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arXiv AI By Saideep Sreekumar, Zeng Wang, Akashdeep Saha, Weihua Xiao, Minghao Shao, Muhammad Shafique, Ozgur Sinanoglu, Ramesh Karri, Johann Knechtel

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion

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TrojanGYM is an LLM‑driven framework that automatically generates diverse hardware Trojan (HT) insertions to expose blind spots in learning‑based detectors. It uses multiple large language models to propose and refine RTL modifications, while an agentic loop with syntactic checks, functional verification, and GNN‑based detectors iteratively improves the HT designs. The authors also present Robust‑GNN4TJ, a more robust detector that improves detection rates on TrojanGYM benchmarks, and demonstrate that TrojanGYM can achieve up to 68.75% evasion against modern GNN detectors on SRAM, AES‑128, UART, and RISC‑V RTL designs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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