The guide outlines key risks associated with large language models and provides strategies to defend against them. Major threats include prompt injection, where attackers manipulate inputs to alter model behavior, and sensitive information disclosure that can expose private data. Other risks involve supply chain vulnerabilities, data and model poisoning, improper output handling, and excessive autonomy that can lead to unintended actions. As LLMs become more integrated into applications, these risks grow in complexity and impact. To mitigate them, organizations must implement strong controls, continuous monitoring, and secure development practices, ensuring safe and reliable use of AI systems while protecting data and maintaining trust.

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