标题： DeepSeek的黑暗深处：针对CoT启用模型安全对齐的微调攻击 显示英文标题

标题： The dark deep side of DeepSeek: Fine-tuning attacks against the safety alignment of CoT-enabled models

摘要： 大规模语言模型通常在预训练阶段使用大量数据进行训练，这可能包括一些潜在有害的信息。 微调攻击可以通过提示模型揭示这些行为，导致生成有害内容。 在本文中，我们专注于研究基于思维链推理模型 DeepSeek 在受到微调攻击时的性能。 具体来说，我们探讨了微调如何操纵模型的输出，在检查思维链推理与对抗性输入之间的相互作用的同时，加剧其响应的有害性。 通过这项研究，我们旨在揭示启用思维链的模型对微调攻击的脆弱性及其对安全和伦理部署的影响。 显示英文摘要

摘要： Large language models are typically trained on vast amounts of data during the pre-training phase, which may include some potentially harmful information. Fine-tuning attacks can exploit this by prompting the model to reveal such behaviours, leading to the generation of harmful content. In this paper, we focus on investigating the performance of the Chain of Thought based reasoning model, DeepSeek, when subjected to fine-tuning attacks. Specifically, we explore how fine-tuning manipulates the model's output, exacerbating the harmfulness of its responses while examining the interaction between the Chain of Thought reasoning and adversarial inputs. Through this study, we aim to shed light on the vulnerability of Chain of Thought enabled models to fine-tuning attacks and the implications for their safety and ethical deployment.