Topic starter
25/10/2026 11:00 am
TL;DR: Tenzai tested three Base44 app specs across five models and found 254 vulnerabilities across 15 applications, with Base1 the only model producing no high or critical findings and no access-control vulnerabilities. Model choice changes the security baseline, but it still does not replace application testing or governance.
Editorial analysis by NHI Mgmt Group, based on content published by Tenzai: “Five Models Walk Into an App Builder. Security Gets Interesting.”.
By the numbers:
- Tenzai uncovered 254 vulnerabilities across the 15 applications.
Key questions
A: Treat the model as a security-relevant component of the build pipeline, not just a productivity choice.
Q: How should teams secure AI-generated applications before they reach production?
A: Treat security as a precondition of generation, not a review step at the end.
Q: Where do AI deployments most often fail in practice?
A: They fail when teams treat AI as isolated innovation instead of production infrastructure.
Practitioner guidance
- Audit each model as a distinct security source Compare vulnerability profiles across models before you standardise on one for app generation, because identical prompts can still produce very different access-control and injection outcomes.
- Require server-side authorisation checks on every generated data path Verify that the application enforces access rules in backend logic, not only in UI flow or prompt instructions, especially for endpoints that return sensitive data.
- Run security testing on the generated application, not the model output Probe the deployed app for unauthenticated access, broken object handling, and injection exposure before any production release.
Bottom line: The article shows that AI app builders can produce materially different security outcomes even when the prompt and platform are unchanged.
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