Lion Cipher Lab
Add guardrails to your LLM app: filter what goes in and what comes out
- AI Security
- Beginner
- ~50 min
- Free · no account required
Why it matters
Once you've seen your own LLM app misbehave, the next question is obvious: what stops it from happening in front of a real user? Guardrails are the answer — the validation and filtering layer that sits around the model and inspects traffic on the way in and on the way out. On the way in, a guardrail can flag a prompt that looks like an injection or steer an off-topic request away before it ever reaches the model. On the way out, it can catch a reply that leaks a secret, exposes personal data, turns toxic, or wanders outside what your app is for — and block or rewrite it before it's shown.…
What you'll learn
- what LLM guardrails are and where they sit
- input vs. output validation
- blocking prompt injection at the boundary
- catching PII and secret leakage in output
- filtering toxic and off-topic responses
- the open-source guardrail landscape (NeMo Guardrails, Llama Guard, Guardrails AI)
- writing a simple output check for your own app
- defense-in-depth thinking for AI systems
Steps in this lab
- What a guardrail is, and where it sits
- The four things a guardrail catches
- Why the OUTPUT boundary earns its own guardrail
- The open-source options, and what each is good at
- Add a simple output check to your OWN app
- Level up: use a real validator library
- Guard the input side too
- Guardrails are a layer, not a cure