The globalization of the integrated circuit (IC) supply chain increases the risk of security threats, such as hardware Trojans (HTs) and the theft of intellectual property (IP). Graph Neural Networks (GNNs), among the most powerful deep learning methods for processing graph-structured data, have been widely adopted to detect such threats. However, GNNs are susceptible to backdoor attacks that can maliciously manipulate output predictions toward an adversarial target. These attacks are not only difficult to detect but also compromise the integrity of GNN-based security systems. Most prior work embeds backdoor triggers using randomly generated subgraphs or gradient-guided generative subgraphs. However, such triggers are impractical for GNN-based hardware security applications as they do not guarantee the preservation of circuit functionality. In this paper, we propose GRAFT, a graph let-triggered backdoor attack targeting GNN-based hardware security. GRAFT embeds graphlet-based triggers at either the register-transfer level (RTL) or gate level of the design while preserving the circuit 's original function. We evaluate GRAFT on the ISCAS-85 and TrustHub datasets. Our experimental results demonstrate that GRAFT can effectively evade HT detection and IP piracy detection, achieving an attack success rate (ASR) of up to 100%.

翻译：集成电路（IC）供应链的全球化增加了安全威胁的风险，例如硬件木马（HT）和知识产权（IP）盗用。图神经网络（GNN）作为处理图结构数据最强大的深度学习方法之一，已被广泛用于检测此类威胁。然而，GNN容易受到后门攻击，这些攻击会恶意地将输出预测操纵至对抗性目标。此类攻击不仅难以检测，还会破坏基于GNN的安全系统的完整性。以往多数工作通过随机生成的子图或梯度引导的生成子图来嵌入后门触发器，但这些触发器无法保证维持电路功能，因此不适用于基于GNN的硬件安全应用。本文提出GRAFT——一种针对基于GNN硬件安全的图元触发后门攻击。GRAFT在设计的寄存器传输级（RTL）或门级嵌入基于图元的触发器，同时保留电路的原始功能。我们在ISCAS-85和TrustHub数据集上评估了GRAFT。实验结果表明，GRAFT能够有效规避HT检测和IP盗版检测，实现高达100%的攻击成功率（ASR）。