# MuxLink: Circumventing Learning-Resilient MUX-Locking Using Graph Neural Network-based Link Prediction

arXiv: https://arxiv.org/abs/2112.07178 · HTML: https://mlsanity.com/paper/2112.07178/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2021.

## Built on by (most cited first, 5 of 5)

- [AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning](https://mlsanity.com/paper/2402.13946/) (2024)
- [GNN4REL: Graph Neural Networks for Predicting Circuit Reliability Degradation](https://mlsanity.com/paper/2208.02868/) (2022)
- [Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs](https://mlsanity.com/paper/2211.16495/) (2022)
- [Security Closure of IC Layouts Against Hardware Trojans](https://mlsanity.com/paper/2211.07997/) (2022)
- [TroLLoc: Logic Locking and Layout Hardening for IC Security Closure against Hardware Trojans](https://mlsanity.com/paper/2405.05590/) (2024)

## Abstract (arXiv)

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].