# Large Language Models and Attention-Based AI for Hardware Design and Security: Progress, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2504.08854 · HTML: https://mlsanity.com/paper/2504.08854/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2025.

## Built on by (most cited first, 4 of 4)

- [AI Agents for Photonic Integrated Circuit Design Automation](https://mlsanity.com/paper/2508.14123/) (2025)
- [Can Agents Secure Hardware? Evaluating Agentic LLM-Driven Obfuscation for IP Protection](https://mlsanity.com/paper/2604.13298/) (2026)
- [RAG-PRISM: A Personalized, Rapid, and Immersive Skill Mastery Framework with Adaptive Retrieval-Augmented Tutoring](https://mlsanity.com/paper/2509.00646/) (2025)
- [Surveying GenAI-based Automation in Printed Circuit Board Design and Test](https://mlsanity.com/paper/2606.17074/) (2026)

## Abstract (arXiv)

Recent advances in attention-based artificial intelligence (AI) models have unlocked vast potential to automate digital hardware design while enhancing and strengthening security measures against various threats. This rapidly emerging field leverages Large Language Models (LLMs) to generate HDL code, identify vulnerabilities, and sometimes mitigate them. The state of the art in this design automation space utilizes optimized LLMs with HDL datasets, creating automated systems for register transfer level (RTL) generation, verification, and debugging, and establishing LLM-driven design environments for streamlined logic designs. Additionally, attention-based models like graph attention have shown promise in chip design applications, including floorplanning. This survey investigates the integration of these models into hardware-related domains, emphasizing logic design and hardware security, with or without the use of IP libraries. This study explores the commercial and academic landscape, highlighting technical hurdles and future prospects for automating hardware design and security. Moreover, it provides new insights into the study of LLM-driven design systems, advances in hardware security mechanisms, and the impact of influential works on industry practices. Through the examination of 30 representative approaches and illustrative case studies, this paper underscores the transformative potential of attention-based models in revolutionizing hardware design while addressing the challenges that lie ahead in this interdisciplinary domain.