# Strategic commitments shape collective cybersecurity under AI inequality

arXiv: https://arxiv.org/abs/2605.09415 · HTML: https://mlsanity.com/paper/2605.09415/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 68 references that are in the map.
- First posted on arXiv 10 May 2026.

## Builds on (most cited first, 15 of 15)

- [Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives](https://mlsanity.com/paper/2311.09227/) (2023)
- [Statistical physics of human cooperation](https://mlsanity.com/paper/1705.07161/) (2017)
- [Artificial Intelligence Development Races in Heterogeneous Settings](https://mlsanity.com/paper/2012.15234/) (2020)
- [Optimizing Opinions with Stubborn Agents](https://mlsanity.com/paper/1806.11253/) (2018)
- [Evolutionary mechanisms that promote cooperation may not promote social welfare](https://mlsanity.com/paper/2408.05373/) (2024)
- [Asymmetry by Design: Boosting Cyber Defenders with Differential Access to AI](https://mlsanity.com/paper/2506.02035/) (2025)
- [Statistical physics of crime: A review](https://mlsanity.com/paper/1411.1743/) (2014)
- [Emergence of Cooperation and Commitment in Optional Prisoner's Dilemma](https://mlsanity.com/paper/2508.06702/) (2025)
- [Random evolutionary games and random polynomials](https://mlsanity.com/paper/2304.13831/) (2023)
- [Unaware, Unfunded and Uneducated: A Systematic Review of SME Cybersecurity](https://mlsanity.com/paper/2309.17186/) (2023)
- [Subsidizing a New Technology: An Impulse Stackelberg Game Approach](https://mlsanity.com/paper/2407.05393/) (2024)
- [A Non-Zero-Sum Game Model for Optimal Cyber Defense Strategies](https://mlsanity.com/paper/2505.16049/) (2025)
- [Perceptual Rationality: An Evolutionary Game Theory of Perceptually Rational Decision-Making](https://mlsanity.com/paper/2506.17724/) (2025)
- [Evolutionary Cooperation with Game Transitions via Markov Decision Chain in Networked Population](https://mlsanity.com/paper/2512.18972/) (2025)
- [Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems](https://mlsanity.com/paper/2603.24742/) (2026)

## Built on by (most cited first, 1 of 1)

- [Reputation-driven Cooperation in Lattice-based Decentralized Federated Learning through Evolutionary Game Theory](https://mlsanity.com/paper/2608.01197/) (2026)

## Abstract (arXiv)

The growing integration of AI into cybersecurity is reshaping the balance between attackers and defenders. When access to advanced AI-enabled defence tools is uneven, resource-limited defenders may be unable to adopt effective protection, creating persistent system vulnerabilities. We study the impact of differential AI access using an evolutionary game-theoretic model in a finite population. We first show that when high-capability defence is costly, the population is driven toward low-cost, weak-defence behaviour, sustaining attacks and weakening long-run security. To address this problem, we introduce differential access to AI defence tools by allowing defenders to choose between low- and high-capability protection based on their resources. We then examine the role of a small group of committed defenders who always adopt strong defence and influence others through social learning. Although commitment increases the prevalence of strong defence, it alone cannot stabilise secure outcomes due to high defence costs. We therefore incorporate a targeted subsidy to remove the cost disadvantage from committed defenders. Our analysis shows that subsidised commitment significantly increases strong defence adoption, suppresses successful attacks, and improves overall system resilience. Simulations across a broad parameter space confirm that subsidies consistently outperform commitment alone. In addition, social-welfare analysis shows improved defender outcomes while keeping attacker gains low. These findings suggest that targeted support for key defenders can be an effective mechanism for stabilising cybersecurity in AI-driven environments and provide a theoretical bridge between cybersecurity policy, AI governance, and strategic allocation of defensive AI capabilities.