Large Language Models (LLMs), such as GPT-3 and BERT, have revolutionized natural language understanding and generation. They possess deep language comprehension, human-like text generation capabilities, contextual awareness, and robust problem-solving skills, making them invaluable in various domains (e.g., search engines, customer support, translation). In the meantime, LLMs have also gained traction in the security community, revealing security vulnerabilities and showcasing their potential in security-related tasks. This paper explores the intersection of LLMs with security and privacy. Specifically, we investigate how LLMs positively impact security and privacy, potential risks and threats associated with their use, and inherent vulnerabilities within LLMs. Through a comprehensive literature review, the paper categorizes findings into "The Good" (beneficial LLM applications), "The Bad" (offensive applications), and "The Ugly" (vulnerabilities and their defenses). We have some interesting findings. For example, LLMs have proven to enhance code and data security, outperforming traditional methods. However, they can also be harnessed for various attacks (particularly user-level attacks) due to their human-like reasoning abilities. We have identified areas that require further research efforts. For example, research on model and parameter extraction attacks is limited and often theoretical, hindered by LLM parameter scale and confidentiality. Safe instruction tuning, a recent development, requires more exploration. We hope that our work can shed light on the LLMs' potential to both bolster and jeopardize cybersecurity.

翻译：大规模语言模型(LLM)，如GPT-3和BERT，彻底革新了自然语言理解与生成领域。它们具备深层的语言理解能力、类人文本生成能力、上下文感知能力以及强大的问题解决能力，在搜索引擎、客户支持、翻译等多个领域展现出不可替代的价值。与此同时，LLM在安全社区也引起了广泛关注，揭示了其安全漏洞及其在安全相关任务中的潜力。本文探讨了LLM与安全及隐私的交集。具体而言，我们研究了LLM对安全与隐私的积极影响、其使用过程中的潜在风险与威胁，以及LLM自身的内在脆弱性。通过全面的文献综述，本文将研究发现归纳为“好”(有益的LLM应用)、“坏”(攻击性应用)和“丑”(脆弱性及其防御)三类。我们获得了一些有趣的发现。例如，LLM已被证明能够增强代码和数据安全，其性能优于传统方法。然而，由于其类人推理能力，它们也可能被用于各种攻击(尤其是用户级攻击)。我们识别出需要进一步研究的领域。例如，针对模型和参数提取攻击的研究目前较为有限且多处于理论层面，受限于LLM的参数规模与保密性。安全指令调整这一近期发展，也需要更多探索。我们希望本研究能够揭示LLM在增强和威胁网络安全方面的潜在可能性。