11 issues found before a single user onboarded.

The product team had moved quickly from prototype to launch candidate using AI-assisted development across the frontend, backend, and deployment scripts. That speed created exactly the kind of uncertainty most early-stage teams struggle to see clearly: the code looked plausible, the happy paths worked, and nobody had time to review every boundary the AI touched.

The founders were not asking for a long consulting engagement. They needed to know whether the codebase was safe enough for real users, real billing data, and real production traffic before launch day.

That meant checking for the failures AI coding tools introduce most often: hardcoded secrets, broken auth assumptions, unsafe query construction, and logging behavior that exposes more data than intended.

Shellexa ran a full QA and security review across the codebase, focusing on the failure patterns AI coding tools introduce most often. The review surfaced 11 issues in 72 hours, including 4 hardcoded credentials, an authentication bypass in the admin dashboard, a SQL injection in search, and user data being logged without PII masking.

The issue that mattered most was not only the number of findings. It was how normal they looked. Every vulnerable path had been generated in code that appeared clean, readable, and easy to approve in a fast-moving startup workflow.

The final report prioritized fixes by severity, explained why each issue mattered, and gave the engineering team a remediation path they could work through immediately.

• 4 hardcoded credentials in production code
• Authentication bypass on the admin dashboard
• SQL injection in the search endpoint
• User data logged without PII masking

The startup fixed every critical issue in 4 days and still shipped on the original launch schedule. Secrets were moved into managed environment configuration, access checks were tightened around privileged flows, and unsafe query logic was replaced before customer data entered the system.

Launch happened without incident, and the team gained something more valuable than a single report: a clearer model of how AI-generated code fails under pressure. That let them tighten internal review practices before growth compounded the problem.

The result was simple and important. Zero incidents post-launch, no slip in go-live timing, and a cleaner foundation for the next round of product work.