From the course: Securing Generative AI: Strategies, Methodologies, Tools, and Best Practices

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Understanding insecure output handling attacks

Understanding insecure output handling attacks

From the course: Securing Generative AI: Strategies, Methodologies, Tools, and Best Practices

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Understanding insecure output handling attacks

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- [Instructor] Insecure output handling occurs whenever there's inadequate validation and sanitation, and also the processing of outputs that are generated by the AI application or large language models. And these are failures of validation before their pass on the systems or components that they're interacting since the outputs or the inference can be influenced by user prompts. And, of course, giving users indirect access to additional systems and functionalities is one of the issues that we have in here. So unlike over-reliance, which focuses on the broader risk of depending too much on the accuracy and the reliability of the LLM outputs, in this case, this vulnerability, insecure output handling, is specifically concerned with the handling of those outputs before they're moving downstream to other systems. Whether it's to a web browser or to a system shell and so on. Exploiting an insecure output handling vulnerability could lead into security issues like cross-site scripting or…

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