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SAIL: Analyzing Structural Artifacts of Logic Locking Using Machine Learning

Obfuscation or Logic locking (LL) is a technique for protecting hardware intellectual property (IP) blocks against diverse threats, including IP theft, reverse engineering, and malicious modifications. State-of-the-art locking techniques primarily focus on securing a design from unauthorized usage by disabling correct functionality – they often do not directly address hiding design intent through structural transformations. They rely on the synthesis tool to introduce structural changes. We observe that this process is insufficient as the resulting changes in circuit topology are: (1) local and (2) predictable. In this paper, we analyze the structural transformations introduced by LL and introduce a potential attack, called SAIL, that can exploit structural artifacts introduced by LL. SAIL uses machine learning (ML) guided structural recovery that exposes a critical vulnerability in these techniques. Through this attack, we demonstrate that the gate-level structure of a locked design can be retrieved in most parts through a systematic set of steps. The proposed attack is applicable to most forms of logic locking, and significantly more powerful than existing attacks, e.g., SAT-based attacks, since it does not require the availability of golden functional responses (e.g., an unlocked IC). Evaluation on benchmark circuits shows that we can recover an average of about 92%, up to 97%, transformations (Top-10 R-Metric) introduced by logic locking. We show that this attack is scalable, flexible, and versatile. Additionally, to evaluate the SAIL attack resilience of a locked design, we present the SIVA-Metric that is fast in terms of computation speed and does not require any training. We also propose possible mitigation steps for incorporating SAIL resilience into a locked design.