The $10M Mistake: Deconstructing the Tea App & Enrichlead Disasters

Two startups. Zero specs. One brutal lesson on why "Vibe Coding" is dangerous.

2025 was the year "Vibe Coding" hit the wall. The promise was seductive: Just tell the AI what you want, and it builds it. No technical co-founder needed. No specs required.

Then the reality check arrived.

Within months, we watched high-profile startups implode not because the AI couldn't write code, but because it wrote dangerous code. The most damning examples? The Tea Dating App data breach and the collapse of Enrichlead.

These weren't "glitches." They were structural failures caused by the absence of one thing: A Technical Spec.

Case Study 1: The Tea Dating App Data Breach

The Incident

Tea, a safety-focused dating app for women, suffered a catastrophic breach that leaked 72,000 sensitive images—including government-issued driver's licenses and verification selfies.

As reported by TechCrunch and the BBC, this wasn't a sophisticated hack. It was a failure of basic configuration.

The Root Cause: The "Happy Path" Problem

The app was built rapidly using AI tools. The founders likely prompted the AI with something like: "Build a feature where users upload an ID photo to verify their account."

The AI did exactly that. It wrote code that:

But because the AI was optimizing for functionality rather than security, it defaulted to the path of least resistance: An unauthenticated Firebase bucket.

The AI didn't "fail." It succeeded at the prompt. But because the prompt lacked a negative constraint ("Ensure these files are NOT public"), the AI left the digital front door wide open.

Case Study 2: The Collapse of Enrichlead

The Incident

Entrepreneur Leonel Acevedo famously built Enrichlead, a SaaS platform, with "zero handwritten code" using Cursor. It was the poster child for Vibe Coding.

It lasted less than a week.

Within days of launch, Acevedo had to shut it down. Attackers had bypassed his paywalls and flooded his API, maxing out his credit cards.

The Failure: Missing Guardrails

When you ask an AI to "build a payment page," it builds the UI and the Stripe connection. It rarely asks:

"Should I implement server-side validation to prevent users from manipulating the client-side code to bypass the paywall?"

The AI built a functional app, but a defenseless one. It lacked Rate Limiting and Server-Side Auth because those are architectural decisions, not code snippets. Without a spec defining these constraints, the AI assumed they weren't needed.

The ProdMoh Fix: Security is a Constraint, Not a Feature

These disasters prove that you cannot let an LLM guess your architecture. You must inject the rules first.

This is the core of Spec-Driven Development with ProdMoh.

How ProdMoh Would Have Prevented This

If the Tea App team had used ProdMoh, they wouldn't have just "prompted" a feature. They would have generated a Golden Context spec first.

A ProdMoh Spec explicitly defines:


security_model:
  storage:
    default_permission: "private"
    access_control: "authenticated_read_only"
    encryption: "at_rest"
  api:
    rate_limit: "100_req_per_min"
    auth_strategy: "server_side_validation"

When this context is injected into Cursor via the ProdMoh MCP:

  1. The AI is forbidden from creating public buckets.
  2. The AI is forced to wrap API endpoints in rate-limiters.
  3. The ProdMoh Score flags the spec as "Unsafe" if these rules are missing—blocking code generation before it starts.

Stop Gambling. Start Spec-ing.

Jason Lemkin’s database wipe. Tea’s leak. Enrichlead’s shutdown. These are not random accidents. They are the cost of "Vibe Coding."

Security is not a feature you add later. It is a constraint you inject first.

Don’t build a $10M mistake. Build with ProdMoh, and ensure your AI knows the rules before it writes a single line of code.

Start Spec-Driven Development today.