Keystroke Inference

Keystroke inference attacks aim to reconstruct typed information from indirect signals, such as sounds, movements, or even gaze patterns, posing a significant threat to digital security. Current research explores diverse attack vectors, employing machine learning models like neural networks and large language models to analyze data from various sources including acoustic signals, accelerometer data from wearables, and even gaze tracking in VR/MR devices. These advancements highlight the vulnerability of various input methods and underscore the need for robust security measures to protect sensitive information against increasingly sophisticated attacks.