Understanding the Adversarial Landscape of Large Language Models Through the Lens of Attack Objectives

Large language models face four main types of adversarial threats: privacy breaches (exposing sensitive data the model learned), integrity compromises (corrupting the model's outputs or training data), adversarial misuse (using the model for harmful purposes), and availability disruptions (making the model unavailable or slow). The article organizes these threats by their attackers' goals to help understand the landscape of vulnerabilities in LLMs.