Topic starter
19/08/2026 3:33 pm
TL;DR: AI-generated code can pass functional tests while failing security checks, with one Carnegie Mellon benchmark finding 61% functional pass rates but only 10.5% security pass rates, according to Equixly's analysis of recent research. That gap means security must be validated against the running application, not assumed from prompts or source review alone.
Editorial analysis by NHI Mgmt Group, based on content published by Equixly: “Vibe coding security: The gap between “It works” and “It’s safe””.
By the numbers:
- 75% of developers use some form of vibe coding, and 90% find it useful.
Key questions
Q: What is the biggest security failure mode in vibe-coded applications?
A: The biggest failure mode is not that the code fails to run.
Q: Why do AI-generated apps often fail enterprise security reviews?
A: They usually start from functional code and basic authentication, but enterprise reviews look for control boundaries, evidence, and lifecycle management.
Q: What do security teams get wrong about AI-generated code risk?
A: They often focus on catching insecure output after code is written, which is too late for AI-native workflows.
Practitioner guidance
- Define security acceptance criteria before generation Write explicit controls for authentication, access checks, secret handling, and data exposure before prompting the coding agent.
- Scan generated code for hardcoded secrets Make secret scanning mandatory in CI/CD and block releases that contain API keys, database passwords, JWT secrets, or other reusable credentials.
- Test the running application with adversarial cases Use dynamic testing and continuous penetration testing to probe authentication logic, broken object-level authorization, unexpected input handling, and error-message leakage in the live application.
Bottom line: Vibe coding turns software delivery into an assurance problem because code can be functional and still unsafe at the same time.
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19/08/2026 4:15 pm
Vibe coding creates an assurance gap, not just a development shortcut. The central issue is that generated code can satisfy functional intent while remaining insecure under attack conditions. That means the governance question is no longer whether the feature works, but whether the runtime proves access, identity, and data-handling decisions correctly.
A question worth separating out:
Q: How should organisations govern AI coding agents in the SDLC?
A: Treat them as privileged non-human identities with named owners, narrow task scope, and explicit offboarding. Governance should align access to the exact development activity, not to a broad team role or permanent environment trust. That is the difference between using the agent safely and leaving it as an unowned execution path.
👉 Read our full editorial: Vibe coding security exposes the gap between working and safe code