{"ID":2835246,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.00591","arxiv_id":"2512.00591","title":"TrojanLoC: LLM-based Framework for RTL Trojan Localization","abstract":"Hardware Trojans (HT s) are a persistent threat to integrated circuits, especially when inserted at the register-transfer level (RTL). Existing methods typically first convert the design into a graph, such as a gate-level netlist or an RTL-derived dataflow graph (DFG), and then use a graph neural network (GNN ) to obtain an embedding of that graph, which (i) loses compact RTL semantics, (ii) relies on shallow GNNs with limited receptive field, and (iii) is largely restricted to coarse, module-level binary HT detection. We propose TrojanLoC, an LLM-based framework for RTL-level HT localization. We use an RTL-finetuned LLM to derive module-level and line-level embeddings directly from RTL code, capturing both global design context and local semantics. Next, we train task-specific classifiers on these embeddings to perform module-level Trojan detection, type prediction, and fine-grained line-level localization. We also introduce TrojanInS, a large synthetic dataset of RTL designs with systematically injected Trojans from four effect-based categories, each accompanied by precise line-level annotations. Our experiments show that TrojanLoC achieves strong module-level performance, reaching 0.99 F1-score for Trojan detection, up to 0.68 higher than baseline, and 0.84 macro-F1 for Trojan-type classification. At the line level, TrojanLoc further achieves up to 0.93 macro-F1, enabling fine-grained localization of Trojan-relevant RTL lines","short_abstract":"Hardware Trojans (HT s) are a persistent threat to integrated circuits, especially when inserted at the register-transfer level (RTL). Existing methods typically first convert the design into a graph, such as a gate-level netlist or an RTL-derived dataflow graph (DFG), and then use a graph neural network (GNN ) to obta...","url_abs":"https://arxiv.org/abs/2512.00591","url_pdf":"https://arxiv.org/pdf/2512.00591v1","authors":"[\"Weihua Xiao\",\"Zeng Wang\",\"Minghao Shao\",\"Raghu Vamshi Hemadri\",\"Ozgur Sinanoglu\",\"Muhammad Shafique\",\"Johann Knechtel\",\"Siddharth Garg\",\"Ramesh Karri\"]","published":"2025-11-29T18:45:00Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[\"Graph Neural Network\",\"Large Language Model\"]","has_code":false}