Blog / AI Provenance
AI Provenance
15 articles in this category.
Prompt Injection Detection: Data Geometry Beats Model Scale
A sparse linear SVM hits 95.97% recall at sub-ms latency vs 44.35% for a 22M-param neural model. What this means for your LLM security stack.
AI-Synthesized Data: A Critical Threat to PQC Trust
Consumer AI can now fabricate realistic quantum experimental data that passes expert review. Learn how to detect it and protect data provenance.
Cryptographic Backdoors in Neural Networks: Critical Risks
Can a planted cryptographic backdoor make an AI attack invisible — and also protect your model IP? See what the research means and how to respond.
Critical: Post-Quantum Blockchain for Embodied AI Systems
Quantum advances threaten ECDSA-secured robotics data economies. Learn the crypto-agile migration path for embodied AI. Audit your stack now.
Quantum ML Security: Critical Adversarial Robustness Gaps
New research exposes fatal accuracy-robustness trade-offs in quantum ML systems. Learn which encoding schemes leave your QML pipeline exposed — and what to do n
Quantum QUBO Embedding: Neural Networks Beat Gurobi
Neural networks now outperform Gurobi at mapping QUBO problems onto neutral-atom quantum hardware. Learn what this means for your quantum migration path.
MAGIQ: Post-Quantum Security for Multi-Agent AI Systems
NIST deprecates RSA and ECC by 2030. MAGIQ offers provably secure, quantum-resistant governance for agentic AI. Learn what this means for your security posture.
SAGE Framework: LLM Vulnerability Detection 318% Smarter
A 7B model using SAGE outperforms 34B baselines with 318% MCC gains. Learn what Signal Submersion means for your DevSecOps pipeline and what to do next.
Quantum ML Advantage Challenged: What QELMs Reveal
New QELM research shows moderate entanglement — not quantum complexity — drives ML performance. What this means for your quantum hardware strategy. Read now.
Quantum Transformer QASA: 36 Parameters Beat Classical Models
How does QASA hit 6% MAE gains on ETTh1 with just 36 quantum parameters? Inside the architectural parsimony principle that changes quantum ML procurement.
Quantum Error-Correcting Codes: AI Agents Deliver 14,116 Lean-Certified Results
A multi-agent platform produced 14,116 formally verified quantum error-correcting codes. What does AI-certified fault tolerance mean for your PQC migration?
ContractShield: AI Detection for Obfuscated Smart Contracts
ContractShield achieves 91% F1-score detecting smart contract vulnerabilities under adversarial obfuscation. Learn what this means for your blockchain security