AI & LLM Vulnerabilities

Retrieval-based-Voice-Conversion-WebUI (a framework for changing voices using AI) in version 2.2.231006 and earlier has a critical vulnerability where user input is passed unsafely to a function that loads model files using torch.load (a Python tool that can execute code from files). An attacker could exploit this by providing a malicious model file path, leading to RCE (remote code execution, where an attacker can run commands on the system).

Retrieval-based-Voice-Conversion-WebUI, a voice changing framework, has a vulnerability in versions 2.2.231006 and earlier where user input (like a file path) is passed directly to torch.load (a function that reads model files). This unsafe deserialization (loading untrusted data that could contain malicious code) allows attackers to execute arbitrary commands on the system running the software.

Retrieval-based-Voice-Conversion-WebUI is a voice changing tool that has a security flaw in versions 2.2.231006 and earlier. The vulnerability allows unsafe deserialization (loading untrusted data that could contain malicious code) when the program takes user input for a model file path and loads it using torch.load, which could let attackers run arbitrary code on the system.

Retrieval-based-Voice-Conversion-WebUI, a voice changing tool, has a vulnerability in versions 2.2.231006 and earlier where unsafe deserialization (loading data in a way that can execute malicious code) allows attackers to run code remotely. The problem occurs because the software takes user input for model file paths and loads them using torch.load without proper safety checks, enabling RCE (remote code execution, where attackers can run commands on the affected system).

Retrieval-based-Voice-Conversion-WebUI, a voice-changing tool, has a vulnerability in versions 2.2.231006 and earlier where user input for model file paths is passed unsafely to torch.load (a function that loads saved AI models). This unsafe deserialization (loading data from untrusted sources without checking it first) can allow attackers to run arbitrary code on the system.

Retrieval-based-Voice-Conversion-WebUI, a voice-changing framework, has a critical vulnerability in versions 2.2.231006 and earlier where unsafe deserialization (loading data from untrusted sources without checking it first) can occur. An attacker can exploit this by providing a malicious file path that gets loaded using torch.load, which can lead to RCE (remote code execution, where an attacker runs commands on a system they don't own).

Retrieval-based-Voice-Conversion-WebUI, a voice changing tool based on VITS (a voice synthesis model), has a vulnerability in versions 2.2.231006 and earlier where user-supplied file paths are loaded directly using torch.load (a function that can execute code when loading files), allowing attackers to run arbitrary code on the system. This happens because the ckpt_path1 variable accepts untrusted input and passes it unsafely to a model-loading function.

vLLM (a system for running AI models efficiently) versions 0.6.5 through 0.8.4 have a critical vulnerability when using mooncake integration. Attackers can execute arbitrary code remotely because the system uses pickle (an unsafe method for converting data into a format that can be transmitted) over unencrypted ZeroMQ sockets (communication channels) that listen to all network connections, making them easily accessible from the internet.

PyTorch (a Python package for machine learning computations) versions 2.5.1 and earlier contain a remote code execution (RCE, where an attacker can run commands on a system they don't own) vulnerability when loading models with the torch.load function set to weights_only=True. The vulnerability stems from insecure deserialization (converting data back into executable code without checking if it's safe), which allows attackers to execute arbitrary commands remotely.