Production Readiness Guide
7 ways vibe-coded apps break in production
Cursor, Bolt, Replit, and their peers have made something genuinely useful possible: a founder with a clear idea can create a working prototype without a dedicated engineering team. The demo works. Users respond. The next step seems obvious.
Then production pressure arrives. The problems that emerge are not random. They tend to follow the same patterns across AI-assisted applications: secrets, auth gaps, fragile dependencies, missing observability, scaling limits, codebase drift, and weak deployment process.
The risk is measurable. Escape.tech reported finding 2,038 highly critical vulnerabilities, more than 400 leaked secrets, and 175 exposed PII instances across 1.4K vibe-coded applications. The core problem is simple: AI coding tools are optimized to make things work, but production software also has to be safe, scalable, and maintainable.
Use this as a pre-launch checklist
If your prototype is moving toward real users, payments, sensitive data, or investor diligence, these are the seven areas worth reviewing before launch. Recognizing them is not a reason to panic. It is a way to make the work concrete while the cost of fixing it is still manageable.
Failure mode 1
Hardcoded secrets and credential sprawl
What it is
API keys, database passwords, auth tokens, and third-party credentials embedded directly in source code, committed to version control, or shipped inside browser-accessible bundles.
Why AI tools produce it
AI coding tools optimize for a working connection. Unless the prompt explicitly asks for proper secret handling, the fastest path is often a literal key in a config file or API call.
What it costs if you ignore it
Exposed credentials can lead to cloud cost spikes, fraudulent transactions, database exposure, or user data leaks. Public commits are scanned quickly, and old credentials often remain valid far longer than teams realize.
How to fix it
- Scan the full repository and commit history for secrets, not only the current files.
- Move secrets into environment variables and a real secrets manager.
- Rotate every exposed key, even if it appears unused.
- Add pre-commit and CI checks with tools such as GitGuardian or TruffleHog.
Failure mode 2
Broken authentication and authorization logic
What it is
The app can tell who is logged in, but it does not reliably enforce what each user is allowed to see, change, delete, or export.
Why AI tools produce it
Authentication has common examples. Authorization depends on your business rules: tenants, roles, ownership, record boundaries, internal staff access, and edge cases that live outside generic training data.
What it costs if you ignore it
Broken authorization is how one customer reads another customer's data, users modify records they do not own, and technical diligence turns into an uncomfortable conversation.
How to fix it
- Map every API endpoint to the roles and ownership rules that should apply.
- Enforce authorization on the server for every sensitive read and write.
- Review endpoints that accept user IDs, organization IDs, or record IDs.
- Test with valid but unprivileged users trying to access other users' resources.
Failure mode 3
Hallucinated dependencies and supply chain risk
What it is
AI tools suggest packages that do not exist, packages that are outdated, or packages that pull in risky transitive dependencies several levels deep.
Why AI tools produce it
Language models generate plausible package names from patterns. They do not always verify a live registry before recommending a dependency.
What it costs if you ignore it
A malicious or abandoned package can exfiltrate environment variables, compromise a build, or introduce vulnerabilities that are hard to spot from a quick package.json review.
How to fix it
- Verify every AI-suggested package against the registry before installing it.
- Commit lockfiles and use automated dependency scanning.
- Flag packages with thin documentation, low adoption, or suspicious publication history.
- Review the full dependency tree, not only direct dependencies.
Failure mode 4
No error handling, logging, or observability
What it is
The app works on the happy path, but production failures are invisible, raw errors leak to users, and there is no clear trail when something breaks.
Why AI tools produce it
Observability does not make the demo look better. It is a deliberate production concern that needs context about what can fail and what the team must know when it does.
What it costs if you ignore it
Your first alert may be a user complaint. Without logs, error tracking, request IDs, or uptime checks, diagnosing a real outage becomes guesswork.
How to fix it
- Add error tracking with context for unhandled exceptions.
- Use structured logs with request IDs and relevant user or account context.
- Monitor uptime, latency, slow queries, and background jobs.
- Add audit logs for sensitive actions and administrative access.
Failure mode 5
Architecture that cannot scale
What it is
The app handles one user at a time, but database queries, file processing, background work, and external API calls collapse under real usage.
Why AI tools produce it
AI tools answer the prompt in front of them. They usually do not ask how the data will be queried, what should run asynchronously, or where the system will bottleneck.
What it costs if you ignore it
Performance issues become user experience problems, infrastructure cost problems, and product velocity problems once production data is involved.
How to fix it
- Profile database queries under realistic load and add missing indexes.
- Move long-running work to queues or background jobs.
- Cache data that does not need to change on every request.
- Load test a staging environment before launch.
Failure mode 6
Iterative degradation
What it is
Prompt after prompt, the codebase accumulates duplicated logic, contradictory patterns, inconsistent validation, and bugs introduced by changes that do not account for earlier decisions.
Why AI tools produce it
Each AI-generated change can be locally coherent while making the whole system harder to reason about. Nobody designed the system; it accumulated.
What it costs if you ignore it
Velocity slows because every new feature becomes riskier. During diligence, this shows up as a maintainability problem, not just a security problem.
How to fix it
- Review the codebase as a whole before launch, not feature by feature.
- Consolidate duplicated logic and inconsistent patterns.
- Establish conventions for validation, error handling, data access, and naming.
- Add tests around authentication, payments, sensitive data, and high-change flows.
Failure mode 7
No deployment infrastructure or recovery plan
What it is
The app is live because someone ran a command, but there is no staging environment, automated test gate, rollback plan, backup strategy, or repeatable deployment process.
Why AI tools produce it
Deployment and recovery are operational systems. AI coding tools generate application code, but they do not automatically create the process that keeps production safe.
What it costs if you ignore it
Every deploy is a live experiment. A bad release, accidental deletion, or failed migration can turn into extended downtime because there is no rehearsed way back.
How to fix it
- Set up CI/CD with tests that block unsafe deploys.
- Create a staging environment that mirrors production closely enough to matter.
- Automate backups and test restore procedures.
- Document rollback steps before the first urgent incident.
What to do if you recognize your app in this list
Most founders will recognize at least a few of these risks in their own product. That does not mean the app is doomed. It means the next step is a structured assessment: which problems are present, how severe they are, and what order they should be fixed in.
The pattern we see most often is founders knowing something is fragile, but spending weeks on the wrong fixes while the real risks stay hidden. A focused technical review turns that uncertainty into a prioritized remediation plan.
Related checklist
Ready to audit your app before launch?
Once you know the common failure modes, the next step is a systematic audit and production-readiness pass. Use the launch checklist to work through security, dependencies, architecture, deployment, observability, and recovery in order.
Open the production audit checklistNext Step
If your product is real but the technical foundation is not ready, start with discovery.
ScaleProof Labs works with early-stage founders to review vibe-coded and AI-assisted codebases, identify production risks, and build a practical plan to close the gaps before they become incidents.