Automated Hardware Trojan Insertion in Industrial-Scale Designs

• URL: http://arxiv.org/abs/2511.08703v1
• Date: Thu, 13 Nov 2025 01:02:47 GMT
• Title: Automated Hardware Trojan Insertion in Industrial-Scale Designs
• Authors: Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband,
• Abstract summary: This work presents an automated and scalable methodology for generating HT-like patterns in industry-scale netlists.<n>The pipeline parses large gate-level designs into connectivity graphs, and explores rare regions using SCOAP testability metrics.<n>When evaluated on the benchmarks generated in this work, representative state-of-the-art graph-learning models fail to detect Trojans.
• Score: 1.1772291323400081
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Industrial Systems-on-Chips (SoCs) often comprise hundreds of thousands to millions of nets and millions to tens of millions of connectivity edges, making empirical evaluation of hardware-Trojan (HT) detectors on realistic designs both necessary and difficult. Public benchmarks remain significantly smaller and hand-crafted, while releasing truly malicious RTL raises ethical and operational risks. This work presents an automated and scalable methodology for generating HT-like patterns in industry-scale netlists whose purpose is to stress-test detection tools without altering user-visible functionality. The pipeline (i) parses large gate-level designs into connectivity graphs, (ii) explores rare regions using SCOAP testability metrics, and (iii) applies parameterized, function-preserving graph transformations to synthesize trigger-payload pairs that mimic the statistical footprint of stealthy HTs. When evaluated on the benchmarks generated in this work, representative state-of-the-art graph-learning models fail to detect Trojans. The framework closes the evaluation gap between academic circuits and modern SoCs by providing reproducible challenge instances that advance security research without sharing step-by-step attack instructions.

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