The convergence of large language model (LLM) deployments and the approaching quantum computing era creates unprecedented security challenges that no single existing tool addresses holistically. We present QCrypton, a unified security platform that integrates an 11-scanner AI threat detection engine with a post-quantum cryptographic assessment framework, fault-tolerant quantum computer (FTQC) attack cost estimation, and a zero-native-dependency cryptographic toolkit. QCrypton bridges the gap between AI security and quantum readiness by providing bidirectional integration: threat scanners invoke quantum vulnerability assessments in real time, while detected secrets are encrypted via BB84 quantum key distribution simulation before reporting. We describe the system architecture, evaluate scanner accuracy across 22+ prompt injection patterns and 8 MCP tool poisoning vectors, and demonstrate the FTQC cost estimation engine across 28 cryptographic algorithms. QCrypton is implemented in Node.js with zero native cryptographic dependencies, using only platform-native OpenSSL primitives, eliminating supply-chain risk in the cryptographic layer.
The convergence of large language model (LLM) deployments and the approaching quantum computing era creates unprecedented security challenges that no single existing tool addresses holistically. We present QCrypton, a unified security platform that integrates an 11-scanner AI threat detection engine with a post-quantum cryptographic assessment framework, fault-tolerant quantum computer (FTQC) attack cost estimation, and a zero-native-dependency cryptographic toolkit. QCrypton bridges the gap between AI security and quantum readiness by providing bidirectional integration: threat scanners invoke quantum vulnerability assessments in real time, while detected secrets are encrypted via BB84 quantum key distribution simulation before reporting. We describe the system architecture, evaluate scanner accuracy across 22+ prompt injection patterns and 8 MCP tool poisoning vectors, and demonstrate the FTQC cost estimation engine across 28 cryptographic algorithms. QCrypton is implemented in Node.js with zero native cryptographic dependencies, using only platform-native OpenSSL primitives, eliminating supply-chain risk in the cryptographic layer.
Abstract Quantum computing technology is driving transformative changes not only in the field of quantum computing but also across the broader domain of cryptography. As technology advances, the threat to conventional cryptographic systems is becoming increasingly tangible. This paper introduces the architecture and functionalities of the QCrypton platform, a quantum security strength analysis framework designed to quantitatively assess the quantum vulnerabilities inherent in various cryptographic algorithms. QCrypton provides an integrated environment that encompasses quantum circuit implementation, quantum simulation, and quantum library generation. In particular, it includes an analysis module for estimating the physical resource requirements for implementing quantum circuits in a Fault‐Tolerant Quantum Computing (FTQC) environment. The capabilities of the platform are demonstrated through the implementation and resource‐based analysis of fundamental components commonly used in cryptographic algorithms.
We present QCrypton v4.0, extending the unified quantum-safe security platform with two major capabilities: (1) a Quantum Noise Reachability Engine that classifies noise into 12 sub-types, evaluates against 6 QEC codes (720 evaluations), and recommends optimal correction with 24 compensating controls; and (2) enhanced BB84 eavesdrop detection via statistical noise decomposition, demonstrated catching an eavesdropper at 9.8 sigma when standard BB84 reported "secure." The platform now comprises 12 scanners, post-quantum crypto (FIPS 203/204/205), FTQC cost estimation (28 algorithms), noise reachability, code scanning (6 languages), and automated remediation — all via CLI/SDK/API with zero third-party crypto dependencies.
Quantum-safe security platforms must operate at cloud scale to protect modern distributed systems, yet existing quantum cryptography tools are designed as standalone utilities with no consideration for horizontal scaling, multi-tenancy, or container orchestration. We present the architecture of QCrypton, a stateless cloud-native quantum security platform that combines AI threat detection, post-quantum cryptography, and cryptographic code auditing in a fully database-backed, horizontally-scalable design. Key contributions include: (1) a stateless authentication architecture combining JWT sessions with API key fallback and distributed token revocation via hash-based blocklists; (2) domain-verified multi-tenant provisioning with wildcard email matching and per-user permission overrides computed at request time using set algebra; (3) idempotent bootstrap enabling safe replica restart without coordination or distributed locks; and (4) Kubernetes-native deployment with defense-in-depth security across application and infrastructure layers. The platform supports three deployment modes (CLI, SDK, Server) from a single codebase with consistent security guarantees. We evaluate the architecture under horizontal scaling scenarios, demonstrating linear throughput scaling to 10 replicas with 96% efficiency, sub-4ms authentication latency, and 99.97% availability over a 30-day production evaluation with zero dropped requests during rolling updates.
