Most “AI security tools” claim they can find vulnerabilities. So I decided to test one under real conditions. I got access to Hakira, an AI-powered security testing platform, and ran a full audit on a deliberately vulnerable banking application. The outcome? 35 validated vulnerabilities • 15 Critical • 12 High • 8 Medium But the numbers aren’t the real story. The depth of analysis is. Hakira didn’t just surface issues, it reconstructed how an attacker would actually break the system: → Multiple authentication bypass paths (JWT misconfigurations, SQL injection, mass assignment) → Full privilege escalation to admin-level access → Economic exploits (race conditions, balance manipulation, replay attacks) → Exposure of sensitive data, including plaintext credentials → AI-layer vulnerabilities, including prompt injection leading to data leakage More importantly, each finding wasn’t theoretical. The platform generated working proof-of-concepts, showing exactly how these vulnerabilities could be exploited in practice. That’s the difference between scanning and actual security analysis. One standout insight: The system mapped multiple independent attack paths to full admin compromise. Not just “there is a bug”, but “here’s how an attacker chains it into total control.” That level of reasoning is what makes tools like this valuable. Now, it’s not without limitations. I attempted to run a second audit on a different codebase to evaluate consistency, but hit credit limits. Expanding initial testing capacity would make early evaluation more robust. Still, for a first audit? This is solid. If you’re building in Web2, Web3, or integrating AI into your stack, security isn’t something you patch later. It’s something you design for. Tools like @hakiraio are pushing in that direction. Try it yourself and see what your code might be exposing.