Detection: Linux Auditd Clipboard Data Copy

Updated Date: 2026-05-13 ID: 9ddfe470-c4d0-4e60-8668-7337bd699edd Author: Teoderick Contreras, Splunk Type: Anomaly Product: Splunk Enterprise Security

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.

Search

 1`linux_auditd` execve_command IN ("*xclip*", "*clipboard*") AND execve_command IN ("*-o*", "*-selection *", "*-sel *" )
 2  
 3| rename host as dest
 4  
 5| rename comm as process_name
 6  
 7| rename exe as process
 8  
 9| stats count min(_time) as firstTime max(_time) as lastTime
10    BY argc execve_command dest
11  
12| `security_content_ctime(firstTime)`
13  
14| `security_content_ctime(lastTime)`
15  
16| `linux_auditd_clipboard_data_copy_filter`

Data Source

Name	Platform	Sourcetype	Source
Linux Auditd Execve	Linux	`'auditd'`	`'auditd'`

Macros Used

Name	Value
security_content_ctime	`convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)`
linux_auditd_clipboard_data_copy_filter	`search *`

linux_auditd_clipboard_data_copy_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
T1115	Clipboard Data	Collection

Exploitation

DE.AE

CIS 10

APT39

Kimsuky

OilRig

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 Finding (Notable)	No
Creates Intermediate Finding (Risk Event)	Yes

Anomaly detections generate Intermediate Findings (Risk Events). They do not generate a Finding (Notable) directly.

Implementation

To implement this detection, the process begins by ingesting auditd data, that consists of 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

Intermediate Findings

Message	Entity Field	Entity Type	Risk Score
A [$execve_command$] event occurred on host - [$dest$] to copy data from the clipboard.	dest	system	20

References

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	✅ Passing	N/A	N/A	N/A
Unit	✅ Passing	Dataset	`auditd`	`auditd`
Integration	✅ Passing	Dataset	`auditd`	`auditd`

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: 9