A drop-in guardrail layer for LLM apps: the input guard blocks prompt injection and jailbreak attempts before they reach the model; the output guard redacts leaked secrets and PII before they reach the user. Measured on the committed sample battery: 23/23 attacks blocked, 0/17 false positives on benign lookalike prompts, 10/10 leaked secrets redacted with 0/6 clean outputs mangled. A guard that blocks real users is just a different kind of outage - so the false-positive number matters as much as the catch rate. Public code: github.com/jigonyoo/llm-guardrails