Adversarial Attacks on Large Language Models in Medicine.

ArXiv Pub Date : 2024-12-05
Yifan Yang, Qiao Jin, Furong Huang, Zhiyong Lu
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Abstract

The integration of Large Language Models (LLMs) into healthcare applications offers promising advancements in medical diagnostics, treatment recommendations, and patient care. However, the susceptibility of LLMs to adversarial attacks poses a significant threat, potentially leading to harmful outcomes in delicate medical contexts. This study investigates the vulnerability of LLMs to two types of adversarial attacks in three medical tasks. Utilizing real-world patient data, we demonstrate that both open-source and proprietary LLMs are susceptible to manipulation across multiple tasks. This research further reveals that domain-specific tasks demand more adversarial data in model fine-tuning than general domain tasks for effective attack execution, especially for more capable models. We discover that while integrating adversarial data does not markedly degrade overall model performance on medical benchmarks, it does lead to noticeable shifts in fine-tuned model weights, suggesting a potential pathway for detecting and countering model attacks. This research highlights the urgent need for robust security measures and the development of defensive mechanisms to safeguard LLMs in medical applications, to ensure their safe and effective deployment in healthcare settings.

对医学大型语言模型的对抗性攻击。
将大型语言模型(LLM)集成到医疗保健应用中,有望在医疗诊断、治疗建议和患者护理方面取得进步。然而,大型语言模型易受对抗性攻击,这构成了重大威胁,有可能在微妙的医疗环境中导致有害结果。本研究调查了三种医疗任务中 LLMs 易受两类对抗性攻击的情况。利用真实世界的患者数据,我们证明了开源和专有 LLM 在多个任务中都容易受到操纵。这项研究进一步揭示,与一般领域的任务相比,特定领域的任务在模型微调方面需要更多的对抗数据,以有效执行攻击,尤其是对于能力更强的模型。我们发现,虽然整合对抗数据不会明显降低模型在医疗基准上的整体性能,但却会导致微调模型权重发生明显变化,这为检测和反击模型攻击提供了潜在途径。这项研究表明,迫切需要采取强有力的安全措施和开发防御机制来保护医疗应用中的 LLM,以确保其在医疗环境中的安全和有效部署。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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