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

TL;DR

This paper presents a quantum gradient descent-based framework to detect and prevent malicious tampering in large language models used for medical applications, ensuring security without significant performance loss.

Contribution

It introduces a novel quantum-inspired method for real-time tamper detection in LLMs, enhancing security in sensitive medical AI systems.

Findings

Minimal performance impact on accuracy (89.1 to 88.3 on MIMIC)

Robust detection of adversarial parameter modifications

Superior sensitivity compared to baseline methods

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.