AI for Hardware Trojan Detection - 97% Accuracy Method Targets Chip Supply Chain Risk

University of Missouri researchers report a 97%-accurate AI-driven framework to detect and explain hardware trojans in chip designs, outlined in an IEEE Access publication. The system—leveraging large language models similar to those powering chatbots—scans hardware design code to identify suspicious logic and provides human-readable explanations for why a segment is malicious. Detecting hardware trojans has traditionally been costly and slow; the team positions their approach as both faster and more transparent, enabling integration across open-source and enterprise design pipelines and deployable on local or cloud infrastructure. Because hardware trojans are effectively immutable post-fabrication and can remain dormant until triggered, early detection in pre-silicon stages is crucial to avoid catastrophic downstream recalls and national security impacts. The authors note applicability across consumer electronics, healthcare, finance, and defense, and indicate ongoing work on automated fixes during design. For enterprises depending on complex global chip supply chains, explainability is key—security and design teams must trust why detections occur to make timely go/no-go decisions. The research aligns with a broader push toward Software/Hardware Bills of Materials and verifiable design provenance in critical systems.