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在语义缓存中增强对抗性弹性,以安全检索增强代系统.

Mohanad Afiffy1, Mohamed Waleed Fakhr2, Fahima A Maghraby3

  • 1College of Engineering and Technology, Arab Academy for Science and Technology, Cairo, Egypt. m.abdelba33713@student.aast.edu.

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概括

新的SAFE-CACHE系统通过防止对语义缓存的对抗性攻击来提高大型语言模型 (LLM) 的安全性. 它显著提高了可靠性,并降低了自然语言处理应用程序中的计算成本.

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科学领域:

  • 人工智能的人工智能
  • 自然语言处理自然语言处理.
  • 机器学习安全 机器学习安全

背景情况:

  • 大型语言模型 (LLM) 与检索增强生成 (RAG) 提供先进的NLP功能,但由于重复查询处理而面临高计算成本.
  • 语义缓存通过重用过去对类似查询的响应来减轻这些成本,但现有的方法容易受到利用语义相似性的对抗性攻击.

研究的目的:

  • 调查当前语义近距离缓存系统中的安全漏洞,例如GPTCache.
  • 引入SAFE-CACHE,一种新,强大的语义缓存方法,旨在抵御对抗性利用.

主要方法:

  • SAFE-CACHE采用基于集群中心的策略,与GPTCache的单查询嵌入形成鲜明对比.
  • 使用无监督集群,统计噪声检测,双编码器改进,以及精心调整的LLM用于意图推断.
  • 将输入查询与集群中心体进行比较,以增强语义验证.

主要成果:

  • 与GPTCache相比,SAFE-CACHE显著降低了对抗性攻击的成功率,从52.77%降至14.27%.
  • 在对抗阻力方面表现出高达72%的改善.
  • 保持系统可靠性和对恶意输入的安全性.

结论:

  • 像GPTCache这样的当前语义缓存系统容易受到对抗性攻击.
  • 通过其集群中心的方法,SAFE-CACHE提供了更安全和更有弹性的替代方案.
  • 拟议的方法有效地减轻了安全风险,同时保留了LLMs语义缓存的好处.