /var/log/audit/audit.log linux:audit

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: GitHub | Version: 2