针对医疗保健中的大型语言模型的网络安全威胁和缓解策略
Tugba Akinci D'Antonoli1,2, Ali S Tejani3, Bardia Khosravi4
1Department of Diagnostic and Interventional Neuroradiology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland.
Radiology. Artificial intelligence
|May 14, 2025
概括
医疗保健中的大型语言模型 (LLM) 存在独特的网络安全风险,包括数据操纵和隐私侵犯. 实施强有力的安全措施对于安全部署和保护患者信息至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 为推进医疗保健实践和患者结果提供了重大潜力.
- 然而,它们的集成引入了超出典型人工智能系统的独特网络安全漏洞.
研究的目的:
- 识别和分析在医疗保健环境中LLM所带来的特定网络安全挑战.
- 提出缓解策略,以应对这些风险,并确保安全实施.
主要方法:
- 审查在医疗保健中潜在的LLM利用载体.
- 对威胁的分析,包括数据泄露,未经授权的操纵,以及对敏感信息的推断.
- 检查针对LLMs的数据中毒技术.
主要成果:
- LLM易受恶意攻击,隐私侵犯和未经授权的数据操纵的影响.
- 恶意行为者可以从LLM培训数据中推断出敏感的患者信息.
- 数据中毒可以改变LLM输出,使攻击者受益.
结论:
- 在广泛采用医疗保健之前,解决LLM网络安全风险至关重要.
- 在开发,培训和部署期间实施强有力的安全措施是关键.
- 为了保护患者隐私和数据完整性,需要采取积极的缓解策略.
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