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The Insecurity of Home Digital Voice Assistants -- Amazon Alexa as a Case Study

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abstract

Home Digital Voice Assistants (HDVAs) are getting popular in recent years. Users can control smart devices and get living assistance through those HDVAs (e.g., Amazon Alexa, Google Home) using voice. In this work, we study the insecurity of HDVA service by using Amazon Alexa as a case study. We disclose three security vulnerabilities which root in the insecure access control of Alexa services. We then exploit them to devise two proof-of-concept attacks, home burglary and fake order, where the adversary can remotely command the victim's Alexa device to open a door or place an order from Amazon.com. The insecure access control is that the Alexa device not only relies on a single-factor authentication but also takes voice commands even if no people are around. We thus argue that HDVAs should have another authentication factor, a physical presence based access control; that is, they can accept voice commands only when any person is detected nearby. To this end, we devise a Virtual Security Button (VSButton), which leverages the WiFi technology to detect indoor human motions. Once any indoor human motion is detected, the Alexa device is enabled to accept voice commands. Our evaluation results show that it can effectively differentiate indoor motions from the cases of no motion and outdoor motions in both the laboratory and real world settings.

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cs.LG 1

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2025 1

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representative citing papers

ML Mule: Mobile-Driven Context-Aware Collaborative Learning

cs.LG · 2025-01-13 · conditional · novelty 6.0

Using mobile devices as physical couriers of model snapshots between fixed devices in different spaces outperforms federated, decentralized, and local-only learning in simulated and prototype settings.

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  • ML Mule: Mobile-Driven Context-Aware Collaborative Learning cs.LG · 2025-01-13 · conditional · none · ref 1 · internal anchor

    Using mobile devices as physical couriers of model snapshots between fixed devices in different spaces outperforms federated, decentralized, and local-only learning in simulated and prototype settings.