This paper comprehensively explores the ethical challenges arising from security threats to Large Language Models (LLMs). These intricate digital repositories are increasingly integrated into our daily lives, making them prime targets for attacks that can compromise their training data and the confidentiality of their data sources. The paper delves into the nuanced ethical repercussions of such security threats on society and individual privacy. We scrutinize five major threats--prompt injection, jailbreaking, Personal Identifiable Information (PII) exposure, sexually explicit content, and hate-based content--going beyond mere identification to assess their critical ethical consequences and the urgency they create for robust defensive strategies. The escalating reliance on LLMs underscores the crucial need for ensuring these systems operate within the bounds of ethical norms, particularly as their misuse can lead to significant societal and individual harm. We propose conceptualizing and developing an evaluative tool tailored for LLMs, which would serve a dual purpose: guiding developers and designers in preemptive fortification of backend systems and scrutinizing the ethical dimensions of LLM chatbot responses during the testing phase. By comparing LLM responses with those expected from humans in a moral context, we aim to discern the degree to which AI behaviors align with the ethical values held by a broader society. Ultimately, this paper not only underscores the ethical troubles presented by LLMs; it also highlights a path toward cultivating trust in these systems.

翻译：本文全面探讨了大型语言模型（LLMs）面临的安全威胁所引发的伦理挑战。这些复杂的数字存储库日益融入日常生活，使其成为攻击的主要目标，可能导致训练数据及数据源机密性受损。本文深入分析了此类安全威胁对社会与个人隐私产生的细致伦理影响。我们系统审视了五大威胁——提示注入、越狱攻击、个人身份信息（PII）泄露、色情内容与仇恨内容——不仅限于识别威胁，更评估其关键的伦理后果及对建立强健防御策略的紧迫需求。随着对LLMs依赖度的持续攀升，确保这些系统在伦理规范框架内运行显得尤为重要，特别是其滥用可能引发重大的社会与个人危害。我们提出为LLMs量身构建评估工具的概念框架，该工具将发挥双重作用：在开发阶段引导开发者与设计者预先强化后端系统，并在测试阶段审视LLM聊天机器人回应的伦理维度。通过将LLM的回应与道德情境下人类预期回应进行对比，我们旨在辨析AI行为在多大程度上符合更广泛社会持有的伦理价值观。最终，本文不仅揭示了LLMs带来的伦理困境，更为培育对这些系统的信任指明路径。