LLM for SoC Security: A Paradigm Shift
Dipayan Saha Shams Tarek Katayoon Yahyaei Sujan Kumar Saha Jingbo Zhou Mark Tehranipoor Farimah Farahmandi
As the ubiquity and complexity of system-on-chip (SoC) designs increase
across electronic devices, the task of incorporating security into an SoC
design flow poses significant challenges. Existing security solutions are
inadequate to provide effective verification of modern SoC designs due to their
limitations in scalability, comprehensiveness, and adaptability. On the other
hand, Large Language Models (LLMs) are celebrated for their remarkable success
in natural language understanding, advanced reasoning, and program synthesis
tasks. Recognizing an opportunity, our research delves into leveraging the
emergent capabilities of Generative Pre-trained Transformers (GPTs) to address
the existing gaps in SoC security, aiming for a more efficient, scalable, and
adaptable methodology. By integrating LLMs into the SoC security verification
paradigm, we open a new frontier of possibilities and challenges to ensure the
security of increasingly complex SoCs. This paper offers an in-depth analysis
of existing works, showcases practical case studies, demonstrates comprehensive
experiments, and provides useful promoting guidelines. We also present the
achievements, prospects, and challenges of employing LLM in different SoC
security verification tasks.