ThermoSecure: Investigating the Effectiveness of AI-Driven Thermal Attacks on Commonly Used Computer Keyboards

Abstract

Thermal cameras can reveal heat traces on user interfaces, such as keyboards. This can be exploited maliciously to infer sensitive input, such as passwords. While previous work considered thermal attacks that rely on visual inspection of simple image processing techniques, we show that attackers can perform more effective artificial intelligence (AI)–driven attacks. We demonstrate this by presenting the development of ThermoSecure and its evaluation in two user studies (N = 21, N = 16), which reveal novel insights about thermal attacks. We detail the implementation of ThermoSecure and make a dataset of 1,500 thermal images of keyboards with heat traces resulting from input publicly available. Our first study shows that ThermoSecure successfully attacks 6-symbol, 8-symbol, 12-symbol, and 16-symbol passwords with an average accuracy of 92%, 80%, 71%, and 55% respectively, and even higher accuracy when thermal images are taken within 30 seconds. We found that typing behavior significantly impacts vulnerability to thermal attacks: hunt-and-peck typists are more vulnerable than fast typists (92% vs. 83% thermal attack success. respectively, if performed within 30 seconds). The second study showed that keycap material has a statistically significant effect on the effectiveness of thermal attacks: ABS keycaps retain the thermal trace of user presses for a longer period of time, making them more vulnerable to thermal attacks, with a 52% average attack accuracy compared with 14% for keyboards with PBT keycaps. Finally, we discuss how systems can leverage our results to protect from thermal attacks and present 7 mitigation approaches that are based on our results and previous work.