数据中毒在医疗保健中的漏洞 人工智能架构:分析安全框架和防御策略
Farhad Abtahi1,2,3, Fernando Seoane1,4,5,3, Ivan Pau6
1Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Huddinge, Stockholm, Sweden.
Journal of medical Internet research
|January 23, 2026
概括
医疗保健人工智能易受数据中毒攻击的影响,很少有样本会造成重大损害. 目前的法规阻碍了检测,需要为患者安全制定多层次的防御策略.
科学领域:
- 医疗人工智能 医疗人工智能
- 医疗保健中的网络安全
- 人工智能模型安全安全模型
背景情况:
- 医疗保健人工智能系统越来越多地集成到临床工作流中,但仍然容易受到数据中毒攻击.
- 少数操纵的训练样本可以破坏用于诊断,文档和资源分配的AI模型.
- 现有的隐私法规可能无意中使异常检测和审计复杂化,限制对抗活动的可见性.
研究的目的:
- 在主要的医疗保健AI架构中对数据中毒漏洞进行全面的威胁分析.
- 在临床AI系统中识别攻击表面.
- 提出适用于医疗保健机构的多层防御框架.
主要方法:
- 综合了41项安全研究 (2019-2025) 的经验发现,将其纳入一个针对医疗保健的威胁建模框架.
- 在AI架构,联合学习,临床文档,资源配置和供应链中构建了8种假设攻击场景.
- 与现实的内部访问威胁模型和当前临床部署实践结合的场景.
主要成果:
- 只有100-500个有毒样本的攻击者可以破坏医疗保健AI系统 (≥60%的成功率).
- 攻击的成功取决于中毒样本的绝对数量,挑战大数据集提供固有的保护的假设.
- 检测延迟在6-12个月之间,在分布式或隐私受限制的环境中可能是几年;隐私法规进一步复杂化了检测.
结论:
- 医疗保健人工智能系统面临着严重的安全挑战,目前的法规和验证实践无法充分解决这些挑战.
- 提出了一种多层次的防御策略,结合了整体分歧监测,对抗性测试,保护隐私的可审计机制和加强的治理.
- 确保患者安全可能需要转向更易于解释和验证的AI架构,而不是不透明的高性能模型.
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