Large Language Model Adversarial Landscape Through the Lens of Attack Objectives

TL;DR

This paper proposes an objective-based adversarial taxonomy for large language models, organizing threats by four goals: privacy breach, integrity compromise, availability disruption, and misuse. It provides a technical overview of LLM architectures and maps attack objectives to vulnerable components such as Data, Prompts, Weights, and Gradients, detailing representative attacks under each objective. The defenses discussed span red teaming, optimization-based mitigations (including model auditing and unlearning), prompt taming, response reformulation, randomized smoothing, differential privacy, and alignment, with forward-looking directions in backdoor detection, verifiable privacy-preserving LLMs, and cryptographic defenses. By focusing on attacker goals rather than techniques, the work aims to guide researchers and practitioners toward more targeted, resilient defenses and safer deployment of LLMs in real-world applications.

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.

Figures (6)

• Figure 1: A simplified architecture of LLM-based systems.
• Figure 2: The overview of the four categories of LLM attacks.
• Figure 3: A model extraction example with the privacy breach objective.
• Figure 4: A data poisoning example with the integrity compromise objective.
• Figure 5: A DoS example with the availability disruption objective.