arXiv subjects

Cristóbal R. J. Veas Chavez

Publications and source records attributed to Cristóbal R. J. Veas Chavez.

4 recordsLinked to original sources

arXiv2026

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

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 feeds a concise digest back into the LLM loop \emph{guiding} the agent's actions. Across five real-world exercises, G-CTR matches 70--90% of expert graph structure while running 60--245x faster and over 140x cheaper than manual analysis. In a 44-run cyber-range, adding the digest lifts success from 20.0% to 42.9%, cuts cost-per-success by 2.7x, and reduces behavioral variance by 5.2x. In Attack-and-Defense exercises, a shared digest produces the Purple agent, winning roughly 2:1 over the LLM-only baseline and 3.7:1 over independently guided teams. This closed-loop guidance is what produces the breakthrough: it reduces ambiguity, collapses the LLM's search space, suppresses hallucinations, and keeps the model anchored to the most relevant parts of the problem, yielding large gains in success rate, consistency, and reliability.

cs.CR↗

arXiv2025

Cybersecurity AI: The World's Top AI Agent for Security Capture-the-Flag (CTF)

Are Capture-the-Flag competitions obsolete? In 2025, Cybersecurity AI (CAI) systematically conquered some of the world's most prestigious hacking competitions, achieving Rank #1 at multiple events and consistently outperforming thousands of human teams. Across five major circuits-HTB's AI vs Humans, Cyber Apocalypse (8,129 teams), Dragos OT CTF, UWSP Pointer Overflow, and the Neurogrid CTF showdown-CAI demonstrated that Jeopardy-style CTFs have become a solved game for well-engineered AI agents. At Neurogrid, CAI captured 41/45 flags to claim the $50,000 top prize; at Dragos OT, it sprinted 37% faster to 10K points than elite human teams; even when deliberately paused mid-competition, it maintained top-tier rankings. Critically, CAI achieved this dominance through our specialized alias1 model architecture, which delivers enterprise-scale AI security operations at unprecedented cost efficiency and with augmented autonomy-reducing 1B token inference costs from $5,940 to just $119, making continuous security agent operation financially viable for the first time. These results force an uncomfortable reckoning: if autonomous agents now dominate competitions designed to identify top security talent at negligible cost, what are CTFs actually measuring? This paper presents comprehensive evidence of AI capability across the 2025 CTF circuit and argues that the security community must urgently transition from Jeopardy-style contests to Attack & Defense formats that genuinely test adaptive reasoning and resilience-capabilities that remain uniquely human, for now.

cs.CR↗

arXiv2025

Cybersecurity AI Benchmark (CAIBench): A Meta-Benchmark for Evaluating Cybersecurity AI Agents

Cybersecurity spans multiple interconnected domains, complicating the development of meaningful, labor-relevant benchmarks. Existing benchmarks assess isolated skills rather than integrated performance. We find that pre-trained knowledge of cybersecurity in LLMs does not imply attack and defense abilities, revealing a gap between knowledge and capability. To address this limitation, we present the Cybersecurity AI Benchmark (CAIBench), a modular meta-benchmark framework that allows evaluating LLM models and agents across offensive and defensive cybersecurity domains, taking a step towards meaningfully measuring their labor-relevance. CAIBench integrates five evaluation categories, covering over 10,000 instances: Jeopardy-style CTFs, Attack and Defense CTFs, Cyber Range exercises, knowledge benchmarks, and privacy assessments. Key novel contributions include systematic simultaneous offensive-defensive evaluation, robotics-focused cybersecurity challenges (RCTF2), and privacy-preserving performance assessment (CyberPII-Bench). Evaluation of state-of-the-art AI models reveals saturation on security knowledge metrics (~70\% success) but substantial degradation in multi-step adversarial (A\&D) scenarios (20-40\% success), or worse in robotic targets (22\% success). The combination of framework scaffolding and LLM model choice significantly impacts performance; we find that proper matches improve up to 2.6$\times$ variance in Attack and Defense CTFs. These results demonstrate a pronounced gap between conceptual knowledge and adaptive capability, emphasizing the need for a meta-benchmark.

cs.CR↗

arXiv2025

CAI Fluency: A Framework for Cybersecurity AI Fluency

This work introduces CAI Fluency, an an educational platform of the Cybersecurity AI (CAI) framework dedicated to democratizing the knowledge and application of cybersecurity AI tools in the global security community. The main objective of the CAI framework is to accelerate the widespread adoption and effective use of artificial intelligence-based cybersecurity solutions, pathing the way to vibe-hacking, the cybersecurity analogon to vibe-coding. CAI Fluency builds upon the Framework for AI Fluency, adapting its three modalities of human-AI interaction and four core competencies specifically for cybersecurity applications. This theoretical foundation ensures that practitioners develop not just technical skills, but also the critical thinking and ethical awareness necessary for responsible AI use in security contexts. This technical report serves as a white-paper, as well as detailed educational and practical guide that helps users understand the principles behind the CAI framework, and educates them how to apply this knowledge in their projects and real-world security contexts.

cs.CR↗