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Description

The following analytic detects the use of the Linux 'xclip' command to copy data from the clipboard. It leverages Linux Auditd telemetry, focusing on process names and command-line arguments related to clipboard operations. This activity is significant because adversaries can exploit clipboard data to capture sensitive information such as passwords or IP addresses. If confirmed malicious, this technique could lead to unauthorized data exfiltration, compromising sensitive information and potentially aiding further attacks within the environment.

  • Type: Anomaly

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

  • Last Updated: 2024-09-04
  • Author: Teoderick Contreras, Splunk
  • ID: 9ddfe470-c4d0-4e60-8668-7337bd699edd

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1115 Clipboard Data Collection
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`linux_auditd` `linux_auditd_normalized_execve_process` 
| rename host as dest 
| where LIKE(process_exec, "%xclip%") AND (LIKE(process_exec, "%clipboard%") OR LIKE(process_exec, "%-o%") OR LIKE(process_exec, "%clip %") OR LIKE(process_exec, "%-selection %") OR LIKE(process_exec, "%sel %")) 
| 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_clipboard_data_copy_filter`

Macros

The SPL above uses the following Macros:

:information_source: linux_auditd_clipboard_data_copy_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

False positives may be present on Linux desktop as it may commonly be used by administrators or end users. Filter as needed.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
16.0 40 40 A [$process_exec$] event occurred on host - [$dest$] to copy data from the clipboard.

: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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