paperJanuary 2026Unreviewed

Cybersecurity AI: A Game-Theoretic AI for Guiding Attack and Defense

V. Vilches, Mar'ia Sanz-G'omez, Francesco Balassone, Stefan Rass, Lidia Salas Espejo, Benjamin Jablonski, Luis Javier Navarrete-Lozano, Maite del Mundo de Torres, Cristóbal R. J. Veas Chavez

arXiv.org

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Abstract

AI-driven penetration testing now executes thousands of actions per hour but still lacks the strategic intuition humans apply in competitive security. To build cybersecurity superintelligence --Cybersecurity AI exceeding best human capability-such strategic intuition must be embedded into agentic reasoning processes. We present Generative Cut-the-Rope (G-CTR), a game-theoretic guidance layer that extracts attack graphs from agent's context, computes Nash equilibria with effort-aware scoring, and

Cite

@misc{vilches2026cybersecurity,
  title = {{Cybersecurity AI: A Game-Theoretic AI for Guiding Attack and Defense}},
  author = {V. Vilches and Mar'ia Sanz-G'omez and Francesco Balassone and Stefan Rass and Lidia Salas Espejo and Benjamin Jablonski and Luis Javier Navarrete-Lozano and Maite del Mundo de Torres and Cristóbal R. J. Veas Chavez},
  year = {2026},
  month = jan,
  eprint = {2601.05887},
  archivePrefix = {arXiv},
  doi = {10.48550/arXiv.2601.05887},
  url = {https://www.semanticscholar.org/paper/3316e5185e695a00f15eb372c05c6cab5ef3fbfa}
}