Lion Cipher Lab
Red-team your own LLM app: find its holes before someone else does
- AI Security
- Beginner
- ~60 min
- Free · no account required
Why it matters
The fastest way to stop shipping vulnerable AI is to attack your own work on purpose, in a controlled way, before it reaches anyone who would attack it for real. Building an LLM into an app quietly opens a set of doors most builders never think to check: text the model reads can hijack it, guardrails can be talked around, private data and system prompts can leak out in an answer, and a model wired up to take actions can be steered into taking the wrong ones. These are not exotic — OWASP maintains a Top 10 specifically for LLM applications precisely because the same handful of failures keep…
What you'll learn
- structured LLM red-teaming
- the OWASP Top 10 for LLM Applications
- testing for prompt injection and jailbreaks
- testing for sensitive-information disclosure
- testing for unsafe tool use / excessive agency
- running an automated LLM scanner (garak)
- mapping findings to mitigations
- rules of engagement and staying in scope
Steps in this lab
- What red-teaming is here, and the one rule that matters
- Your scorecard: the failure classes and the OWASP map
- Set the target and start a findings log
- Class 1: probe for prompt injection and jailbreaks by hand
- Class 2: probe for sensitive-information disclosure
- Class 3: probe for unsafe tool use (excessive agency)
- Widen the net: run garak, the automated scanner
- Turn findings into fixes, then re-test
- What you just built into yourself