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Obfuscation is a technique for protecting hardware intellectual property (IP)blocks against reverse engineering, piracy, and malicious modifications.Current obfuscation efforts mainly focus on functional locking of a design toprevent black-box usage. They do not directly address hiding design intentthrough structural transformations, which is an important objective ofobfuscation. We note that current obfuscation techniques incorporate only: (1)local, and (2) predictable changes in circuit topology. In this paper, wepresent SAIL, a structural attack on obfuscation using machine learning (ML)models that exposes a critical vulnerability of these methods. Through thisattack, we demonstrate that the gate-level structure of an obfuscated designcan be retrieved in most parts through a systematic set of steps. The proposedattack is applicable to all forms of logic obfuscation, and significantly morepowerful than existing attacks, e.g., SAT-based attacks, since it does notrequire the availability of golden functional responses (e.g. an unlocked IC).Evaluation on benchmark circuits show that we can recover an average of around84% (up to 95%) transformations introduced by obfuscation. We also show thatthis attack is scalable, flexible, and versatile.

SAIL: Machine Learning Guided Structural Analysis Attack on Hardware Obfuscation