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Securing critical assets in a bus-based System-On-Chip (SoC) is imperative tomitigate potential vulnerabilities and prevent unauthorized access, ensuringthe integrity, availability, and confidentiality of the system. Ensuringsecurity throughout the SoC design process is a formidable task owing to theinherent intricacies in SoC designs and the dispersion of assets across diverseIPs. 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. Inthis work, we propose DIVAS, a novel framework that leverages the knowledgebase of LLMs to identify security vulnerabilities from user-defined SoCspecifications, map them to the relevant Common Weakness Enumerations (CWEs),followed by the generation of equivalent assertions, and employ securitymeasures through enforcement of security policies. The proposed framework isimplemented using multiple ChatGPT and BARD models, and their performance wasanalyzed while generating relevant CWEs from the SoC specifications provided.The experimental results obtained from open-source SoC benchmarks demonstratethe efficacy of our proposed framework.

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