Incremental Attack Synthesis
Seemanta Saha, William Eiers, İsmet Burak Kadron, Lucas Bang, Tevfik Bultan · ACM SIGSOFT Software Engineering Notes · 2019
Information leakage is a signi cant problem in modern software systems. Information leaks due to side channels are especially hard to detect and analyze. In recent years, techniques have been developed for automated synthesis of adaptive side-channel attacks that recover secret values by iteratively generating inputs to reveal partial information about the secret based on the sidechannel observations. Prominent approaches of attack synthesis use symbolic execution, model counting, and meta-heuristics to maximize information gain. These approaches could bene t by reusing results from prior steps in each step. In this paper, we present an incremental approach to attack synthesis that reuses model counting results from prior iterations in each attack step to improve efficiency. Experimental evaluation demonstrates that our approach drastically improves performance, reducing the attack synthesis time by an order of magnitude.