AI models generate code that runs perfectly but hides critical security flaws. We collect expert reasoning traces to train the next generation of secure AI models.
45%
of AI-generated code contains security vulnerabilities
10.5%
of AI code is actually secure (Dec 2025 benchmark)
0
tools specifically trained to catch AI logic hallucinations
Three steps to help build the missing security layer for AI
We show you real code generated by frontier models (Grok, Claude, GPT-4o) in response to common prompts. The code runs perfectly — but something is wrong.
Identify the vulnerability the AI hallucinated. Was it a broken auth check? An injection? The AI thought it was safe — prove it wrong.
Your explanation of WHY the code is insecure becomes training data for the next generation of AI models. You're teaching the AI to think about security.
Tools like Snyk and SonarQube catch syntax errors. But AI models make logic errors — they confidently write an admin panel that hides the button but leaves the route wide open. These "hallucinated security" patterns aren't in any scanner's rulebook.
Muence collects the reasoning data that teaches AI models to stop making these mistakes. We're not building a better scanner — we're fixing the models themselves.
// The AI wrote this:
{user?.isAdmin && (
<Link href="/admin">
Admin Panel
</Link>
)}
// BUG: Hid the button, forgot to protect the route
// Any user can type /admin and get full access
5 challenges. Real AI-generated vulnerabilities. Can you spot them all?
Start Your First Challenge