BadCLM:对电子健康记录的临床语言模型的后门攻击
Weimin Lyu1, Zexin Bi2, Fusheng Wang1
1The Stony Brook University, New York, NY.
概括
研究人员开发了基于注意力的后门攻击BadCLM,以破坏临床语言模型. 这种方法将隐藏的漏洞插入AI系统,影响临床决策支持和患者安全.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 医疗保健中的网络安全
背景情况:
- 电子健康记录 (EHR) 中的临床语言模型增强了决策支持.
- 这些先进的人工智能模型中的潜在安全漏洞尚未得到充分了解.
- 后门攻击对临床AI的完整性构成重大威胁.
研究的目的:
- 介绍和评估BadCLM,一种基于注意力的后门攻击方法,用于临床语言模型.
- 通过使用后门攻击来证明危害临床决策支持系统的潜力.
- 要突出与医疗保健中的AI相关的安全风险.
主要方法:
- 开发了BadCLM,一种基于注意力的创新后门攻击技术.
- 在临床语言模型中嵌入了一个秘密的后门触发器.
- 在使用MIMIC III数据集的医院死亡率预测任务中评估了攻击的有效性.
主要成果:
- BadCLM成功地嵌入了一个后门,当触发器存在时导致错误的预测.
- 该模型在没有触发器的输入上准确地运行,展示了隐形.
- 在现实世界中,攻击破坏了临床语言模型的完整性.
结论:
- 后门攻击对临床决策支持系统构成重大安全风险.
- 坏CLM方法有效地证明了临床语言模型的脆弱性.
- 需要进一步的研究来开发针对医疗保健AI中此类网络威胁的强有力的防御.
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