Demystifying Gate-Level Localization of RTL Trojans

2026-09-15

AI Summary

本文提出一种基于启发式的轻量级方法LoRD，用于检测和定位RTL木马，在合成后的门级网表中通过识别稳定的结构和信号流模式实现高效检测。

Abstract

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.

Problem

• Hardware Trojans
• Register Transfer Level (RTL)
• gate-level netlists
• detection

Innovation

• heuristic-based approach
• subgraph signatures
• RTL Trojans
• gate-level localization
• lightweight detection