GhostTalk: Interactive Attack on Smartphone Voice System Through Power Line

TL;DR

GhostTalk introduces a novel power line side-channel attack that enables inaudible voice command injection and eavesdropping on smartphones, increasing attack resilience and interactivity compared to traditional over-the-air methods.

Contribution

This paper presents GhostTalk, a new attack method using power line signals for inaudible command injection and eavesdropping, with practical implementations and neural network-based audio recognition.

Findings

Attack is effective even in noisy environments.

Power line signals can be exploited to inject and eavesdrop on voice commands.

Neural network accurately classifies spoken digits from power line signals.

Abstract

Inaudible voice command injection is one of the most threatening attacks towards voice assistants. Existing attacks aim at injecting the attack signals over the air, but they require the access to the authorized user's voice for activating the voice assistants. Moreover, the effectiveness of the attacks can be greatly deteriorated in a noisy environment. In this paper, we explore a new type of channel, the power line side-channel, to launch the inaudible voice command injection. By injecting the audio signals over the power line through a modified charging cable, the attack becomes more resilient against various environmental factors and liveness detection models. Meanwhile, the smartphone audio output can be eavesdropped through the modified cable, enabling a highly-interactive attack. To exploit the power line side-channel, we present GhostTalk, a new hidden voice attack that is capable of injecting and eavesdropping simultaneously. Via a quick modification of the power bank cables, the attackers could launch interactive attacks by remotely making a phone call or capturing private information from the voice assistants. GhostTalk overcomes the challenge of bypassing the speaker verification system by stealthily triggering a switch component to simulate the press button on the headphone. In case when the smartphones are charged by an unaltered standard cable, we discover that it is possible to recover the audio signal from smartphone loudspeakers by monitoring the charging current on the power line. To demonstrate the feasibility, we design GhostTalk-SC, an adaptive eavesdropper system targeting smartphones charged in the public USB ports. To correctly recognize the private information in the audio, GhostTalk-SC carefully extracts audio spectra and integrates a neural network model to classify spoken digits in the speech.