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
Guardrails regex filters and output scanners all work post-sampling. They can catch known patterns but they are always one step behind. The jailbreak already happened at the logit level before the guardrail ever saw the text.
The Logit-Level Approach
Instead of inspecting inputs or outputs we intercept the probability distribution itself. Using Aho-Corasick pattern matching on the GPU we can shadow-ban token sequences before they are ever sampled. This is proactive not reactive.
from resklogits import LogitProcessor
processor = LogitProcessor(patterns=["ignore previous instructions", "you are now"])
processed_logits = processor.process(logits)
Under 1ms for 10000+ patterns on modern hardware. No latency hit at inference time.
Links
- GitHub: https://github.com/Resk-Security/resk-logits
- PyPI: https://pypi.org/project/resklogits
- Site: https://resk.fr
The Bottom Line
Audits and guardrails have their place but they cannot be your only line of defense. Real LLM security requires operating where the decisions are made: the logit distribution.
Try resk-logits today and close the gap.
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