# Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations arXiv: https://arxiv.org/abs/2512.22883 ยท HTML: https://mlsanity.com/paper/2512.22883/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. - Builds on 39 of its 79 references that are in the map. - First posted on arXiv 28 Dec 2025. ## Placed in - LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/) ## Builds on (most cited first, 30 of 39) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning](https://mlsanity.com/paper/1801.08917/) (2018) - [PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation](https://mlsanity.com/paper/2407.17788/) (2024) - [Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges](https://mlsanity.com/paper/2510.23883/) (2025) - [The Confluence of Networks, Games and Learning](https://mlsanity.com/paper/2105.08158/) (2021) - [Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classrooms](https://mlsanity.com/paper/2406.07571/) (2024) - [A Dynamic Games Approach to Proactive Defense Strategies against Advanced Persistent Threats in Cyber-Physical Systems](https://mlsanity.com/paper/1906.09687/) (2019) - [Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Games](https://mlsanity.com/paper/2402.18781/) (2024) - [Automated Security Response through Online Learning with Adaptive Conjectures](https://mlsanity.com/paper/2402.12499/) (2024) - [Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approach](https://mlsanity.com/paper/2303.03349/) (2023) - [Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats](https://mlsanity.com/paper/2507.10621/) (2025) - [On the Price of Transparency: A Comparison between Overt Persuasion and Covert Signaling](https://mlsanity.com/paper/2304.00096/) (2023) - [Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories](https://mlsanity.com/paper/2404.01205/) (2024) - [Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks](https://mlsanity.com/paper/2410.17431/) (2024) - [Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare](https://mlsanity.com/paper/2403.10570/) (2024) - [A First Order Meta Stackelberg Method for Robust Federated Learning](https://mlsanity.com/paper/2306.13800/) (2023) - [Revisiting Game-Theoretic Control in Socio-Technical Networks: Emerging Design Frameworks and Contemporary Applications](https://mlsanity.com/paper/2411.01794/) (2024) - [Dynamic Bayesian Games for Adversarial and Defensive Cyber Deception](https://mlsanity.com/paper/1809.02013/) (2018) - [Decision-Dominant Strategic Defense Against Lateral Movement for 5G Zero-Trust Multi-Domain Networks](https://mlsanity.com/paper/2310.01675/) (2023) - [Reasoning and Behavioral Equilibria in LLM-Nash Games: From Mindsets to Actions](https://mlsanity.com/paper/2507.08208/) (2025) - [Toward a Dynamic Stackelberg Game-Theoretic Framework for Agentic AI Defense Against LLM Jailbreaking](https://mlsanity.com/paper/2507.08207/) (2025) - [Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization](https://mlsanity.com/paper/2508.14385/) (2025) - [Cybersecurity in Robotics: Challenges, Quantitative Modeling, and Practice](https://mlsanity.com/paper/2103.05789/) (2021) - [Informational Design of Dynamic Multi-Agent System](https://mlsanity.com/paper/2105.03052/) (2021) - [Disentangling Resilience from Robustness: Contextual Dualism, Interactionism, and Game-Theoretic Paradigms](https://mlsanity.com/paper/2403.06299/) (2024) - [From Texts to Shields: Convergence of Large Language Models and Cybersecurity](https://mlsanity.com/paper/2505.00841/) (2025) - [Designing Policies for Truth: Combating Misinformation with Transparency and Information Design](https://mlsanity.com/paper/2304.08588/) (2023) ## Built on by (most cited first, 4 of 4) - [The Internet of Agentic AI: Communication, Coordination, and Collective Intelligence at Scale](https://mlsanity.com/paper/2606.12835/) (2026) - [Agentic Incident Response through Digital Twin-Enhanced Multiscale Planning](https://mlsanity.com/paper/2608.02422/) (2026) - [Internet of Agentic Things: Networked AI Agents for Closed-Loop IoT Orchestration](https://mlsanity.com/paper/2607.12662/) (2026) - [Hierarchical Agentic Incident Response with Digital-Twin-Validated Attack Inference](https://mlsanity.com/paper/2608.15016/) (2026) ## Abstract (arXiv) Cybersecurity is being fundamentally reshaped by foundation-model-based artificial intelligence. Large language models now enable autonomous planning, tool orchestration, and strategic adaptation at scale, challenging security architectures built on static rules, perimeter defenses, and human-centered workflows. This chapter argues for a shift from prevention-centric security toward agentic cyber resilience. Rather than seeking perfect protection, resilient systems must anticipate disruption, maintain critical functions under attack, recover efficiently, and learn continuously. We situate this shift within the historical evolution of cybersecurity paradigms, culminating in an AI-augmented paradigm where autonomous agents participate directly in sensing, reasoning, action, and adaptation across cyber and cyber-physical systems. We then develop a system-level framework for designing agentic AI workflows. A general agentic architecture is introduced, and attacker and defender workflows are analyzed as coupled adaptive processes, and game-theoretic formulations are shown to provide a unifying design language for autonomy allocation, information flow, and temporal composition. Case studies in automated penetration testing, remediation, and cyber deception illustrate how equilibrium-based design enables system-level resiliency design.