# Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats arXiv: https://arxiv.org/abs/2507.10621 ยท HTML: https://mlsanity.com/paper/2507.10621/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. - Builds on 45 of its 79 references that are in the map. - First posted on arXiv 14 Jul 2025. ## Placed in - LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/) ## Builds on (most cited first, 30 of 45) - [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023) - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023) - [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023) - [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023) - [Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models](https://mlsanity.com/paper/2401.01335/) (2024) - [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023) - [ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://mlsanity.com/paper/2309.13007/) (2023) - [Self-Play Preference Optimization for Language Model Alignment](https://mlsanity.com/paper/2405.00675/) (2024) - [Nash Learning from Human Feedback](https://mlsanity.com/paper/2312.00886/) (2023) - [Chain of Agents: Large Language Models Collaborating on Long-Context Tasks](https://mlsanity.com/paper/2406.02818/) (2024) - [Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences](https://mlsanity.com/paper/2404.03715/) (2024) - [A Minimaximalist Approach to Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2401.04056/) (2024) - [MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution](https://mlsanity.com/paper/2403.17927/) (2024) - [Panacea: Pareto Alignment via Preference Adaptation for LLMs](https://mlsanity.com/paper/2402.02030/) (2024) - [Online Iterative Reinforcement Learning from Human Feedback with General Preference Model](https://mlsanity.com/paper/2402.07314/) (2024) - [The Confluence of Networks, Games and Learning](https://mlsanity.com/paper/2105.08158/) (2021) - [Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium](https://mlsanity.com/paper/2503.10990/) (2025) - [A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy](https://mlsanity.com/paper/1712.05441/) (2017) - [Scalable Reinforcement Post-Training Beyond Static Human Prompts: Evolving Alignment via Asymmetric Self-Play](https://mlsanity.com/paper/2411.00062/) (2024) - [Mechanism Design for LLM Fine-tuning with Multiple Reward Models](https://mlsanity.com/paper/2405.16276/) (2024) - [A Dynamic Games Approach to Proactive Defense Strategies against Advanced Persistent Threats in Cyber-Physical Systems](https://mlsanity.com/paper/1906.09687/) (2019) - [Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models](https://mlsanity.com/paper/2308.04399/) (2023) - [Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories](https://mlsanity.com/paper/2404.01205/) (2024) - [Efficient Model-agnostic Alignment via Bayesian Persuasion](https://mlsanity.com/paper/2405.18718/) (2024) - [Revisiting Game-Theoretic Control in Socio-Technical Networks: Emerging Design Frameworks and Contemporary Applications](https://mlsanity.com/paper/2411.01794/) (2024) - [Modeling and Analysis of Leaky Deception using Signaling Games with Evidence](https://mlsanity.com/paper/1804.06831/) (2018) - [Decoy Allocation Games on Graphs with Temporal Logic Objectives](https://mlsanity.com/paper/2010.01208/) (2020) - [Verbalized Bayesian Persuasion](https://mlsanity.com/paper/2502.01587/) (2025) - [Disentangling Resilience from Robustness: Contextual Dualism, Interactionism, and Game-Theoretic Paradigms](https://mlsanity.com/paper/2403.06299/) (2024) - [Dynamic Contract Design for Systemic Cyber Risk Management of Interdependent Enterprise Networks](https://mlsanity.com/paper/1908.04431/) (2019) ## Built on by (most cited first, 11 of 11) - [A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes](https://mlsanity.com/paper/2601.05293/) (2026) - [Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations](https://mlsanity.com/paper/2512.22883/) (2025) - [Stable Agentic Control: Tool-Mediated LLM Architecture for Autonomous Cyber Defense](https://mlsanity.com/paper/2605.03034/) (2026) - [Insurance of Agentic AI](https://mlsanity.com/paper/2606.05449/) (2026) - [The Evolution of Agentic AI in Cybersecurity: From Single LLM Reasoners to Multi-Agent Systems and Autonomous Pipelines](https://mlsanity.com/paper/2512.06659/) (2025) - [The Internet of Agentic AI: Communication, Coordination, and Collective Intelligence at Scale](https://mlsanity.com/paper/2606.12835/) (2026) - [Guarding Against Malicious Biased Threats (GAMBiT) Experiments: Revealing Cognitive Bias in Human-Subjects Red-Team Cyber Range Operations](https://mlsanity.com/paper/2508.20963/) (2025) - [Collaboration and Conflict between Humans and Language Models through the Lens of Game Theory](https://mlsanity.com/paper/2509.04847/) (2025) - [Agentomics: Economic Foundations for the Valuation, Attribution, and Pricing of AI Agents in Human-AI Workflows](https://mlsanity.com/paper/2606.14769/) (2026) - [AI Tokenomics: The Economics of Tokens, Computation, and Pricing in Foundation Models](https://mlsanity.com/paper/2606.24616/) (2026) - [LLM Semantic Signaling Game and Mechanism Design: Systematic Blindness, Awareness Shaping, and Mindset Dynamics](https://mlsanity.com/paper/2606.29113/) (2026) ## Abstract (arXiv) 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.