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arXiv · 2506.19086

Enhancing Biosecurity in Tamper-Resistant Large Language Models With Quantum Gradient Descent

Fahmida Hai·Saif Nirzhor·Rubayat Khan·Don Roosan
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Abstract

This paper introduces a tamper-resistant framework for large language models (LLMs) in medical applications, utilizing quantum gradient descent (QGD) to detect malicious parameter modifications in real time. Integrated into a LLaMA-based model, QGD monitors weight amplitude distributions, identifying adversarial fine-tuning anomalies. Tests on the MIMIC and eICU datasets show minimal performance impact (accuracy: 89.1 to 88.3 on MIMIC) while robustly detecting tampering. PubMedQA evaluations confirm preserved biomedical question-answering capabilities. Compared to baselines like selective unlearning and cryptographic fingerprinting, QGD offers superior sensitivity to subtle weight changes. This quantum-inspired approach ensures secure, reliable medical AI, extensible to other high-stakes domains.

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Fahmida Hai, Saif Nirzhor, Rubayat Khan, Don Roosan. 2025-06-23. Enhancing Biosecurity in Tamper-Resistant Large Language Models With Quantum Gradient Descent. https://arxiv.org/abs/2506.19086

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