Machine learning has shown great promise in addressing several critical
hardware security problems. In particular, researchers have developed novel
graph neural network (GNN)-based techniques for detecting intellectual property
(IP) piracy, detecting hardware Trojans (HTs), and reverse engineering
circuits, to name a few. These techniques have demonstrated outstanding
accuracy and have received much attention in the community. However, since
these techniques are used for security applications, it is imperative to
evaluate them thoroughly and ensure they are robust and do not compromise the
security of integrated circuits.
In this work, we propose AttackGNN, the first red-team attack on GNN-based
techniques in hardware security. To this end, we devise a novel reinforcement
learning (RL) agent that generates adversarial examples, i.e., circuits,
against the GNN-based techniques. We overcome three challenges related to
effectiveness, scalability, and generality to devise a potent RL agent. We
target five GNN-based techniques for four crucial classes of problems in
hardware security: IP piracy, detecting/localizing HTs, reverse engineering,
and hardware obfuscation. Through our approach, we craft circuits that fool all
GNNs considered in this work. For instance, to evade IP piracy detection, we
generate adversarial pirated circuits that fool the GNN-based defense into
classifying our crafted circuits as not pirated. For attacking HT localization
GNN, our attack generates HT-infested circuits that fool the defense on all
tested circuits. We obtain a similar 100% success rate against GNNs for all
classes of problems.

该研究通过针对硬件安全中的基于图神经网络技术的攻击，提出了 AttackGNN 方法，并通过生成对抗性示例，即电路，来欺骗这些技术。通过攻击，成功地生成了能欺骗所有测试电路的对抗电路，包括知识产权盗版、硬件木马侦测 / 定位、电路逆向工程和硬件混淆等四类硬件安全问题。