Abstract This paper presents a formal adversarial methodology, Quantum Adversarial Physics (QAP), for red teaming quantum communication systems, with a focus on Quantum Key Distribution (QKD) and related quantum protocols. Rather than treating quantum mechanical principles as immutable security guarantees, QAP operationalises them as adversarial variables: coherence times, entanglement correlations, and entropy flows are each reframed as exploitable attack surfaces. The methodology introduces a structured red team lifecycle, a formally grounded threat model, a phase-by-phase attack taxonomy with feasibility assessment, an attack–defence correspondence table, and a principled vulnerability scoring system. This work provides practitioners with a repeatable, physics-aware process for assessing the real-world resilience of quantum systems.
Abstract This paper presents a formal adversarial methodology, Quantum Adversarial Physics (QAP), for red teaming quantum communication systems, with a focus on Quantum Key Distribution (QKD) and related quantum protocols. Rather than treating quantum mechanical principles as immutable security guarantees, QAP operationalises them as adversarial variables: coherence times, entanglement correlations, and entropy flows are each reframed as exploitable attack surfaces. The methodology introduces a structured red team lifecycle, a formally grounded threat model, a phase-by-phase attack taxonomy with feasibility assessment, an attack–defence correspondence table, and a principled vulnerability scoring system. This work provides practitioners with a repeatable, physics-aware process for assessing the real-world resilience of quantum systems.
Quantum key distribution (QKD) and postquantum cryptographic systems rely on physical properties for security guarantees. However, the weaponization of quantum phenomena by adversaries necessitates a comprehensive theoretical framework grounding attack surfaces in fundamental physics. We present a unified framework integrating Bose-Einstein statistics, Schrödinger wave mechanics, Heisenberg uncertainty relations, Noether’s symmetry theorem, Jackson’s semiconductor physics, and Dirac’s quantum field formalism to characterize, predict, and detect physical-layer attacks on quantum cryptographic systems. This framework reveals that all attacks manifest as violations of fundamental symmetries, enabling comprehensive detection through conserved quantity monitoring. I demonstrate how material constraints defined by semiconductor physics bound the parameter space of exploits, while Noether’s theorem provides the mathematical foundation for complete attack attribution. The synthesis yields both theoretical attack bounds and practical detection methodologies, validated against documented quantum cryptanalyses including detector blinding, photon-number splitting, decoherence engineering, and spectral separation attacks.
Quantum computing and artificial intelligence 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 recognised globally as the leading institution in this domain, and the algorithms are selected though a decadelong 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 AIenhanced cryptographic processes. This study offers a comprehensive methodology for strengthening quantum security and provides a foundation for integrating AIdriven cybersecurity practices into the evolving quantum landscape.
Quantum key distribution (QKD) is widely regarded as a highly secure quantum encryption approach since it applies theoretical principles of quantum mechanics as a medium of security, unlike classical encryption processes that rely on complex mathematics. However, practical QKD systems are vulnerable due to imperfect photon source hardware, communication channel, and detector implementation. These hardware imperfections open side channels that an adversary can exploit without violating principles of quantum mechanics. This review provides a comprehensive overview of the current QKD protocols, quantum hacking strategies and their operating principles, vulnerable hardware components and their mitigation techniques. This paper further investigates how state-of-the-art paradigms based on artificial intelligence (AI) and machine learning (ML), along with key subfields—such as deep learning (DL), deep reinforcement learning (DRL), and quantum machine learning (QML)—are reshaping the domain. In particular, this paper analyzes their dual roles as enablers of sophisticated attacks and as instruments for attack detection and mitigation. By jointly addressing the challenges in quantum communication and adaptive learning, as well as attack and detection architectures, this survey elucidates both the capabilities and the limitations of contemporary attack-detection mechanisms in the field. This survey seeks to establish a unified framework that conceptually integrates physical-layer quantum hacking with AI-/ML-based security, emphasizing their dual function as both an intelligent defensive layer and a potential tool for advanced threats.
