揭秘RTL木马的门级定位

中文摘要 AI 辅助

硬件木马是恶意修改，会破坏功能或泄露敏感数据。它们构成严重威胁，尤其是在寄存器传输级（RTL）插入时。综合后，这些木马常被门级网表中的优化所掩盖。近期工作，包括ICCAD 2025竞赛，强调使用机器学习（ML）对标记网表进行无金芯片检测。在本工作中，我们表明RTL木马在综合后表现出稳定的结构和信号流模式，从而能够通过针对性启发式方法而非通用ML特征学习实现有效检测。我们提出LoRD，一种轻量级基于启发式的方法，利用这些独特的子图签名，在竞赛测试用例上实现了近乎完美的检测和定位。与基于Transformer的ML基线和前五名团队相比，LoRD在植入木马的设计上平均得分为2.957（满分3分），且无需数据和调优开销。

英文摘要

Hardware Trojans are malicious modifications that compromise functionality or leak sensitive data. They pose a severe threat, particularly when inserted at the Register Transfer Level (RTL). After synthesis, these Trojans are often concealed by optimizations in gate-level netlists. Recent efforts, including the ICCAD 2025 contest, emphasize golden-chip-free detection using machine learning (ML) on labeled netlists. In this work, we show that RTL Trojans exhibit stable structural and signal-flow patterns post-synthesis, enabling effective detection through targeted heuristics rather than generic ML feature learning. We propose LoRD, a lightweight heuristic-based approach that exploits these distinctive subgraph signatures, achieving near-perfect detection and localization on the contest testcases. Com- pared to a transformer-based ML baseline and top five teams, LoRD achieves on-average a score of 2.957 (out of 3) for Trojan- implanted designs without the data and tuning overhead.

发表机构

• University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
• Marist University（玛里斯特学院）

机构由 AI 辅助整理，请以论文原文为准。