Large Language Model Adversarial Landscape Through the Lens of Attack Objectives

TL;DR

This paper offers a comprehensive, goal-oriented analysis of adversarial threats to Large Language Models, emphasizing attack objectives like privacy, integrity, and misuse to improve understanding and defense strategies.

Contribution

It introduces a novel perspective by analyzing LLM adversarial attacks through attack objectives, moving beyond traditional technique-based taxonomies.

Findings

Highlights strategic intent behind adversarial attacks

Identifies evolving threat landscape and attack effectiveness

Provides guidance for developing more robust LLM defenses

Abstract

Large Language Models (LLMs) represent a transformative leap in artificial intelligence, enabling the comprehension, generation, and nuanced interaction with human language on an unparalleled scale. However, LLMs are increasingly vulnerable to a range of adversarial attacks that threaten their privacy, reliability, security, and trustworthiness. These attacks can distort outputs, inject biases, leak sensitive information, or disrupt the normal functioning of LLMs, posing significant challenges across various applications. In this paper, we provide a novel comprehensive analysis of the adversarial landscape of LLMs, framed through the lens of attack objectives. By concentrating on the core goals of adversarial actors, we offer a fresh perspective that examines threats from the angles of privacy, integrity, availability, and misuse, moving beyond conventional taxonomies that focus solely on attack techniques. This objective-driven adversarial landscape not only highlights the strategic intent behind different adversarial approaches but also sheds light on the evolving nature of these threats and the effectiveness of current defenses. Our analysis aims to guide researchers and practitioners in better understanding, anticipating, and mitigating these attacks, ultimately contributing to the development of more resilient and robust LLM systems.