LLM Audits and Guardrails Are Not Enough: Why You Must Filter at the Logit Level

The Blind Spot in LLM Security

Every week a new jailbreak bypasses the latest guardrail. Every month another audit reveals training data contamination. These approaches share a fundamental flaw: they operate on the wrong layer of the stack.

Why Audits Fall Short

Audits examine what went into the model training data and what came out as final text. But the model does not produce text directly. It produces a probability distribution over tokens at each generation step. By the time you audit the output the token is already delivered to the user.

Why Guardrails Are Reactive