梳理大模型对抗攻击与防御技术，揭示安全风险与应对策略
Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations
- 系统分析梯度优化、强化学习与提示工程等攻击方法
- 指出大模型易受越狱攻击，威胁安全与可靠性
- 适合关注AI安全、模型防御的研究者与从业者
大型语言模型（LLMs）在自然语言处理任务中表现出卓越能力，但其对越狱攻击的脆弱性带来了重大安全风险。本文综述了大语言模型红队测试领域的最新进展，系统分析了基于梯度优化、强化学习及提示工程等多种攻击策略，探讨了这些攻击对大模型安全性的影响，并强调改进防御机制的必要性。本研究旨在全面呈现当前大模型红队攻击与防御的技术格局，推动更安全可靠的语言模型发展。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, but their vulnerability to jailbreak attacks poses significant security risks. This survey paper presents a comprehensive analysis of recent advancements in attack strategies and defense mechanisms within the field of Large Language Model (LLM) red-teaming. We analyze various attack methods, including gradient-based optimization, reinforcement learning, and prompt engineering approaches. We discuss the implications of these attacks on LLM safety and the need for improved defense mechanisms. This work aims to provide a thorough understanding of the current landscape of red-teaming attacks and defenses on LLMs, enabling the development of more secure and reliable language models.
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