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

Fahmida Hai, Saif Nirzhor, Rubayat Khan, Don Roosan

2025

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.

Paper Citation

in Harvard Style

Hai F., Nirzhor S., Khan R. and Roosan D. (2025). Enhancing Biosecurity in Tamper-Resistant Large Language Models with Quantum Gradient Descent. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 97-107. DOI: 10.5220/0013553100003967

in Bibtex Style

@conference{data25,
author={Fahmida Hai and Saif Nirzhor and Rubayat Khan and Don Roosan},
title={Enhancing Biosecurity in Tamper-Resistant Large Language Models with Quantum Gradient Descent},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={97-107},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013553100003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Enhancing Biosecurity in Tamper-Resistant Large Language Models with Quantum Gradient Descent
SN - 978-989-758-758-0
AU - Hai F.
AU - Nirzhor S.
AU - Khan R.
AU - Roosan D.
PY - 2025
SP - 97
EP - 107
DO - 10.5220/0013553100003967
PB - SciTePress