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A Practical Survey on Emerging Threats from AI-driven Voice Attacks: How Vulnerable are Commercial Voice Control Systems?

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arxiv 2312.06010 v2 pith:2SUNQFWD submitted 2023-12-10 cs.CR cs.SDeess.AS

classification cs.CRcs.SDeess.AS
keywords voiceattackscontrolsystemscommercialattackaudiodefense
verification ladder T0 review T1 audit T2 compute T3 formal
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The emergence of Artificial Intelligence (AI)-driven audio attacks has revealed new security vulnerabilities in voice control systems. While researchers have introduced a multitude of attack strategies targeting voice control systems (VCS), the continual advancements of VCS have diminished the impact of many such attacks. Recognizing this dynamic landscape, our study endeavors to comprehensively assess the resilience of commercial voice control systems against a spectrum of malicious audio attacks. Through extensive experimentation, we evaluate six prominent attack techniques across a collection of voice control interfaces and devices. Contrary to prevailing narratives, our results suggest that commercial voice control systems exhibit enhanced resistance to existing threats. Particularly, our research highlights the ineffectiveness of white-box attacks in black-box scenarios. Furthermore, the adversaries encounter substantial obstacles in obtaining precise gradient estimations during query-based interactions with commercial systems, such as Apple Siri and Samsung Bixby. Meanwhile, we find that current defense strategies are not completely immune to advanced attacks. Our findings contribute valuable insights for enhancing defense mechanisms in VCS. Through this survey, we aim to raise awareness within the academic community about the security concerns of VCS and advocate for continued research in this crucial area.

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  1. ASRJam: Human-Friendly AI Speech Jamming to Prevent Automated Phone Scams

    cs.CL 2025-06 reject novelty 6.0 of 10

    EchoGuard adds echo-like acoustic distortions to outgoing speech that confuse scam bots' speech recognition while leaving human callers able to understand.

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