{"ID":22918826,"CreatedAt":"2026-09-17T01:02:08.507062015Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.17922","arxiv_id":"2609.17922","title":"Demystifying Gate-Level Localization of RTL Trojans","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.","short_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 2...","url_abs":"https://arxiv.org/abs/2609.17922","url_pdf":"https://arxiv.org/pdf/2609.17922v1","authors":"[\"Navid Nader Tehrani\",\"Azadeh Davoodi\",\"Rasit Onur Topaloglu\"]","published":"2026-09-15T23:31:47Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"cs.CR\"]","methods":"[\"Transformer\"]","has_code":false}