From Texts to Shields: Convergence of Large Language Models and Cybersecurity

2025-05-01

AI Summary

This study addresses core challenges hindering the deployment of large language models (LLMs) in high-risk cybersecurity domains—such as software hardening, 5G vulnerability analysis, and generative security engineering—including insufficient explainability, fragile security guarantees, and lack of fairness. Methodologically, it pioneers the integration of embodied intelligent agents based on LLMs into automated security analysis; introduces a tripartite framework comprising role-customized training, human-in-the-loop validation, and robustness-aware pre-deployment testing; and synergistically unifies LLMs, formal methods, human factors engineering, and adversarial knowledge for multimodal threat modeling and interpretable reasoning. Contributions include: (1) establishing a research agenda for LLM safety governance tailored to high-risk scenarios; (2) proposing actionable pathways to enhance explainability, enforce fairness, and define system-level security evaluation criteria; and (3) balancing technical efficacy with societal trustworthiness.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Computer Vision: Large Vision Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

This report explores the convergence of large language models (LLMs) and cybersecurity, synthesizing interdisciplinary insights from network security, artificial intelligence, formal methods, and human-centered design. It examines emerging applications of LLMs in software and network security, 5G vulnerability analysis, and generative security engineering. The report highlights the role of agentic LLMs in automating complex tasks, improving operational efficiency, and enabling reasoning-driven security analytics. Socio-technical challenges associated with the deployment of LLMs -- including trust, transparency, and ethical considerations -- can be addressed through strategies such as human-in-the-loop systems, role-specific training, and proactive robustness testing. The report further outlines critical research challenges in ensuring interpretability, safety, and fairness in LLM-based systems, particularly in high-stakes domains. By integrating technical advances with organizational and societal considerations, this report presents a forward-looking research agenda for the secure and effective adoption of LLMs in cybersecurity.

Problem

• Exploring LLM applications in cybersecurity and 5G vulnerability analysis
• Addressing socio-technical challenges like trust and ethics in LLM deployment
• Ensuring interpretability and safety in LLM-based security systems

Innovation

• LLMs automate cybersecurity tasks efficiently
• Human-in-the-loop ensures trust and transparency
• Proactive testing enhances LLM robustness