This paper comprehensively explores the ethical challenges arising from
security threats to Language Learning 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.

通过探讨语言学习模型的安全威胁，本文全面研究了一系列涉及伦理道德的挑战，包括数据安全、隐私保护等问题，提出了一种定制的评估工具，用于加强语言学习模型的后端系统，并在测试阶段评估其伦理维度与社会伦理价值之间的一致性。