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Evolutionary Large Language Models for Hardware Security: A Comparative Survey

Mohammad Akyash, Hadi Mardani Kamali

TL;DR

This paper addresses the problem of detecting and mitigating hardware security vulnerabilities during chip design by surveying evolutionary large language models (LLMs) applied to RTL-level tasks. It organizes HW-oriented LLM work into RTL generation/refinement and security verification, further subdividing verification into prompt engineering and fine-tuning approaches, and it analyzes methods such as Circuit Transformer, RTLCoder, SVAs generation, and RTLFixer. The study highlights challenges in universality, functional verification, data scarcity, and scalability, and proposes future directions like domain-specific LLMs, standardized RTL datasets, and graph/AST-informed models to enable reliable automated security assessment. The work underscores the practical impact of tailored HW-security LLMs for pre-silicon risk mitigation and robust, automated security measurement in SoC design.

Abstract

Automating hardware (HW) security vulnerability detection and mitigation during the design phase is imperative for two reasons: (i) It must be before chip fabrication, as post-fabrication fixes can be costly or even impractical; (ii) The size and complexity of modern HW raise concerns about unknown vulnerabilities compromising CIA triad. While Large Language Models (LLMs) can revolutionize both HW design and testing processes, within the semiconductor context, LLMs can be harnessed to automatically rectify security-relevant vulnerabilities inherent in HW designs. This study explores the seeds of LLM integration in register transfer level (RTL) designs, focusing on their capacity for autonomously resolving security-related vulnerabilities. The analysis involves comparing methodologies, assessing scalability, interpretability, and identifying future research directions. Potential areas for exploration include developing specialized LLM architectures for HW security tasks and enhancing model performance with domain-specific knowledge, leading to reliable automated security measurement and risk mitigation associated with HW vulnerabilities.

Evolutionary Large Language Models for Hardware Security: A Comparative Survey

TL;DR

This paper addresses the problem of detecting and mitigating hardware security vulnerabilities during chip design by surveying evolutionary large language models (LLMs) applied to RTL-level tasks. It organizes HW-oriented LLM work into RTL generation/refinement and security verification, further subdividing verification into prompt engineering and fine-tuning approaches, and it analyzes methods such as Circuit Transformer, RTLCoder, SVAs generation, and RTLFixer. The study highlights challenges in universality, functional verification, data scarcity, and scalability, and proposes future directions like domain-specific LLMs, standardized RTL datasets, and graph/AST-informed models to enable reliable automated security assessment. The work underscores the practical impact of tailored HW-security LLMs for pre-silicon risk mitigation and robust, automated security measurement in SoC design.

Abstract

Automating hardware (HW) security vulnerability detection and mitigation during the design phase is imperative for two reasons: (i) It must be before chip fabrication, as post-fabrication fixes can be costly or even impractical; (ii) The size and complexity of modern HW raise concerns about unknown vulnerabilities compromising CIA triad. While Large Language Models (LLMs) can revolutionize both HW design and testing processes, within the semiconductor context, LLMs can be harnessed to automatically rectify security-relevant vulnerabilities inherent in HW designs. This study explores the seeds of LLM integration in register transfer level (RTL) designs, focusing on their capacity for autonomously resolving security-related vulnerabilities. The analysis involves comparing methodologies, assessing scalability, interpretability, and identifying future research directions. Potential areas for exploration include developing specialized LLM architectures for HW security tasks and enhancing model performance with domain-specific knowledge, leading to reliable automated security measurement and risk mitigation associated with HW vulnerabilities.
Paper Structure (10 sections, 2 figures, 2 tables)

This paper contains 10 sections, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. LLMs for SW: Engineering and Testing
  3. LLMs for HW: Design and Testing
  4. LLM Agent for EDA Automation
  5. LLM for RTL Generation and Refinement
  6. LLM for HW: Security (Verification)
  7. Prompt Engineering
  8. Fine-Tuning
  9. Takeaways and Future Directions
  10. Conclusion

Figures (2)

  • Figure 1: The Usage of LLMs for HDL (RTL) Generation/Validation.
  • Figure 2: An Examplary Case in GPT-4 for Security Debugging.