Securing Your AI: Best Practices for Protecting LLMs

As large language models (LLMs) are integrated into more applications, they become attractive targets for malicious actors. Securing these models against attack is a new and critical challenge for developers and security professionals. This guide provides an overview of the most common security vulnerabilities affecting LLMs, including prompt injection, data poisoning, and model extraction attacks. We explain how these attacks work and, more importantly, provide a set of best practices and mitigation strategies for building more robust and secure AI systems. Protecting your LLMs is essential for maintaining user trust and preventing misuse.