LLM如何提升安全防御，又有哪些自身风险
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
- 将大模型融入威胁检测、漏洞评估等安全场景
- 揭示大模型在安全应用中的潜在漏洞与攻击面
- 适合关注AI安全融合的工程师与研究人员
大型语言模型（LLMs）正推动网络安全向智能化、自适应和自动化方向发展，在物联网、区块链及硬件安全等领域展现出超越传统方法的能力。本文综述了大模型在网络安全中的两大核心应用：一是将其集成到关键安全领域；二是分析大模型自身的脆弱性及其缓解策略。通过整合最新进展并指出关键局限，本工作为构建安全、可扩展且面向未来的网络防御体系提供了实践洞察与战略建议。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in tackling challenges across domains such as IoT, blockchain, and hardware security. This survey provides a comprehensive overview of LLM applications in cybersecurity, focusing on two core areas: (1) the integration of LLMs into key cybersecurity domains, and (2) the vulnerabilities of LLMs themselves, along with mitigation strategies. By synthesizing recent advancements and identifying key limitations, this work offers practical insights and strategic recommendations for leveraging LLMs to build secure, scalable, and future-ready cyber defense systems.
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