Red Teaming Quantum-Resistant Cryptographic Standards: A Penetration Testing Framework Integrating AI and Quantum Security
It addresses security risks in quantum networks for cybersecurity practitioners, offering a scalable methodology that is incremental in integrating AI into existing quantum security practices.
This study tackled the problem of evaluating vulnerabilities in quantum cryptographic protocols by developing an AI-driven red teaming framework, demonstrating that AI can effectively simulate adversarial attacks and identify latent vulnerabilities through automated exploit simulations and protocol fuzzing.
This study presents a structured approach to evaluating vulnerabilities within quantum cryptographic protocols, focusing on the BB84 quantum key distribution method and National Institute of Standards and Technology (NIST) approved quantum-resistant algorithms. By integrating AI-driven red teaming, automated penetration testing, and real-time anomaly detection, the research develops a framework for assessing and mitigating security risks in quantum networks. The findings demonstrate that AI can be effectively used to simulate adversarial attacks, probe weaknesses in cryptographic implementations, and refine security mechanisms through iterative feedback. The use of automated exploit simulations and protocol fuzzing provides a scalable means of identifying latent vulnerabilities, while adversarial machine learning techniques highlight novel attack surfaces within AI-enhanced cryptographic processes. This study offers a comprehensive methodology for strengthening quantum security and provides a foundation for integrating AI-driven cybersecurity practices into the evolving quantum landscape.