ID | Technique | Tactic |
---|---|---|
T1574.006 | Dynamic Linker Hijacking | Defense Evasion |
T1574 | Hijack Execution Flow | Persistence |
Detection: Linux Auditd Preload Hijack Via Preload File
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.
Search
1`linux_auditd` type=PATH name="/etc/ld.so.preload*"
2| rename host as dest
3| stats count min(_time) as firstTime max(_time) as lastTime by name nametype OGID type dest
4| `security_content_ctime(firstTime)`
5| `security_content_ctime(lastTime)`
6| `linux_auditd_preload_hijack_via_preload_file_filter`
Data Source
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
Linux Auditd Path | Linux | 'linux:audit' |
'/var/log/audit/audit.log' |
N/A |
Macros Used
Name | Value |
---|---|
linux_auditd | sourcetype="linux:audit" |
linux_auditd_preload_hijack_via_preload_file_filter | search * |
linux_auditd_preload_hijack_via_preload_file_filter
is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
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 |
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 use this application for automation purposes. Please update the filter macros to remove false positives.
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
A [$type$] event has occured on host - [$dest$] to modify the preload file. | 81 | 90 | 90 |
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: 1