大型语言模型中的攻击和防御技术:一项调查和新的视角
Zhiyu Liao1, Kang Chen2, Yuanguo Lin1
1School of Computer Engineering, Jimei University, Xiamen, China.
概括
本调查检查了大型语言模型 (LLM) 的漏洞,详细介绍了各种攻击和防御策略. 它强调了确保LLM的挑战,并提出了强大的AI安全的未来研究方向.
科学领域:
- 人工智能的人工智能
- 网络安全 网络安全
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 是NLP的组成部分,但具有重要的安全和道德漏洞.
- 法律法规的快速发展需要对其威胁格局有全面的了解.
研究的目的:
- 系统地调查和分类针对LLM的当前攻击和防御技术.
- 在开发安全和有弹性的LLM系统中识别挑战和开放问题.
主要方法:
- 将LLM攻击分为以下类别:对抗性提示,优化,模型盗窃和特定应用程序的攻击.
- 分析防御策略,包括基于预防和基于检测的方法.
- 审查现有文献,以综合最先进的LLM安全.
主要成果:
- 详细介绍了各种LLM攻击载体的详细机制和影响.
- 分析了基于预防和检测的防御策略,以及它们各自的优势和局限性.
- 确定的挑战包括适应不断变化的威胁,平衡安全与可用性以及资源限制.
结论:
- 开发安全和有弹性的LLM需要解决适应性可扩展性,强大的对抗性攻击检测和通用的防御机制.
- 伦理考虑和跨学科合作对于减轻现实世界LLM应用中的风险至关重要.
- 需要进行持续的研究,以跟上动态的威胁格局,并确保AI安全.
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