# Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?

arXiv: https://arxiv.org/abs/2508.05670 · HTML: https://mlsanity.com/paper/2508.05670/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 65 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 21 of 21)

- [Playing repeated games with Large Language Models](https://mlsanity.com/paper/2305.16867/) (2023)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [LLM Multi-Agent Systems: Challenges and Open Problems](https://mlsanity.com/paper/2402.03578/) (2024)
- [Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis](https://mlsanity.com/paper/2312.05488/) (2023)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma?](https://mlsanity.com/paper/2406.13605/) (2024)
- [Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing](https://mlsanity.com/paper/2309.05898/) (2023)
- [Towards Agentic AI Networking in 6G: A Generative Foundation Model-as-Agent Approach](https://mlsanity.com/paper/2503.15764/) (2025)
- [FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory](https://mlsanity.com/paper/2504.14325/) (2025)
- [Empirical Game-Theoretic Analysis: A Survey](https://mlsanity.com/paper/2403.04018/) (2024)
- [Generative AI for Game Theory-based Mobile Networking](https://mlsanity.com/paper/2404.09699/) (2024)
- [Media and responsible AI governance: a game-theoretic and LLM analysis](https://mlsanity.com/paper/2503.09858/) (2025)
- [Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents](https://mlsanity.com/paper/2504.08640/) (2025)
- [Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation](https://mlsanity.com/paper/2403.09510/) (2024)
- [Large Language Models' Detection of Political Orientation in Newspapers](https://mlsanity.com/paper/2406.00018/) (2024)
- [A Robust Stackelberg Game for Cyber-Security Investment in Networked Control Systems](https://mlsanity.com/paper/2103.16611/) (2021)
- [Exploring Equilibrium Strategies in Network Games with Generative AI](https://mlsanity.com/paper/2405.08289/) (2024)
- [The Impact of Revealing Large Language Model Stochasticity on Trust, Reliability, and Anthropomorphization](https://mlsanity.com/paper/2503.16114/) (2025)
- [Co-evolutionary Dynamics of Attack and Defence in Cybersecurity](https://mlsanity.com/paper/2505.19338/) (2025)
- [A Zero-Sum Game Framework for Optimal Sensor Placement in Uncertain Networked Control Systems under Cyber-Attacks](https://mlsanity.com/paper/2210.04091/) (2022)

## Built on by (most cited first, 4 of 4)

- [Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamics](https://mlsanity.com/paper/2512.07462/) (2025)
- [GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory](https://mlsanity.com/paper/2602.12316/) (2026)
- [How Personas Can Influence Agents to Play Split or Steal](https://mlsanity.com/paper/2607.05398/) (2026)
- [When Numbers Start Talking: Numerical Signalling and Strategic Behaviour Among LLMs](https://mlsanity.com/paper/2610.03033/) (2026)

## Abstract (arXiv)

Game theory has long served as a foundational tool in cybersecurity to test, predict, and design strategic interactions between attackers and defenders. The recent advent of Large Language Models (LLMs) offers new tools and challenges for the security of computer systems; In this work, we investigate whether classical game-theoretic frameworks can effectively capture the behaviours of LLM-driven actors and bots. Using a reproducible framework for game-theoretic LLM agents, we investigate two canonical scenarios -- the one-shot zero-sum game and the dynamic Prisoner's Dilemma -- and we test whether LLMs converge to expected outcomes or exhibit deviations due to embedded biases. Our experiments involve four state-of-the-art LLMs and span five natural languages, English, French, Arabic, Vietnamese, and Mandarin Chinese, to assess linguistic sensitivity. For both games, we observe that the final payoffs are influenced by agents characteristics such as personality traits or knowledge of repeated rounds. Moreover, we uncover an unexpected sensitivity of the final payoffs to the choice of languages, which should warn against indiscriminate application of LLMs in cybersecurity applications and call for in-depth studies, as LLMs may behave differently when deployed in different countries. We also employ quantitative metrics to evaluate the internal consistency and cross-language stability of LLM agents, to help guide the selection of the most stable LLMs and optimising models for secure applications.