Every team shipping an LLM feature has the same quiet worry: what happens when someone tries to jailbreak our chatbot? And almost nobody has a real answer, because "is our system prompt resilient?" usually gets settled by gut feeling and a couple of manual pokes.
I wanted a feedback loop instead. So I built a Prompt Injection Tester: paste your system prompt, and it runs five classes of attack against it, then hands you a resilience score with the actual model responses as evidence.
The five attack classes
Instruction override — "Ignore all previous instructions and…" The classic. Still works more often than you'd like.
Prompt extraction — coaxing the model into leaking its own system prompt. If your prompt contains rules, examples, or hints about internal tools, this is how they get out.
