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arXiv · 2212.08738

SkillFence: A Systems Approach to Practically Mitigating Voice-Based Confusion Attacks

Ashish Hooda·Matthew Wallace·Kushal Jhunjhunwalla·Earlence Fernandes·Kassem Fawaz
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Abstract

Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to voice-based confusion attacks that exploit design issues. We propose a systems-oriented defense against this class of attacks and demonstrate its functionality for Amazon Alexa. We ensure that only the skills a user intends execute in response to voice commands. Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones. For example, the Lyft ride-sharing Alexa skill has an Android app and a website. Our work shows how information from counterpart apps can help reduce dis-ambiguities in the skill invocation process. We build SkilIFence, a browser extension that existing voice assistant users can install to ensure that only legitimate skills run in response to their commands. Using real user data from MTurk (N = 116) and experimental trials involving synthetic and organic speech, we show that SkillFence provides a balance between usability and security by securing 90.83% of skills that a user will need with a False acceptance rate of 19.83%.

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Ashish Hooda, Matthew Wallace, Kushal Jhunjhunwalla, Earlence Fernandes, Kassem Fawaz. 2022-12-16. SkillFence: A Systems Approach to Practically Mitigating Voice-Based Confusion Attacks. https://doi.org/10.1145/3517232

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