Red teaming quantum-resistant cryptographic standards: a penetration testing framework integrating AI and quantum security

Abstract

Quantum computing and artificial intelligence (AI) are transforming the cybersecurity landscape, dictating a reassessment of cryptographic resilience in the face of emerging threats. 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. NIST is recognized globally as the leading institution in this domain, and the algorithms are selected through a decade-long process. By integrating AI-driven red teaming, automated penetration testing, and real-time anomaly detection, the research develops a robust 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.