Logic locking has received considerable interest as a prominent technique for protecting the design intellectual property from untrusted entities, especially the foundry. Recently, machine learning (ML)-based attacks have questioned the security guarantees of logic locking, and have demonstrated considerable success in deciphering the secret key without relying on an oracle, hence, proving to be very useful for an adversary in the fab. Such ML-based attacks have triggered the development of learning-resilient locking techniques. The most advanced state-of-the-art deceptive MUX-based locking (D-MUX) and the symmetric MUX-based locking techniques have recently demonstrated resilience against existing ML-based attacks. Both defense techniques obfuscate the design by inserting key-controlled MUX logic, ensuring that all the secret inputs to the MUXes are equiprobable. In this work, we show that these techniques primarily introduce local and limited changes to the circuit without altering the global structure of the design. By leveraging this observation, we propose a novel graph neural network (GNN)-based link prediction attack, MuxLink, that successfully breaks both the D-MUX and symmetric MUX-locking techniques, relying only on the underlying structure of the locked design, i.e., in an oracle-less setting. Our trained GNN model learns the structure of the given circuit and the composition of gates around the non-obfuscated wires, thereby generating meaningful link embeddings that help decipher the secret inputs to the MUXes. The proposed MuxLink achieves key prediction accuracy and precision up to 100% on D-MUX and symmetric MUX-locked ISCAS-85 and ITC-99 benchmarks, fully unlocking the designs. We open-source MuxLink [1].

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 to prevent black-box usage. They do not directly address hiding design intent through structural transformations, which is an important objective of obfuscation. We note that current obfuscation techniques incorporate only: (1) local, and (2) predictable changes in circuit topology. In this paper, we present SAIL, a structural attack on obfuscation using machine learning (ML) models that exposes a critical vulnerability of these methods. Through this attack, we demonstrate that the gate-level structure of an obfuscated design can be retrieved in most parts through a systematic set of steps. The proposed attack is applicable to all forms of logic obfuscation, 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 show that we can recover an average of around 84% (up to 95%) transformations introduced by obfuscation. We also show that this attack is scalable, flexible, and versatile.

Logic locking has emerged as a prominent key-driven technique to protect the integrity of integrated circuits. However, novel machine-learning-based attacks have recently been introduced to challenge the security foundations of locking schemes. These attacks are able to recover a significant percentage of the key without having access to an activated circuit. This paper address this issue through two focal points. First, we present a theoretical model to test locking schemes for key-related structural leakage that can be exploited by machine learning. Second, based on the theoretical model, we introduce D-MUX: a deceptive multiplexer-based logic-locking scheme that is resilient against structure-exploiting machine learning attacks. Through the design of D-MUX, we uncover a major fallacy in existing multiplexer-based locking schemes in the form of a structural-analysis attack. Finally, an extensive cost evaluation of D-MUX is presented. To the best of our knowledge, D-MUX is the first machine-learning-resilient locking scheme capable of protecting against all known learning-based attacks. Hereby, the presented work offers a starting point for the design and evaluation of future-generation logic locking in the era of machine learning.