Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human-like text. However, with their rising prominence, the security and vulnerability aspects of these models have garnered significant attention. This paper presents a comprehensive survey of the various forms of attacks targeting LLMs, discussing the nature and mechanisms of these attacks, their potential impacts, and current defense strategies. We delve into topics such as adversarial attacks that aim to manipulate model outputs, data poisoning that affects model training, and privacy concerns related to training data exploitation. The paper also explores the effectiveness of different attack methodologies, the resilience of LLMs against these attacks, and the implications for model integrity and user trust. By examining the latest research, we provide insights into the current landscape of LLM vulnerabilities and defense mechanisms. Our objective is to offer a nuanced understanding of LLM attacks, foster awareness within the AI community, and inspire robust solutions to mitigate these risks in future developments.

翻译：大型语言模型已成为自然语言处理领域的基石，在理解和生成类人文本方面具有革命性能力。然而，随着其日益突出，这些模型的安全性和脆弱性方面引起了广泛关注。本文全面综述了针对大型语言模型的各种攻击形式，探讨了这些攻击的性质和机制、潜在影响以及当前防御策略。我们深入研究了诸如旨在操纵模型输出的对抗攻击、影响模型训练的数据投毒以及与训练数据利用相关的隐私问题等主题。本文还探讨了不同攻击方法的有效性、大型语言模型对这些攻击的抵御能力，以及对模型完整性和用户信任的影响。通过审视最新研究，我们提供了关于当前大型语言模型漏洞和防御机制格局的见解。我们的目标是提供对大型语言模型攻击的细微理解，促进人工智能社区内的意识提升，并激发未来发展中缓解这些风险的稳健解决方案。