The Ethics of Interaction: Mitigating Security Threats in LLMs

TL;DR

The paper addresses the ethical challenges of security threats to Large Language Models (LLMs) and proposes a structured mitigation framework. It catalogs vulnerabilities such as prompt injection, jailbreaking, PII leakage, and content risks, and introduces an evaluative pipeline encompassing prompt reception, classification, ethical checks, and response design. Through a modular tool and governance-oriented approach, the work emphasizes testing, transparency, and continuous monitoring to align LLM behavior with societal values and protect privacy. The proposed methodology aims to build trust and resilience in LLM deployments across sensitive domains by enabling preemptive defense and ethical accountability.

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.

Figures (1)

• Figure 1: A tool designed to mitigate LLM security risks