连锁检测可以实现强大而高效的越狱防御
1Harbin Engineering University, 145 Nantong Street, Harbin, 150001, Heilongjiang, China.
概括
本研究介绍了Chain-of-Detection (CoD),这是一个有效的防御机制,可以防止大型语言模型 (LLM) 通过越狱提示生成有害内容. CoD有效地提前拦截恶意查询,并分析响应,以对抗不断演变的攻击提供强大的安全性.
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.
- 网络安全 网络安全
背景情况:
- 大型语言模型 (LLM) 越来越多地与人类的偏好保持一致,以防止不必要的输出.
- 然而,越狱提示可以绕过这些保护措施,导致生成禁止内容.
- 现有的防御方法在可扩展性,复杂性和对复杂攻击的有效性方面存在局限性.
研究的目的:
- 提出一个高效和强大的防御机制,以防LLMs不断发展的越狱攻击.
- 开发一个能够有效拦截恶意查询和有害响应的系统.
- 加强LLM对基于提示的漏洞的安全性.
主要方法:
- 连锁检测 (CoD) 机制使用查询侧检测.
- 使用指导式有害搜索进行提示级攻击拦截.
- 利用LLM对潜在的有害反应进行深入推理,以实现全面的防御.
- 实施两个阶段的检测过程:预先查询分析和响应侧评估.
主要成果:
- 证明了CoD机制对各种越狱攻击的有效性.
- 成功拦截常见和复杂的恶意查询.
- 在多个闭源和开源LLMs中验证.
- 在LLM安全性和稳定性方面显著改进.
结论:
- 检测链 (Chain-of-Detection,简称CoD) 机制提供了一种高效,强大的防御,以抵御LLMs的越狱攻击.
- CoD的查询侧和响应侧检测的双重方法提供了全面的保护.
- 这种方法可以提高LLM的安全性,而无需大量的开销或广泛的再培训.
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