Enhancing Biosecurity in Tamper-Resistant Large Language Models With Quantum Gradient Descent
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916809076113408 |
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| author | Hai, Fahmida Nirzhor, Saif Khan, Rubayat Roosan, Don |
| author_facet | Hai, Fahmida Nirzhor, Saif Khan, Rubayat Roosan, Don |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19086 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Biosecurity in Tamper-Resistant Large Language Models With Quantum Gradient Descent Hai, Fahmida Nirzhor, Saif Khan, Rubayat Roosan, Don Molecular Networks 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. |
| title | Enhancing Biosecurity in Tamper-Resistant Large Language Models With Quantum Gradient Descent |
| topic | Molecular Networks |
| url | https://arxiv.org/abs/2506.19086 |