梳理大模型攻击与防御进展，揭示安全短板与未来方向。
Recent Advances in Attack and Defense Approaches of Large Language Models
- 系统分析大模型攻击路径与内在弱点
- 评估现有防御策略的有效性与局限
- 适合关注AI安全研究者与开发者
大型语言模型（LLMs）凭借强大的文本处理与生成能力，深刻变革了人工智能领域。然而其广泛应用也带来了显著的安全与可靠性隐患。深度神经网络的固有漏洞与新兴威胁模型可能干扰安全评估，导致虚假安全感。鉴于该领域研究广泛，本文综述当前关于大模型漏洞与威胁的成果，评估主流防御机制的效果。通过分析近期攻击向量与模型缺陷，揭示攻击机理与威胁演化趋势；同时考察现有防御策略，明确其优势与不足。在对比攻防技术进展的基础上，识别研究空白，并提出未来发展方向。旨在深化对大模型安全挑战的理解，推动更稳健的安全措施研发。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have revolutionized artificial intelligence and machine learning through their advanced text processing and generating capabilities. However, their widespread deployment has raised significant safety and reliability concerns. Established vulnerabilities in deep neural networks, coupled with emerging threat models, may compromise security evaluations and create a false sense of security. Given the extensive research in the field of LLM security, we believe that summarizing the current state of affairs will help the research community better understand the present landscape and inform future developments. This paper reviews current research on LLM vulnerabilities and threats, and evaluates the effectiveness of contemporary defense mechanisms. We analyze recent studies on attack vectors and model weaknesses, providing insights into attack mechanisms and the evolving threat landscape. We also examine current defense strategies, highlighting their strengths and limitations. By contrasting advancements in attack and defense methodologies, we identify research gaps and propose future directions to enhance LLM security. Our goal is to advance the understanding of LLM safety challenges and guide the development of more robust security measures.
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