A legal-tech company lets users upload contracts for a Foundry-hosted assistant to summarize. Security testing reveals that a malicious user can embed hidden text inside an uploaded document instructing the model to ignore its summarization instructions and reveal its system prompt instead. The team wants to detect and block this kind of embedded instruction before it reaches the model's reasoning.
Select an answer to reveal the explanation.
Short Explanation
The attack here isn't a rude question typed into the chat box, it's a hidden note buried inside the uploaded file itself, quietly trying to talk the assistant into ignoring its actual job. That calls for a filter built to look for exactly this trick, instructions smuggled inside content the model is supposed to just summarize, and stop them before the model ever acts on them. Cranking up a hate-speech filter doesn't help, since that filter is watching for a totally different kind of harmful language, not sneaky commands. Changing how the deployment is billed has nothing to do with what's inside the document at all. And turning on retrieval grounding so answers cite the source document actually points the wrong direction here, since it could pull the hidden instruction even more directly into the model's context rather than screening it out.
Full Explanation
The correct answer is A. Prompt Shields is designed specifically to scan incoming content, including text embedded inside uploaded documents, for indirect prompt injection attempts and block instructions that try to hijack the model's behavior before they reach its reasoning process, which directly addresses the hidden-instruction attack this team found. Option B is incorrect because a hate-speech severity threshold governs a different category of harmful content entirely; it has no mechanism for recognizing an instruction trying to override the system prompt. Option C is incorrect because the choice between provisioned throughput and pay-as-you-go is a capacity and billing decision, unrelated to detecting malicious instructions embedded in document content. Option D is incorrect because enabling retrieval-augmented generation changes how the assistant sources facts to cite in its answers, but it does nothing to screen the uploaded document for hidden adversarial instructions, and could even make the assistant more likely to ingest the malicious text as part of its grounding context.