This chapter investigates the security dynamics of Quantum Key Distribution (QKD) by contrasting it with classical security methods and examining the resilience of QKD against various theoretical and practical threats. Initially, it highlights the vulnerabilities of traditional cryptographic systems to quantum computing, noting the potential of quantum algorithms like Shor's to compromise widely used encryption standards such as RSA and Elliptic Curve Cryptography (ECC). It then introduces quantum security techniques, emphasizing QKD's foundational reliance on quantum mechanics' principles—such as the uncertainty principle, the no-cloning theorem, and entanglement—to establish a security paradigm secure against both classical and quantum adversaries. The chapter provides a detailed analysis of common QKD protocols, evaluating their security mechanisms, resistance to eavesdropping, and practical implementation challenges. It discusses the operational principles underlying these protocols and assesses their potential in practical applications, given the current technological constraints and security requirements. Various attacks on QKD, including side-channel and eavesdropping attacks, are reviewed to outline the ongoing necessity for advanced security measures beyond theoretical guarantees. Further, the discussion extends to quantum-resistant algorithms and protocols, which are designed to withstand the power of quantum computers by leveraging problems deemed computationally hard for both classical and quantum systems. It elaborates on the infrastructural needs and challenges of implementing a quantum-resistant framework, critical for a secure transition into the quantum era. This chapter concludes by reflecting on the broader implications of integrating quantum security methodologies like QKD into modern cryptographic practices. It argues for the evolution of security strategies to address emerging risks effectively and explains the importance of continual advancements in quantum technologies and security protocols to safeguard data against the progressively sophisticated landscape of cyber threats.
With the rapid advancement of artificial intelligence (AI) and quantum computing, cybersecurity threats have evolved, giving rise to adversarial quantum attacks. These attacks exploit the vulnerabilities of AI models using quantum algorithms, posing a significant risk to data security, model robustness, and decision-making systems. This paper presents a comprehensive review of adversarial quantum attacks on AI, analyzing their mechanisms, potential impacts, and countermeasures. It explores how quantum computing can enhance adversarial attacks by accelerating the generation of adversarial examples, breaking cryptographic protections, and undermining AI model integrity. Additionally, the study examines different attack vectors, including quantum-enhanced adversarial perturbations, quantum machine learning (QML) vulnerabilities, and quantum decryption threats. The paper also discusses defensive strategies such as quantum-resistant AI models, quantum cryptographic defenses, and hybrid quantum-classical security frameworks that can mitigate these risks. By evaluating existing research and emerging trends, this review provides insights into the growing intersection of AI security and quantum computing. The findings emphasize the urgent need for robust quantum-aware AI security frameworks to safeguard AI-driven systems in the quantum era. Future research directions include developing quantum-adaptive AI models, post-quantum cryptographic techniques, and real-world applications of quantum-safe AI architectures. This review aims to contribute to the ongoing discourse on ensuring AI resilience against adversarial quantum threats, paving the way for a secure and quantum-resistant future.
Quantum communications are based on the law of physics for information security and the implications for this form of future information security enabled by quantum science has to be studied. Physics-based vulnerabilities may exist due to the inherent physics properties and behavior of quantum technologies such as Quantum Key Distribution (QKD), thus resulting in new threats that may emerge with attackers exploiting the physics-based vulnerabilities. There were many studies and experiments done to demonstrate the threat of physics-based attacks on quantum links. However, there is a lack of a framework that provides a common language to communicate about the threats and type of adversaries being dealt with for physics-based attacks. This paper is a review of physics-based attacks that were being investigated and attempt to initialize a framework based on the attack objectives and methodologies, referencing the concept from the well-established MITRE ATT&CK, therefore pioneering the classification of Indicator of Compromises (IoCs) for physics-based attacks. This paper will then pave the way for future work in the development of a forensic tool for the different classification of IoCs, with the methods of evidence collections and possible points of extractions for analysis being further investigated.