SkillFence
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%.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 29, 2022
- Source ID
- 10.1145/3517232
Entities
People
- Ashish Hooda
- Earlence Fernandes
- Kassem Fawaz
- Kushal Jhunjhunwalla
- Matthew Wallace
Organizations
- Defense Advanced Research Projects Agency
- National Science Foundation
- University of Washington
- University of Wisconsin–Madison