Which issue occurs when LLM outputs are not sufficiently validated or sanitized and are passed to other systems?

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Multiple Choice

Which issue occurs when LLM outputs are not sufficiently validated or sanitized and are passed to other systems?

Explanation:
The main idea is that when outputs from a language model aren’t checked or cleaned before being sent to other systems, the downstream components can receive data that is unsafe, malformed, or inappropriate to process. This is improper output handling, because the problem lies in how the generated text is managed after it’s produced—without validation, redaction, encoding, or formatting checks, you can introduce security risks, data leakage, or integration failures. For example, sensitive information might slip through, dangerous payloads could be injected into a system, or downstream APIs might misinterpret unstructured or poorly encoded data. The other possibilities don’t capture this pipeline issue: misinformation is about factual accuracy, system prompt leakage is about prompts showing up in outputs, and unbounded consumption is about outputs growing without bound.

The main idea is that when outputs from a language model aren’t checked or cleaned before being sent to other systems, the downstream components can receive data that is unsafe, malformed, or inappropriate to process. This is improper output handling, because the problem lies in how the generated text is managed after it’s produced—without validation, redaction, encoding, or formatting checks, you can introduce security risks, data leakage, or integration failures. For example, sensitive information might slip through, dangerous payloads could be injected into a system, or downstream APIs might misinterpret unstructured or poorly encoded data. The other possibilities don’t capture this pipeline issue: misinformation is about factual accuracy, system prompt leakage is about prompts showing up in outputs, and unbounded consumption is about outputs growing without bound.

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