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.
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.
We introduce noise reachability analysis — a method for determining which physical qubit errors propagate past quantum error correction codes to become logical errors. The engine classifies noise into three source categories (channel, gate, environment) with 12 sub-types, evaluates each against six QEC code families at multiple code distances (720 evaluations per scan), and determines which errors are "reachable" (propagate past the best available correction). We demonstrate automatic noise bias detection, correlated burst noise handling, and noise-aware QEC code selection. The engine recommends optimal codes with 24 source-specific compensating controls beyond code distance increase. Implemented within QCrypton and validated across multiple noise scenarios, the analysis completes in under 2ms.
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.
Automated code scanners routinely detect hardcoded secrets---API keys, private keys, database credentials---yet transmit these findings in plaintext through CI/CD pipelines, dashboards, and webhook notifications, creating a secondary exposure vector. We present a novel approach that integrates BB84 quantum key distribution (QKD) simulation into the code scanning pipeline, encrypting all detected secrets with AES-256-GCM using QKD-derived keys before they leave the scanner process. Our implementation operates within the QCrypton platform using a full BB84 protocol simulation with basis reconciliation, eavesdropper detection at an 11% error threshold, and privacy amplification via LSH-256 hashing (KS X 3262). We demonstrate that the approach adds negligible overhead (<50ms for typical scan results), eliminates plaintext secret exposure in scan reports, and provides a quantum-safe transitional architecture. The system detects 30+ secret types across six programming languages (JavaScript, Python, Go, Java, Rust, C/C++) and wraps each finding individually with unique initialization vectors. To our knowledge, this is the first code scanner to employ quantum key distribution for protecting its own findings.
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 impending arrival of cryptographically relevant quantum computers (CRQCs) threatens the security foundations of modern software: Shor's algorithm breaks RSA, ECDSA, ECDH, and Diffie-Hellman, while Grover's algorithm reduces the effective security of symmetric and hash-based schemes. Despite NIST standardising post-quantum cryptography (PQC) in 2024 (FIPS 203 ML-KEM, FIPS 204 ML-DSA, FIPS 205 SLH-DSA), most codebases lack automated tooling to inventory classical cryptographic usage and prioritise migration based on quantum risk. We present Quantum-Safe Code Auditor, a quantum-aware static analysis framework that combines (i) regex-based detection of 15 classes of quantum-vulnerable primitives, (ii) LLM-assisted contextual enrichment to classify usage and severity, and (iii) risk scoring via a Variational Quantum Eigensolver (VQE) model implemented in Qiskit 2.x, incorporating qubit-cost estimates to prioritise findings. We evaluate the system across five open-source libraries -- python-rsa, python-ecdsa, python-jose, node-jsonwebtoken, and Bouncy Castle Java -- covering 5,775 findings. On a stratified sample of 602 labelled instances, we achieve 71.98% precision, 100% recall, and an F1 score of 83.71%. All code, data, and reproduction scripts are released as open-source.
Standard BB84 QKD detects eavesdropping via a fixed 11% error rate threshold, which does not distinguish legitimate channel noise from eavesdropper-induced errors. We present an enhanced method that decomposes the observed error rate into expected noise contributions (channel transmission, detector dark counts, optical misalignment, timing jitter) and applies statistical significance testing to the anomalous residual. The method provides a four-level detection verdict with calibrated confidence. We demonstrate catching an eavesdropper intercepting 30% of qubits at 9.8 sigma significance when standard BB84 reported "secure" (6.88% < 11%). The method reduces false positives by a factor of 80,000 in noisy channels while eliminating false negatives from low-intercept-rate attacks.