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arXiv · 2609.17922

Demystifying Gate-Level Localization of RTL Trojans

Navid Nader Tehrani·Azadeh Davoodi·Rasit Onur Topaloglu
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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.

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Navid Nader Tehrani, Azadeh Davoodi, Rasit Onur Topaloglu. 2026-09-15. Demystifying Gate-Level Localization of RTL Trojans. https://arxiv.org/abs/2609.17922

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