BarrierBypass: Out-of-Sight Clean Voice Command Injection Attacks through Physical Barriers

Payton Walker, Tianfang Zhang, Cong Shi, Nitesh Saxena, Yingying Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The growing adoption of voice-enabled devices (e.g., smart speakers), particularly in smart home environments, has introduced many security vulnerabilities that pose significant threats to users' privacy and safety. When multiple devices are connected to a voice assistant, an attacker can cause serious damage if they can gain control of these devices. We ask where and how can an attacker issue clean voice commands stealthily across a physical barrier, and perform the first academic measurement study of this nature on the command injection attack. We present the BarrierBypass attack that can be launched against three different barrier-based scenarios termed across-door, across-window, and across-wall. We conduct a broad set of experiments to observe the command injection attack success rates for multiple speaker samples (TTS and live human recorded) at different command audio volumes (65, 75, 85 dB), and smart speaker locations (0.1-4.0m from barrier). Against Amazon Echo Dot 2, BarrierBypass is able to achieve 100% wake word and command injection success for the across-wall and across-window attacks, and for the across-door attack (up to 2 meters). At 4 meters for the across-door attack, BarrierBypass can achieve 90% and 80% injection accuracy for the wake word and command, respectively. Against Google Home mini BarrierBypass is able to achieve 100% wake word injection accuracy for all attack scenarios. For command injection BarrierBypass can achieve 100% accuracy for all the three barrier settings (up to 2 meters). For the across-door attack at 4 meters, BarrierBypass can achieve 80% command injection accuracy. Further, our demonstration using drones yielded high command injection success, up to 100%. Overall, our results demonstrate the potentially devastating nature of this vulnerability to control a user's device from outside of the device's physical space, and its limitations, without the need for complex and error-prone command injection.

Original languageEnglish (US)
Title of host publicationWiSec 2023 - Proceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks
PublisherAssociation for Computing Machinery, Inc
Pages203-214
Number of pages12
ISBN (Electronic)9781450398596
DOIs
StatePublished - May 29 2023
Externally publishedYes
Event16th ACM Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2023 - Guildford, United Kingdom
Duration: May 29 2023Jun 1 2023

Publication series

NameWiSec 2023 - Proceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks

Conference

Conference16th ACM Conference on Security and Privacy in Wireless and Mobile Networks, WiSec 2023
Country/TerritoryUnited Kingdom
CityGuildford
Period5/29/236/1/23

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems
  • Software
  • Safety Research
  • Computer Networks and Communications

Keywords

  • command injection attack
  • iot
  • physical barrier
  • speech recognition

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