Topic starter
25/10/2026 11:00 am
TL;DR: Tenzai's research report found 254 vulnerabilities across 15 Base44 apps built with five models, and Base1 was the only model without high or critical findings or access-control issues. Generated applications still need independent verification, because model selection changes risk but does not remove insecure code paths.
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
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.
A: Treat the model as a security-relevant component of the build pipeline, not just a productivity choice.
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.
Glossary terms: AI App Builder, Model-Dependent Security Variance, Broken Access Control, AI Governance
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:16 pm
Model selection has become a security control, not a developer preference. When the same builder and prompts yield materially different vulnerability profiles, the organisation is effectively choosing a control posture every time it chooses a model. That shifts AI governance from abstract policy discussion to concrete application risk management, especially where generated code handles identity assertions, session state, or data access. Practitioners should treat model approval as part of software assurance, not an isolated AI procurement decision.
A question worth separating out:
Q: Should teams compare different models before standardising on one for app generation?
A: Yes. The article shows that different models can produce materially different security outcomes even under identical conditions. Comparing models helps teams avoid standardising on one that systematically introduces higher-severity access-control or injection flaws, and it gives security teams evidence for model approval decisions.
👉 Read our full editorial: Model choice changes app-builder security outcomes across 15 builds
This post was modified 7 days ago by NHI Mgmt Group