# DIVAS: An LLM-based End-to-End Framework for SoC Security Analysis and Policy-based Protection

arXiv: https://arxiv.org/abs/2308.06932 · HTML: https://mlsanity.com/paper/2308.06932/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2023.

## Built on by (most cited first, 12 of 12)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generation](https://mlsanity.com/paper/2407.01910/) (2024)
- [LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation](https://mlsanity.com/paper/2401.12224/) (2023)
- [LLM for SoC Security: A Paradigm Shift](https://mlsanity.com/paper/2310.06046/) (2023)
- [Can Large Language Models Provide Security & Privacy Advice? Measuring the Ability of LLMs to Refute Misconceptions](https://mlsanity.com/paper/2310.02431/) (2023)
- [LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust](https://mlsanity.com/paper/2405.07061/) (2024)
- [New Solutions on LLM Acceleration, Optimization, and Application](https://mlsanity.com/paper/2406.10903/) (2024)
- [SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models](https://mlsanity.com/paper/2506.20415/) (2025)
- [SVAgent: AI Agent for Hardware Security Verification Assertion](https://mlsanity.com/paper/2507.16203/) (2025)
- [MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation](https://mlsanity.com/paper/2507.02057/) (2025)
- [Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques](https://mlsanity.com/paper/2507.13629/) (2025)

## Abstract (arXiv)

Securing critical assets in a bus-based System-On-Chip (SoC) is imperative to mitigate potential vulnerabilities and prevent unauthorized access, ensuring the integrity, availability, and confidentiality of the system. Ensuring security throughout the SoC design process is a formidable task owing to the inherent intricacies in SoC designs and the dispersion of assets across diverse IPs. Large Language Models (LLMs), exemplified by ChatGPT (OpenAI) and BARD (Google), have showcased remarkable proficiency across various domains, including security vulnerability detection and prevention in SoC designs. In this work, we propose DIVAS, a novel framework that leverages the knowledge base of LLMs to identify security vulnerabilities from user-defined SoC specifications, map them to the relevant Common Weakness Enumerations (CWEs), followed by the generation of equivalent assertions, and employ security measures through enforcement of security policies. The proposed framework is implemented using multiple ChatGPT and BARD models, and their performance was analyzed while generating relevant CWEs from the SoC specifications provided. The experimental results obtained from open-source SoC benchmarks demonstrate the efficacy of our proposed framework.