CVE-2025-43844 in Retrieval-based-Voice-Conversion-WebUI
Summary
by MITRE • 05/05/2025
Retrieval-based-Voice-Conversion-WebUI is a voice changing framework based on VITS. Versions 2.2.231006 and prior are vulnerable to command injection. The variables exp_dir1, among others, take user input and pass it to the click_train function, which concatenates them into a command that is run on the server. This can lead to arbitrary command execution. As of time of publication, no known patches exist.
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Analysis
by VulDB Data Team • 08/01/2025
The vulnerability identified as CVE-2025-43844 affects the Retrieval-based-Voice-Conversion-WebUI framework, a voice changing platform built upon the VITS architecture. This system serves as a web-based interface for voice conversion tasks and has been widely adopted for its accessibility and ease of use in audio processing applications. The framework's design includes a training module that accepts user inputs through various parameters, creating a potential attack surface that has been exploited through command injection flaws.
The technical flaw manifests in the improper handling of user-supplied variables within the click_train function, specifically with the exp_dir1 parameter and similar variables. These inputs are directly concatenated into system commands without adequate sanitization or validation, creating a classic command injection vulnerability. The vulnerability stems from the framework's failure to implement proper input validation and command construction practices, allowing malicious actors to inject arbitrary commands that execute with the privileges of the web server process. This design flaw aligns with CWE-78, which specifically addresses improper neutralization of special elements used in OS commands.
The operational impact of this vulnerability is severe and far-reaching, as it enables remote attackers to achieve arbitrary code execution on the affected system. An attacker could potentially gain complete control over the voice conversion server, allowing them to execute malicious commands, access sensitive data, modify system configurations, or even establish persistent backdoors. The vulnerability affects all versions up to and including 2.2.231006, representing a significant security risk for organizations relying on this framework for voice processing tasks. Given the absence of known patches at the time of publication, affected systems remain highly vulnerable to exploitation.
Mitigation strategies for this vulnerability should focus on immediate defensive measures while awaiting official patches. Organizations should implement network-level restrictions to limit access to the voice conversion web interface, particularly restricting access to trusted IP addresses only. Input validation should be strengthened by implementing strict sanitization of all user-supplied parameters before they are processed, ensuring that special characters and command sequences are properly escaped or filtered. The framework should be updated to use parameterized command execution rather than direct string concatenation, following secure coding practices. Additionally, system monitoring should be enhanced to detect unusual command execution patterns that may indicate exploitation attempts. Security teams should consider implementing web application firewalls and intrusion detection systems to provide additional layers of protection. The vulnerability demonstrates the critical importance of secure input handling in web applications and aligns with ATT&CK technique T1059.001 for command and script injection, emphasizing the need for comprehensive security controls in AI and machine learning frameworks that handle user inputs.