A Portable and Stealthy Inaudible Voice Attack Based on Acoustic Metamaterials

2025-01-25

AI Summary

This work addresses the security vulnerability of voice assistants (e.g., Siri, Alexa, Google Assistant, Xiaoai Tongxue, and Xiao Yi) by proposing a portable, long-range, inaudible voice attack. Unlike prior approaches relying on bulky speaker arrays, we introduce acoustic metamaterials—first applied to inaudible voice attacks—to design METAATTACK: a lightweight system integrating narrowband ultrasonic modulation, speech command reverse engineering, and waveform-stealth encoding, implemented using off-the-shelf speakers and signal processing hardware. Experiments demonstrate that METAATTACK achieves an average word accuracy of 76% across five major voice assistants at a distance of 8.85 meters. The system exhibits high acoustic stealth, compact portability, and practical deployability, significantly extending both the physical reach and real-world threat scope of ultrasonic voice attacks.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Natural Language Processing: Speech
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We present METAATTACK, the first approach to leverage acoustic metamaterials for inaudible attacks for voice control systems. Compared to the state-of-the-art inaudible attacks requiring complex and large speaker setups, METAATTACK achieves a longer attacking range and higher accuracy using a compact, portable device small enough to be put into a carry bag. These improvements in portability and stealth have led to the practical applicability of inaudible attacks and their adaptation to a wider range of scenarios. We demonstrate how the recent advancement in metamaterials can be utilized to design a voice attack system with carefully selected implementation parameters and commercial off-the-shelf components. We showcase that METAATTACK can be used to launch inaudible attacks for representative voice-controlled personal assistants, including Siri, Alexa, Google Assistant, XiaoAI, and Xiaoyi. The average word accuracy of all assistants is 76%, with a range of 8.85 m.

Problem

• Inaudible Commands
• Voice Recognition Systems
• Stealth Attack

Innovation

• METAATTACK
• silent attack
• voice assistant vulnerability