ExploreFault: Identifying Exploitable Fault Models in Block Ciphers with Reinforcement Learning

Exploitable fault models for block ciphers are typically cipher-specific, and their identification is essential for evaluating and certifying fault attack-protected implementations. However, identifying exploitable fault models has been a complex manual process. In this work, we utilize reinforcement learning (RL) to identify exploitable fault models generically and automatically. In contrast to the several weeks/months of tedious analyses required from experts, our RL-based approach identifies exploitable fault models for protected/unprotected AES and GIFT ciphers within 12 hours. Notably, in addition to all existing fault models, we identify/discover a novel fault model for GIFT, illustrating the power and promise of our approach in exploring new attack avenues.