大模型赋能网络安全，实现智能防护与自动化响应
From Texts to Shields: Convergence of Large Language Models and Cybersecurity
- 利用大模型自动分析5G漏洞并生成安全策略
- 通过智能体实现复杂安全任务的自主执行
- 适合安全研究者与系统架构师参考
本报告探讨了大语言模型（LLMs）与网络安全的融合，综合网络安全部、人工智能、形式化方法及以人为本设计的跨学科洞见。研究覆盖大模型在软件与网络安全部、5G漏洞分析及生成式安全工程中的新兴应用。报告指出，智能体型大模型可自动化复杂任务，提升运营效率，并实现基于推理的安全分析。部署中面临信任、透明度与伦理挑战，可通过人机协同系统、角色定制训练及主动鲁棒性测试应对。报告还提出确保可解释性、安全性和公平性的关键研究挑战，尤其在高风险领域。通过整合技术进步与组织社会因素，提出面向未来的大模型在网络安全中安全有效应用的研究蓝图。
原文摘要 · Abstract (English)
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.
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