Why it matters
NVIDIA's AI Red Team organizes LLM application risk around prompt injection, information leakage, and probabilistic failure. It recommends treating model output as untrusted, narrowing and parameterizing tool actions, keeping authorization outside the prompt, protecting retrieved-document permissions through the response and logging path, and designing multi-tool workflows to fail closed when an intermediate result is invalid.
My takeaway: Never place secrets or authorization claims in model-visible context. Enforce user and document permissions outside the LLM, give tools narrow schemas and separate identities, re-authorize sensitive actions, protect prompt and completion logs at the source data's access level, and stop an orchestration chain on malformed output instead of compounding the model's error into a privileged action.