Linux Auditd Data Transfer Size Limits Via Split

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Description

The following analytic detects suspicious data transfer activities that involve the use of the split syscall, potentially indicating an attempt to evade detection by breaking large files into smaller parts. Attackers may use this technique to bypass size-based security controls, facilitating the covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the split syscall, this analytic helps identify potential data exfiltration attempts, allowing security teams to intervene and prevent the unauthorized transfer of critical information from the network.

• Type: Anomaly

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

• Last Updated: 2024-09-04
• Author: Teoderick Contreras, Splunk
• ID: 4669561d-3bbd-44e3-857c-0e3c6ef2120c

Annotations

ATT&CK

ATT&CK

ID	Technique	Tactic
T1030	Data Transfer Size Limits	Exfiltration

Kill Chain Phase

• Actions On Objectives

NIST

• DE.AE

CIS20

• CIS 10

CVE

Search

`linux_auditd` `linux_auditd_normalized_execve_process` 
| rename host as dest 
| where LIKE(process_exec, "%split %") AND LIKE(process_exec, "% -b %") 
| stats count min(_time) as firstTime max(_time) as lastTime by argc process_exec dest 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)`
| `linux_auditd_data_transfer_size_limits_via_split_filter`

Macros

The SPL above uses the following Macros:

• linux_auditd
• linux_auditd_normalized_execve_process
• security_content_ctime

linux_auditd_data_transfer_size_limits_via_split_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
• argc
• process_exec

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

• Linux Living Off The Land
• Linux Privilege Escalation
• Linux Persistence Techniques
• Compromised Linux Host

RBA

Risk Score	Impact	Confidence	Message
49.0	70	70	A [$process_exec$] event occurred on host - [$dest$] to split a file.

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

• https://www.splunk.com/en_us/blog/security/deep-dive-on-persistence-privilege-escalation-technique-and-detection-in-linux-platform.html

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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