Game of Prompts: How Game Theory and Agentic LLMs Are Rewriting Cybersecurity

In today’s threat landscape, cybersecurity is no longer a battle of scripts and firewalls. It’s a war of minds. And with the rise of intelligent agents powered by Large Language Models (LLMs), we are now entering a new era where cyber defense becomes not just technical but deeply strategic. The paper “Game Theory Meets LLM and Agentic AI” by Quanyan Zhu provides one of the most profound frameworks yet for understanding this shift.

When Rationality Meets Reasoning

Traditional game theory is about rational agents optimizing outcomes based on known rules. But cybersecurity defies such order. Attackers are deceptive, defenders are under-informed, and users are unpredictable. What happens when the agents in these games aren’t modeled as perfect optimizers, but as language-powered reasoners that simulate beliefs, intentions, and narratives?

This is where LLMs upend classical assumptions. Instead of encoding strategy as mathematical optimization, Zhu introduces the idea of LLM-based Nash and Stackelberg games, where agents use prompts as reasoning scaffolds to generate actions. The game is no longer played at the level of direct moves; it’s played in the space of prompts and reasoning patterns.

This shift isn’t academic. It means defenders can now build agents that simulate not only what attackers might do, but how they might think. Think of a red-teaming LLM agent generating phishing emails based on cognitive biases, or a blue-teaming agent simulating user responses to deception. The battlefield is the prompt space.

Cybersecurity as Multi-Agent Drama

Zhu doesn’t stop at individual agents. He maps entire multi-agent workflows using game-theoretic structures. These include:

• Chain workflows: Sequential LLM agents (e.g., alert parser → anomaly detector → threat assessor).
• Star workflows: A central orchestrator coordinating specialized LLM agents (e.g., compliance, network, social).
• Feedback workflows: Closed-loop adaptive defense where agents monitor, plan, act, and learn.

Each architecture is analyzed not only as a workflow but as a layered game system. For example, feedback workflows are modeled as recursive signaling games, where each agent’s belief updating affects future rounds. The concept of a Gestalt Game is introduced to capture this emergent, holistic coordination that transcends pairwise modeling.

In contrast to past Cognaptus Insights that treated multi-agent LLM systems as engineering constructs, Zhu provides the missing economic and strategic backbone: how do these agents align goals, negotiate uncertainty, and resolve internal conflict? It’s a leap from coordination to cognition.

From Equilibrium to Epistemic Combat

One of the most fascinating turns in the paper is the reframing of deception and trust in LLM-powered environments. Cyber deception has long been modeled using signaling games. But with LLMs, both sender and receiver are now generative, capable of simulating belief updates and adaptive strategies through language.

This enables a new class of LLM-Stackelberg games where the leader’s prompt is optimized not for content but for anticipated interpretation. For example, a defender may design a prompt that generates decoy network activity crafted to influence the attacker’s own LLM-based reasoning model.

The shift here is subtle but profound: we move from equilibrium as static balance to equilibrium as ongoing epistemic manipulation. It echoes adversarial prompt engineering and RAG-based retrieval in red-teaming tools, but puts it under a formal, game-theoretic light.

Prompt Engineering as Mechanism Design

The final implication is perhaps the most radical: prompt engineering isn’t just about getting better outputs—it’s a form of mechanism design. In classical economics, mechanism design is the art of crafting rules so that self-interested agents produce desirable outcomes. In LLM-based MAS (multi-agent systems), prompts are the rules.

Thus, managing a cybersecurity system becomes about managing the prompt space:

• How to shape agents’ belief priors via prompt templates
• How to tune prompt diversity to avoid converging on brittle equilibria
• How to design workflows that degrade gracefully when agents hallucinate

In earlier Cognaptus articles, we explored LLM workflows from a modularity and interpretability lens. Zhu’s work elevates this to strategic design. It’s not about hardcoding decisions but nudging cognition.

A Final Word: Strategic Fluency in AI

The real contribution of Zhu’s framework isn’t just new models. It’s a call to reimagine AI not as a collection of tools, but as a community of agents reasoning under pressure. For cybersecurity, this means designing agents that do more than detect and respond—they simulate, anticipate, persuade, and adapt.

We often ask: will AI surpass human cognition? Zhu offers a more actionable challenge: can we design agentic cognition that understands and strategically shapes the cognition of others? In cybersecurity, that may be the difference between automation and advantage.

Cognaptus: Automate the Present, Incubate the Future