The advent of Quantum Computing (QC) poses significant threats to the cryptographic foundations of Blockchain (BC) systems, as quantum algorithms like Shor’s and Grover’s undermine the security of public-key cryptography and hash functions. This research conducts a comprehensive risk assessment of quantum vulnerabilities across critical BC components, including consensus mechanisms, smart contracts, and digital wallets. Leveraging the STRIDE threat modeling framework, we analyze threat vectors specific to QC, identifying key areas most susceptible to quantum-enabled attacks, such as private key compromise, consensus disruptions, and smart contract integrity risks. Our contributions provide actionable mitigation strategies, including a detailed security blueprint for quantum resilience, encompassing the integration of Post-Quantum Cryptography (PQC) and the adoption of quantum-resistant hash functions. We offer implementation best practices, focusing on key management, secure coding, and network security to strengthen BC components against quantum threats. To mitigate the risk of QC during transition from classical to quantum-resistant BCs, we present two hybrid BC architectures. As part of a comprehensive quantum resilience strategy, these architectures facilitate a secure and scalable migration by integrating platform-specific adaptations that balance security, adaptability, and operational efficiency. Our analysis extends to major BC platforms, including Bitcoin, Ethereum, Ripple, Litecoin, and Zcash, providing platform-specific vulnerability assessments and highlighting unique weaknesses in the quantum era. By identifying vulnerabilities, developing proactive defense strategies, and adopting a structured hybrid migration approach, this research equips BC stakeholders with a robust framework to achieve long-term quantum resilience. Finally, we explore challenges and research directions for integrating emerging technologies, including quantum machine learning, Artificial Intelligence (AI), and Web3, with BC systems, and discuss new threats that may arise from this convergence in the QC era.
The fast pace of quantum computing is a big threat to the classical cryptographic schemes, making more prevalent encryption patterns like RSA and ECC susceptible to attacks heretofore computationally infeasible. With the shortening of the timeline to inspect quantum supremacy, organizations must consider proactive actions to counteract quantum-based intrusions, especially cloud-native setups that store sensitive information and vital infrastructures. In this paper, an innovative Cloud-Native Artificial Intelligence (AI) Framework, QCAI-Guard, that is focused on real-time detection of quantum computing attacks on classical cryptographic protocols, is introduced. The suggested architecture can use the scalability and flexibility of the cloud native platforms, namely container-based microservices that are dynamically deployed through Kubernetes and proposed threat detection models that are driven by advanced AI but trained using synthetic quantum intrusion datasets. It uses the strategy of layers of security, where it uses zero trust principles, AI to detect anomalies, entails quantum signature responses, and post-quantum secure communication protocols. Moreover, we suggest an event streaming pipeline with a high level of security for real-time alerting and threat attribution. The simulated cloud environment provided experimental evaluations over simulated attack vectors by quantum-caused attacks on TLS and VPN protocols. These outcomes indicate the high accuracy of the framework during the initial attack detection with low latency, proving its practicality in real-world applications. Based on its efficacy in various metrics, performance benchmarks indicate its effectiveness in terms of detection accuracy, response time and scalability of the system. Also, the modular architecture allows full integration with already existing cloud security infrastructures and future expansion to new post-quantum algorithms. QCAI-Guard provides enterprises and government agencies with a quantum-safe solution in the future by integrating the concepts of cloud-native computing, artificial intelligence, and post-quantum security.
Abstract Background The emergence of quantum computing poses unprecedented threats to current cryptographic systems, potentially rendering RSA, ECC, and other widely-deployed algorithms vulnerable to exponential-time attacks. Objective This study provides a comprehensive analysis of quantum threats to cryptography through systematic review of 67 recent publications (2018–2025), identifying vulnerabilities, evaluating countermeasures, and proposing novel hybrid security frameworks with validated economic models. Methods We conducted a systematic literature review using PRISMA-ScR guidelines, analyzed 847 cryptographic implementations across different sectors, developed a quantum threat assessment matrix, and validated our hybrid framework through field deployments in automotive and healthcare sectors. Cross-disciplinary methodologies from quantum physics, AI, and cybersecurity were integrated using context-aware security (CAS) principles. Results Our analysis reveals three critical threat vectors: immediate harvest-now-decrypt-later (HNDL) attacks affecting 67% of surveyed organizations, medium-term RSA/ECC vulnerabilities projected for 2030–2035, and long-term post-quantum transition challenges. We achieved an 800-fold improvement in quantum error correction implementation and demonstrated that our AI-enhanced hybrid cryptographic framework reduces vulnerability exposure by 94% while maintaining 99.4% system uptime. Economic analysis shows 214% greater cost efficiency for SMEs compared to standalone PQC implementations. Conclusions Quantum threats require immediate action despite uncertain timelines. Our proposed AI-enhanced hybrid cryptographic framework, combining post-quantum algorithms with quantum key distribution and FPGA acceleration, provides 99.7% security assurance against projected quantum capabilities through 2040 at 37% below industry average costs.