Explain the various security challenges posed by large language models (LLMs) as they become more widespread and integrated into critical systems. They categorize and describe different types of attacks, such as those focused on breaching privacy, compromising integrity through data poisoning, disrupting availability, and enabling misuse through techniques like prompt injection and jailbreaking.

The sources also highlight the importance of evaluating LLM robustness using frameworks and discuss emerging vulnerabilities in advanced architectures, including multi-modal models and federated learning. Finally, they outline best practices for securing LLMs through careful training data management, model evaluation, and the implementation of multi-layered defense strategies.