Exploiting Voice-Controlled Devices to Infer the Layouts of Private Spaces

Voice-controlled interactions have recently experienced a surge, fundamentally reshaping how people engage with the digital domain. These interactions are made possible through the integration of voice assistants within devices equipped with microphones and speakers, such as Amazon Echo, Google Nest, etc. Nevertheless, the widespread adoption of these devices in security-sensitive contexts has raised a need to explore their potential security risks. This paper unveils an attack wherein a malicious entity can construct the layout of a user's residence by localizing ambient sound sources, exploiting the user-device communication. To achieve this, we segregated background sound sources like doors, windows, ovens, etc. We then built a classification model to identify these objects with an average accuracy of 91% and a 3D localization mechanism that attained a Relative Distance Error of 15% (85% accuracy). The paper brings to light a security vulnerability that demands mitigation measures to ensure the safe use of voice-controlled devices.