Detection: Linux Preload Hijack Library Calls

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 Endpoint Detection and Response (EDR) agents, 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.

Search

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process = "*LD_PRELOAD*" by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id Processes.process_guid 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `linux_preload_hijack_library_calls_filter`

Data Source

Name	Platform	Sourcetype	Source
Sysmon for Linux EventID 1	Linux	'sysmon:linux'	'Syslog:Linux-Sysmon/Operational'

Macros Used

Annotations

Default Configuration

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

Implementation

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 execute this command. Please update the filter macros to remove false positives.

Associated Analytic Story

• Linux Persistence Techniques

• Linux Privilege Escalation

Risk Based Analytics (RBA)

References

• https://compilepeace.medium.com/memory-malware-part-0x2-writing-userland-rootkits-via-ld-preload-30121c8343d5

Detection Testing

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: 3