Prompt Attack

Prompt attacks exploit vulnerabilities in large language models (LLMs) and other AI systems by manipulating input prompts to elicit undesired or harmful outputs, compromising data integrity and user safety. Current research focuses on developing both novel attack methods, often leveraging gradient-based optimization or generative models to create effective adversarial prompts, and robust defense mechanisms, including improved prompt filtering and model architectures designed to resist manipulation. Understanding and mitigating these attacks is crucial for ensuring the safe and reliable deployment of LLMs across various applications, impacting both the security of AI systems and the trustworthiness of AI-driven services.