The scenario describes indirect prompt injection: malicious instructions are embedded in third-party content that Claude processes as retrieved evidence. Option A addresses the threat at the appropriate boundary by classifying retrieved material as untrusted, screening it before use, and validating the model’s output before it can influence downstream actions.
Anthropic recommends placing untrusted third-party content in clearly identified tool-result structures, explicitly instructing Claude that retrieved content cannot override system or user instructions, screening tool output for injection attempts, and applying output validation. Mitigate Jailbreaks and Prompt Injections
Citations in Option B improve factual traceability but do not prevent embedded instructions from attempting to redirect model behavior. An outbound destination allow-list in Option C limits the damage that a compromised agent can cause through tool calls, but it does not directly detect or neutralize the injected content. Option D is valuable for detecting behavioral regression after a model change, not for controlling a specific malicious document during runtime.
A production implementation should combine content provenance, structural separation, injection screening, least-privilege tools, output-schema validation, and human confirmation for consequential actions.
Study Guide references/topics: Indirect prompt injection; untrusted retrieved content; input classification; output validation; layered runtime guardrails.
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