When a company builds a product powered by a large language model, they tend to focus on what the AI can do, not on how it can be exploited.

AI systems introduce a new category of vulnerabilities that traditional security checklists do not cover. An attacker can manipulate an AI by slipping instructions into its input, trick it into leaking sensitive data from its training, or overwhelm it with requests that drain resources and drive up costs, none of which map cleanly onto the security risks that developers already know to look for.

OWASP, the nonprofit organization that maintains the most widely used security risk framework in software development, publishes a dedicated Top 10 list for large language model applications. It covers threats like prompt injection, where a malicious user rewrites the AI's instructions through ordinary text input; excessive agency, where an AI with too many permissions takes actions its designers never intended; and data poisoning, where corrupted training data plants hidden behaviors in the model before it ever reaches users.

The fix for each risk varies. Some require stricter input validation. Some require limiting what the AI is allowed to do. Some require better monitoring of what the model is actually producing at runtime.

This writeup walks through all ten risks in plain language, making it a useful entry point for anyone building with AI who wants to understand the attack surface before something goes wrong.