LLM与生成式AI在安全攻防中双刃齐发，揭示其威胁演化与防御新范式。
Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies
- 系统梳理生成式AI在攻防中的双重作用，涵盖恶意代码生成与安全检测。
- 2025年预计50%威胁由LLM生成，较2021年2%显著增长，自动化攻击成主流。
- 提出水印、对抗防御等实用策略，适合安全研究人员与工程团队参考。
大型语言模型（LLMs）与生成式AI（GenAI）系统如ChatGPT、Claude、Gemini、LLaMA、Copilot、Stable Diffusion等正重塑网络安全领域，既推动自动化防御，也催生复杂攻击。这些技术实现实时威胁检测、钓鱼防御、安全编码生成及漏洞利用，规模空前。2025年，预计50%的已检威胁将由LLM生成，远超2021年仅2%的水平。2026年应对高度自动化的威胁环境，亟需新一代安全框架。本文综述了LLMs在零日检测、DevSecOps、联邦学习、合成内容分析与可解释AI（XAI）中的应用。基于70余篇学术论文、行业报告与技术文档，结合Google Play Protect、Microsoft Defender、AWS、Apple App Store、OpenAI插件商店、Hugging Face Spaces、GitHub等平台的真实案例，以及SAFE框架与AI驱动异常检测等新兴举措，提出模型水印、对抗防御与跨行业协作等负责任部署建议，为可信AI与智能安全提供路线图，成为该交叉领域的权威参考。
原文摘要 · Abstract (English)
Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks. These technologies power real-time threat detection, phishing defense, secure code generation, and vulnerability exploitation at unprecedented scales. Following a rapid surge where LLM-generated malware grew to account for an estimated 50% of detected threats by 2025, up from just 2% in 2021, navigating this highly automated threat landscape in 2026 demands next-generation security frameworks. This paper presents a comprehensive survey of the beneficial and malicious applications of LLMs in cybersecurity, including zero-day detection, DevSecOps, federated learning, synthetic content analysis, and explainable AI (XAI). Drawing on a review of over 70 academic papers, industry reports, and technical documents, this work synthesizes insights from real-world case studies across platforms like Google Play Protect, Microsoft Defender, Amazon Web Services (AWS), Apple App Store, OpenAI Plugin Stores, Hugging Face Spaces, and GitHub, alongside emerging initiatives like the SAFE Framework and AI-driven anomaly detection. We conclude with practical recommendations for responsible and transparent LLM deployment and trustworthy AI, including model watermarking, adversarial defense, and cross-industry collaboration, setting a new benchmark for rigorous, holistic cybersecurity research at the intersection of AI and threat defense, and offering a roadmap for secure, scalable LLM systems that serves as a critical reference for researchers, engineers, and security leaders navigating the complex challenges of AI-driven cybersecurity.
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