# LLM for SoC Security: A Paradigm Shift

arXiv: https://arxiv.org/abs/2310.06046 · HTML: https://mlsanity.com/paper/2310.06046/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 140 of its 242 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 30 of 140)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)

## Built on by (most cited first, 22 of 22)

- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [VeriDebug: A Unified LLM for Verilog Debugging via Contrastive Embedding and Guided Correction](https://mlsanity.com/paper/2504.19099/) (2025)
- [VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation](https://mlsanity.com/paper/2507.13369/) (2025)
- [ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification](https://mlsanity.com/paper/2505.06821/) (2025)
- [Evolution of Optimization Algorithms for Global Placement via Large Language Models](https://mlsanity.com/paper/2504.17801/) (2025)
- [SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models](https://mlsanity.com/paper/2506.20415/) (2025)
- [MARVEL: Multi-Agent RTL Vulnerability Extraction using Large Language Models](https://mlsanity.com/paper/2505.11963/) (2025)
- [BugWhisperer: Fine-Tuning LLMs for SoC Hardware Vulnerability Detection](https://mlsanity.com/paper/2505.22878/) (2025)
- [Automated Consistency Analysis of LLMs](https://mlsanity.com/paper/2502.07036/) (2025)
- [TrojanWhisper: Evaluating Pre-trained LLMs to Detect and Localize Hardware Trojans](https://mlsanity.com/paper/2412.07636/) (2024)
- [RuleGenie: SIEM Detection Rule Set Optimization](https://mlsanity.com/paper/2505.06701/) (2025)
- [Generative AI for Internet of Things Security: Challenges and Opportunities](https://mlsanity.com/paper/2502.08886/) (2025)
- [A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards](https://mlsanity.com/paper/2505.13766/) (2025)
- [TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs](https://mlsanity.com/paper/2506.17894/) (2025)
- [DeepV: A Model-Agnostic Retrieval-Augmented Framework for Verilog Code Generation with a High-Quality Knowledge Base](https://mlsanity.com/paper/2510.05327/) (2025)
- [LAsset: An LLM-assisted Security Asset Identification Framework for System-on-Chip (SoC) Verification](https://mlsanity.com/paper/2601.02624/) (2026)
- [AI in Money Matters](https://mlsanity.com/paper/2505.07393/) (2025)
- [Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques](https://mlsanity.com/paper/2507.13629/) (2025)
- [Cyber Threat Detection and Vulnerability Assessment System using Generative AI and Large Language Model](https://mlsanity.com/paper/2601.06213/) (2026)
- [VeriChat: An Agentic Conversational AI Assistant for Hardware Security Verification](https://mlsanity.com/paper/2607.01668/) (2026)
- [Data-Efficient Adaptation of LLMs via Attention Head Reweighting](https://mlsanity.com/paper/2607.13425/) (2026)

## Abstract (arXiv)

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.