标题： 博弈论遇见大语言模型和代理人工智能：重新构想智能威胁时代的信息安全 显示英文标题

标题： Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats

摘要： 保护网络空间不仅需要先进的工具，还需要我们在威胁、信任和自主性方面的思维方式发生转变。 传统的网络安全方法依赖于人工响应和脆弱的启发式方法。 为了构建主动且智能的防御系统，我们需要集成的理论框架和软件工具。 博弈论为建模对抗行为、设计战略防御和实现自主系统中的信任提供了严格的理论基础。 同时，软件工具处理网络数据、可视化攻击面、验证合规性并提出缓解措施。 然而，理论与实际实现之间仍存在脱节。 大型语言模型（LLMs）和代理AI的兴起为弥合这一差距提供了新的途径。 由LLM驱动的代理可以将抽象策略转化为现实世界的决策。 相反，博弈论可以指导这些代理在复杂工作流程中的推理和协调。 LLM也挑战了经典的博弈论假设，如完全理性或静态收益，促使开发与认知和计算现实相一致的新模型。 这种共同进化有望带来更丰富的理论基础和新颖的解决方案概念。 代理AI也重塑了软件设计：系统现在必须从一开始就具备模块化、自适应和信任意识。 本章探讨博弈论、代理AI和网络安全的交叉领域。 我们回顾关键的博弈论框架（例如静态、动态、贝叶斯和信号博弈）和解决方案概念。 然后，我们研究LLM代理如何增强网络安全防御，并引入嵌入推理到AI代理中的LLM驱动博弈。 最后，我们探讨多代理工作流和协调博弈，概述这种融合如何促进安全、智能和自适应的网络系统。 显示英文摘要

摘要： Protecting cyberspace requires not only advanced tools but also a shift in how we reason about threats, trust, and autonomy. Traditional cybersecurity methods rely on manual responses and brittle heuristics. To build proactive and intelligent defense systems, we need integrated theoretical frameworks and software tools. Game theory provides a rigorous foundation for modeling adversarial behavior, designing strategic defenses, and enabling trust in autonomous systems. Meanwhile, software tools process cyber data, visualize attack surfaces, verify compliance, and suggest mitigations. Yet a disconnect remains between theory and practical implementation. The rise of Large Language Models (LLMs) and agentic AI offers a new path to bridge this gap. LLM-powered agents can operationalize abstract strategies into real-world decisions. Conversely, game theory can inform the reasoning and coordination of these agents across complex workflows. LLMs also challenge classical game-theoretic assumptions, such as perfect rationality or static payoffs, prompting new models aligned with cognitive and computational realities. This co-evolution promises richer theoretical foundations and novel solution concepts. Agentic AI also reshapes software design: systems must now be modular, adaptive, and trust-aware from the outset. This chapter explores the intersection of game theory, agentic AI, and cybersecurity. We review key game-theoretic frameworks (e.g., static, dynamic, Bayesian, and signaling games) and solution concepts. We then examine how LLM agents can enhance cyber defense and introduce LLM-driven games that embed reasoning into AI agents. Finally, we explore multi-agent workflows and coordination games, outlining how this convergence fosters secure, intelligent, and adaptive cyber systems.