Large Language Models for Hardware Security (Invited, Short Paper)

Secure digital hardware is the foundation of secure systems. However, achieving hardware security requires a lot of disparate expertise, ranging from knowledge of tools, awareness of myriad threats, and a fundamental understanding of how digital hardware is used in a given application. Such expertise is rare, so mistakes are made. We believe that recent advancements in AI, specifically Large Language Models (LLMs), provide a potential pathway for alleviating the burden on digital hardware designers. In this paper, we outline some of the challenges faced by cybersecurity experts in the hardware space, our vision for how LLMs can revolutionize hardware security, briefly present current progress and recent works in this research direction, and finally reflect on open challenges that remain for the community.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/tps-isa62245.2024.00055
Citations
22
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
7.71

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article

Large Language Models for Hardware Security (Invited, Short Paper)

Hammond Pearce, Benjamin Tan
22 citations
Advanced Malware Detection Techniques
7.71
article

Large Language Models for Hardware Security (Invited, Short Paper)

Hammond Pearce, Benjamin Tan
article en
22 citations

Abstract

Secure digital hardware is the foundation of secure systems. However, achieving hardware security requires a lot of disparate expertise, ranging from knowledge of tools, awareness of myriad threats, and a fundamental understanding of how digital hardware is used in a given application. Such expertise is rare, so mistakes are made. We believe that recent advancements in AI, specifically Large Language Models (LLMs), provide a potential pathway for alleviating the burden on digital hardware designers. In this paper, we outline some of the challenges faced by cybersecurity experts in the hardware space, our vision for how LLMs can revolutionize hardware security, briefly present current progress and recent works in this research direction, and finally reflect on open challenges that remain for the community.

University of Calgary (CA), UNSW Sydney (AU)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 2%
Advanced Malware Detection Techniques
7.71
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