AI-Assisted Hardware Security Verification: A Survey and AI Accelerator Case Study

TL;DR

This paper reviews recent AI and LLM techniques in hardware security verification, organizing the literature by workflow stages and illustrating practical application through a case study on NVIDIA NVDLA.

Contribution

It synthesizes recent advances in AI-assisted hardware security verification and provides a practical case study demonstrating their application.

Findings

AI/LLM techniques can automate verification stages effectively.

AI-assisted methods accelerate security analysis workflows.

Outputs require grounding in simulation, formal reasoning, and benchmarks.

Abstract

As hardware systems grow in complexity, security verification must keep up with them. Recently, artificial intelligence (AI) and large language models (LLMs) have started to play an important role in automating several stages of the verification workflow by helping engineers analyze designs, reason about potential threats, and generate verification artifacts. This survey synthesizes recent advances in AI-assisted hardware security verification and organizes the literature along key stages of the workflow: asset identification, threat modeling, security test-plan generation, simulation-driven analysis, formal verification, and countermeasure reasoning. To illustrate how these techniques can be applied in practice, we present a case study using the open-source NVIDIA Deep Learning Accelerator (NVDLA), a representative modern hardware design. Throughout this study, we emphasize that while AI/LLM-based automation can significantly accelerate verification tasks, its outputs must remain grounded in simulation evidence, formal reasoning, and benchmark-driven evaluation to ensure trustworthy hardware security assurance.