[论文解读] The Ethics of Interaction: Mitigating Security Threats in LLMs

Ashutosh Kumar, Murthy, Shiv Vignesh+2 more|arXiv (Cornell University)|Jan 22, 2024

Hate Speech and Cyberbullying Detection被引用 14

一句话总结

本文分析大型语言模型（LLMs）的伦理挑战和安全威胁，提出一个评估工具以引导防御设计和对话系统回答进行符合人类道德规范的伦理测试。

ABSTRACT

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.

研究动机与目标

• 识别针对LLMs的安全威胁的伦理含义。
• 检查五大主要威胁及其对社会和个人隐私的影响。
• 提出一个评估框架，以指导后端防护与对LLMs的伦理测试。

提出的方法

• 对LLMs的五种威胁进行调查与伦理分析：提示注入、越狱、PII暴露、露骨性内容，以及仇恨性内容。
• 提出一个概念性评估工具，用于评估并将AI回应与人类道德期望进行比较。
• 讨论将AI与人类道德行为进行比较如何揭示与社会伦理价值的一致性。

实验结果

研究问题

• RQ1 面临LLMs的主要安全威胁的关键伦理后果有哪些？
• RQ2 评估工具如何帮助提前加强LLM后端系统并在测试中评估伦理一致性？
• RQ3 人类与AI的道德比较在哪些方面能够影响对LLM系统的信任与伦理规范？

主要发现

• LLMs的安全威胁对社会和个人隐私具有显著影响。
• 评估工具可以指导LLM回应的防御性设计和伦理测试。
• 将LLM输出与人类道德期望进行比较可以揭示与更广泛社会伦理价值的一致性。
• 该工作强调通过伦理考量构建对LLM系统的信任的必要性。