A Survey on Voice Assistant Security: Attacks and Countermeasures

Author:

Yan Chen1ORCID,Ji Xiaoyu1ORCID,Wang Kai1ORCID,Jiang Qinhong1ORCID,Jin Zizhi1ORCID,Xu Wenyuan1ORCID

Affiliation:

1. Zhejiang University, Hangzhou, Zhejiang, China

Abstract

Voice assistants (VA) have become prevalent on a wide range of personal devices such as smartphones and smart speakers. As companies build voice assistants with extra functionalities, attacks that trick a voice assistant into performing malicious behaviors can pose a significant threat to a user’s security, privacy, and even safety. However, the diverse attacks and stand-alone defenses in the literature often lack a systematic perspective, making it challenging for designers to properly identify, understand, and mitigate the security threats against voice assistants. To overcome this problem, this article provides a thorough survey of the attacks and countermeasures for voice assistants. We systematize a broad category of relevant but seemingly unrelated attacks by the vulnerable system components and attack methods, and categorize existing countermeasures based on the defensive strategies from a system designer’s perspective. To assist designers in planning defense based on their demands, we provide a qualitative comparison of existing countermeasures by the implementation cost, usability, and security and propose practical suggestions. We envision this work can help build more reliability into voice assistants and promote research in this fast-evolving area.

Funder

China NSFC

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference215 articles.

1. Universal adversarial audio perturbations;Abdoli Sajjad;arXiv:1908.03173.,2019

2. Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems

3. Hear “No Evil”, See “Kenansville”: Efficient and transferable black-box attacks on speech recognition and voice identification systems;Abdullah Hadi;arXiv:1910.05262.,2019

4. The faults in our ASRs: An overview of attacks against automatic speech recognition and speaker identification systems;Abdullah Hadi;arXiv:2007.06622.,2020

5. Muhammad Ejaz Ahmed, Il-Youp Kwak, Jun Ho Huh, Iljoo Kim, Taekkyung Oh, and Hyoungshick Kim. 2020. Void: A fast and light voice liveness detection system. In Proceedings of the 29th USENIX Security Symposium. 2685–2702.

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