Pattern-based output monitoring (regex for dollar amounts, company names, known-bad strings) catches 40% of attacks in this test. It’s better than nothing. But the poisoned response in this lab doesn’t trigger any unusual patterns — it reads like a normal financial summary. For output monitoring to be reliable, it needs ML-based intent classification, not regex. Llama Guard 3 and NeMo Guardrails are worth evaluating for production deployments.
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We have several types with progressively less information. This is useful, as you can choose the type you want to represent and don't need to worry about running calculations on any other un-needed data (such as calculating the time if you're only interested in displaying the date).，更多细节参见手游
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