  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].
