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HW Security: Multi-Agent AI Assistant Leveraging LLMs To Automate Key Stages of SoC Security Verification (U. of Florida)
- Researchers at the University of Florida have published a technical paper on using large language models (LLMs) for SoC security verification.
- The paper introduces SV-LLM, a multi-agent assistant system that aims to automate and enhance SoC security verification processes.
- The traditional verification techniques for complex SoC designs face challenges in automation, scalability, comprehensiveness, and adaptability.
- LLMs offer advanced capabilities in natural language understanding, code generation, and reasoning, presenting a promising solution for SoC security verification.
- SV-LLM employs a multi-agent approach where specialized LLM agents collaborate to address various security verification tasks efficiently.
- The system integrates agents for tasks like verification question answering, security asset identification, threat modeling, test plan generation, vulnerability detection, and bug validation through simulation.
- Agents within SV-LLM use different learning paradigms such as in-context learning, fine-tuning, and retrieval-augmented generation (RAG) to optimize their performance.
- SV-LLM aims to reduce manual intervention, enhance accuracy, and speed up the security analysis process, aiding in early risk identification and mitigation during the design phase.
- The paper demonstrates the transformative potential of SV-LLM in hardware security through case studies and experiments, showcasing its practicality and effectiveness.
- Overall, SV-LLM introduces an innovative approach to SoC security verification leveraging LLMs and multi-agent systems for improved efficiency and accuracy.
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