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

Short summary

Traditional LLM audits and guardrails operate post-sampling on final text—too late to stop harmful tokens. Resk-logits intercepts the logit distribution during inference using GPU-accelerated pattern matching to proactively block token sequences before sampling. At <1ms per 10k patterns, it adds no latency cost and closes a critical security gap.

• •Current security approaches (audits, guardrails) work too late—after the logit decision is made
• •Logit-level filtering intercepts probability distributions before token sampling using GPU acceleration
• •Resk-logits enables pattern matching with <1ms overhead for 10k+ patterns at inference time

Generated with AI, which can make mistakes.