Manipulating Voice Assistants Eavesdropping via Inherent Vulnerability Unveiling in Mobile Systems

Numerous mobile devices are equipped with voice assistants to facilitate contactless user-device interaction. However, the widespread availability of voice assistants also raises security and privacy concerns, as they can be maliciously triggered to perform voice eavesdropping. Although diverse attacks have been taken to manipulate voice assistants for eavesdropping, they exhibit deficiencies of limited attack scopes and conspicuous attack behaviors because they target specific voice assistants or require extra voice commands to activate them. To manipulate arbitrary voice assistants for covert eavesdropping attack, we conduct a comprehensive analysis of voice assistant implementation in the Android system and refine a universal workflow. Through meticulous analysis and experimental verification, we uncover an inherent vulnerability that in voice assistants across device types that can be awakened by an artificial faking Intent. Building on this significant discovery, we propose an attack termed
VoiceEar
. It leverages a malicious event generation file and a first-in-first-out Intent generation algorithm to trigger voice assistants within the normal workflow for eavesdropping, without voice commands. Finally, we deploy the
VoiceEar
attacks on 25 mainstream mobile devices, and invite 95 volunteers for eavesdropping activity perception testing. The results unequivocally demonstrate the seamless execution of
VoiceEar
attacks, with neither users nor devices awareness.