Description

Large Language Models (LLMs) are AI algorithms that process user inputs and create plausible responses by predicting sequences of words. They are trained on huge semi-public data sets and can have a wide range of use cases in modern websites, such as customer service, translation, SEO improvement, analysis of user-generated content, and more. Web LLM attacks exploit the model's access to data, APIs, or user information that an attacker cannot access directly. These attacks can retrieve data, trigger harmful actions via APIs, and trigger attacks on other users and systems that query the LLM. The article discusses various aspects of these attacks, including prompt injection, detecting LLM vulnerabilities, and defending against LLM attacks. It also includes practical examples in the form of labs to help understand the concepts better.