The Day I Vibe-Hacked a $6.6B Platform's Showcase App
Let me tell you something that keeps me up at night in 2026. It's not sophisticated nation-state attacks or zero-day exploits. It's something far more mundane—and far more dangerous. It's the blind trust we're placing in AI-generated code that "just works." I recently tested an app showcased by Lovable, the $6.6 billion vibe coding platform everyone's talking about. What I found wasn't just a bug or two. It was a systemic failure that exposed 18,697 real users across three continents. And when I tried to report it? Lovable closed my support ticket. This isn't just about one app. It's about an entire generation of software being built on shaky foundations.
What you're about to read isn't theoretical. It's hands-on, real-world testing of what happens when we prioritize "good vibes" over good security. I spent a few hours with an EdTech app that Lovable proudly features—an app with 100,000+ views on their showcase, used by students at UC Berkeley, UC Davis, and schools across Europe, Africa, and Asia. What I discovered should make anyone using these AI-assisted platforms seriously reconsider their security posture.
When Authentication Logic Goes Backwards
Here's where things get truly concerning. The authentication logic in this app was literally backwards. I'm not using hyperbole here—the code was doing the exact opposite of what it should. Instead of blocking unauthorized users and allowing logged-in ones, it was blocking logged-in users and letting anonymous visitors through critical functions. This isn't just a bug. It's a fundamental misunderstanding of how authentication should work.
Think about that for a second. You have an EdTech platform handling potentially sensitive student data, and the gatekeeper is facing the wrong direction. It's like installing a security door that only locks when you're inside your house. From what I've seen in testing dozens of these AI-generated applications, this pattern isn't uncommon. The AI understands the syntax of authentication checks but sometimes misses the semantic meaning—the actual intent behind the code.
The 16 Vulnerabilities That Shouldn't Exist
Let's break down what I found in those few hours of testing. Six critical vulnerabilities. Not minor issues, but problems that could lead to direct data exposure or system compromise. The authentication flaw was just the most glaring example. There were injection vulnerabilities, improper error handling that leaked system information, and authorization issues that would make any security professional cringe.
What's particularly telling is how these vulnerabilities clustered. They weren't random, isolated issues. They formed patterns—the kind of patterns you see when code is generated without understanding the broader security context. For instance, multiple endpoints had the same authentication bypass pattern. It wasn't just one mistake copied and pasted; it was a fundamental misunderstanding baked into the application's architecture.
Why Vibe Coding Platforms Are Creating Security Debt
Here's the uncomfortable truth about vibe coding in 2026: it's creating security debt at an unprecedented scale. Platforms like Lovable promise rapid development—and they deliver. But they're delivering applications with security flaws that would be caught in any traditional code review process. The problem isn't that AI can't write secure code. The problem is that the current generation of vibe coding tools prioritizes "working" over "secure."
From my experience testing these platforms, there's a pattern emerging. The AI generates code that passes basic functionality tests. It looks right. It even works for happy-path scenarios. But security isn't about the happy path. It's about all the edge cases, the unexpected inputs, the malicious actors probing for weaknesses. And that's where AI-generated code consistently falls short—because it hasn't been trained on failure modes, only on success patterns.
What Was Actually Exposed (And Why It Matters)
When we talk about 18,697 users being exposed, what does that actually mean? In this case, it meant user profiles, potentially including educational records, contact information, and system access. For an EdTech platform, this isn't just a privacy issue—it's potentially a FERPA violation waiting to happen. Students at prestigious universities, across multiple countries, with different data protection regulations.
But here's what really bothers me. This app was showcased. Featured. Presented as a success story. Lovable wasn't hiding this application in some dark corner of their platform. They were putting it front and center as an example of what their technology could accomplish. And that means they either didn't security test it themselves, or they did and decided the risks were acceptable. Neither option is particularly comforting.
The Platform's Response: Closing Tickets, Not Fixing Problems
Now let's talk about Lovable's response. Or rather, their lack of response. When I submitted a detailed report of these vulnerabilities through their official support channel, they didn't engage. They didn't ask for more information. They didn't thank me for responsible disclosure. They closed the ticket. Full stop.
