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

Key Points

• Integrity compromises threaten the reliability of responses generated by large language models, which can lead to misinformation.
• Privacy breaches could expose sensitive user data, increasing the risk of severe consequences for individuals and organizations.
• Adversarial misuse poses threats as malicious actors exploit large language models for harmful purposes, risking public safety.
• Availability disruptions can hinder access to large language models, affecting services and applications relying on their functionality.