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AirKey introduces a cross-modal sensing framework that enables zero-training, contactless PIN inference through WiFi and acoustic signals, addressing critical deployment and waveform ambiguity challenges. By leveraging IEEE 802.11 mechanisms to elicit ACK responses from devices and utilizing acoustic signals to enhance temporal resolution, AirKey achieves a significant improvement in accuracy over existing methods. Real-world evaluations show that it can recover device-unlock PINs with over 4x higher accuracy than current unimodal approaches, highlighting a serious privacy risk in smart interfaces.
AirKey can recover device-unlock PINs with over 4x the accuracy of existing methods, exposing a critical vulnerability in smart device privacy.
Contactless keystroke inference via WiFi sensing highlights severe privacy threats, yet its real-world feasibility is hindered by two fundamental physical and deployment bottlenecks: the strict requirement for network privileges to acquire stable sensing streams, and the inherent"waveform fusion"ambiguity of pure WiFi signals during rapid, muscle-memory typing. To overcome these limitations, we propose AirKey, a novel cross-modal sensing framework that achieves highly stealthy, zero-training PIN eavesdropping. First, to bypass network deployment barriers, AirKey exploits fundamental IEEE 802.11 mechanisms to predictably elicit Acknowledgment (ACK) responses from unmodified target devices. By passively harvesting Channel State Information (CSI) from these ACKs using a low-cost microcontroller, AirKey secures a continuous spatial sensing stream entirely without network association. Crucially, to resolve the WiFi waveform fusion bottleneck, AirKey introduces a cross-modal complementarity mechanism. By utilizing lightweight acoustic signals as precise temporal anchors, the system robustly guides the segmentation of overlapping CSI trajectories. This joint spatiotemporal fusion strictly intersects CSI-derived spatial similarities with acoustic-guided inter-keystroke timing. Extensive real-world evaluations demonstrate that AirKey achieves over 4x higher accuracy than state-of-the-art unimodal zero-training schemes, successfully recovering device-unlock PINs within 6 attempts. Ultimately, this work exposes a critical vulnerability in contemporary smart interfaces, underscoring the severe privacy implications of ubiquitous multimodal sensing.