This response pattern is becoming increasingly common with AI-powered platforms. There's a disconnect between the marketing ("Anyone can build secure apps!") and the reality ("We're not equipped to handle security reports"). In traditional software development, responsible disclosure processes are well-established. Security researchers report vulnerabilities, companies acknowledge them, patches are developed. With vibe coding platforms, that chain seems to be broken at multiple points.
How to Protect Yourself When Using AI-Generated Applications
So what can you actually do about this? If you're using applications built on these platforms—or if you're building them yourself—here are some concrete steps you can take right now. First, assume that AI-generated code has security flaws. That's not being pessimistic; it's being realistic based on the evidence we're seeing in 2026.
Second, implement your own security testing layer. Even if the platform claims their code is secure, verify it yourself. Run basic penetration tests. Check authentication and authorization flows. Look for injection vulnerabilities. These don't require expensive tools—there are plenty of open-source options available. Third, consider hiring a security professional to review critical applications. Yes, it costs money. But compare that cost to the potential liability of exposing 18,000 users' data.
If you're building applications yourself using these platforms, you might want to hire a security consultant on Fiverr to review your authentication logic specifically. Sometimes a fresh pair of expert eyes can catch what you—and the AI—might miss.
The Human Element: Why We Can't Outsource Security to AI
Here's my fundamental concern with where vibe coding is heading in 2026. We're not just outsourcing code generation to AI. We're outsourcing critical thinking about security. And that's a problem because security isn't just about writing correct code. It's about understanding threats, anticipating attacks, and thinking like an adversary. These are fundamentally human skills—at least for now.
The authentication flaw I found is a perfect example. An AI can generate code that checks if a user is logged in. But understanding why that check needs to be there, what happens if it fails, and all the ways it could be bypassed? That requires context and experience that current AI systems simply don't have. We're treating AI like it's a junior developer who needs supervision, but we're giving it senior-level responsibilities without the oversight.
What This Means for the Future of Software Development
Looking ahead, I see two possible paths. Path one: Vibe coding platforms mature their security practices. They implement mandatory security reviews for showcased applications. They establish proper responsible disclosure processes. They acknowledge that with great power (to generate code quickly) comes great responsibility (to ensure that code is secure).
Path two: We continue down the current road. More vulnerabilities. More exposed users. More closed support tickets. And eventually, a major breach that forces regulatory intervention. Personally, I'm hoping for path one. But based on what I've seen so far, we're currently on path two. The incentives are misaligned—platforms make money by showcasing successful applications, not by highlighting their security flaws.
Your Action Plan for 2026 and Beyond
Let me leave you with something practical. If you're concerned about the security of AI-generated applications—and you should be—here's what you can do today. First, audit any applications you're currently using that were built with vibe coding tools. Pay special attention to authentication and data access controls. Second, if you're building applications, implement security testing from day one. Don't assume the platform has you covered.
Third, consider using tools that can help you automate security testing. For example, you could use Apify to build automated security scanners that regularly test your applications for common vulnerabilities. Automation won't catch everything, but it can help identify the low-hanging fruit. Fourth, educate yourself about secure coding practices. Just because the AI is writing the code doesn't mean you shouldn't understand what makes that code secure or vulnerable.
Finally, if you're interested in learning more about secure development practices, consider picking up Secure Coding Books. Having a solid foundation in security principles will help you better evaluate AI-generated code.
The Bottom Line: Trust, But Verify
What happened with that Lovable-showcased app isn't an isolated incident. It's a symptom of a larger problem in how we're adopting AI-assisted development tools. We're moving fast, but we're breaking things that shouldn't be broken—like the security and privacy of 18,697 users.
The lesson here isn't that vibe coding is inherently bad. It's that any tool powerful enough to transform how we build software needs correspondingly powerful safeguards. And right now, those safeguards aren't keeping pace with the technology. Until they do, approach AI-generated applications with healthy skepticism. Test them thoroughly. Assume they have vulnerabilities. And remember that when a platform showcases an application as a success story, they're showing you the best they have to offer. If that's what their best looks like from a security perspective, imagine what their average looks like.
In 2026, we have more power to create software than ever before. With that power comes responsibility—responsibility that platforms, developers, and users all share. The vulnerabilities I found were in the code. But the real vulnerability was in the process. And that's something no AI can fix for us.
