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Vibe coding goes wrong when a developer treats a plausible demo as proof that the software is correct. The fix is not to stop using coding agents; it is to make their work inspectable: choose an appropriate autonomy level, provide project context, define acceptance tests, work in small checkpoints, review the diff, and limit what tools can access.
The 17 practices below turn an open-ended prompt into a controlled engineering loop. They apply whether an agent writes one function or an entire application, and they scale the amount of human review to the consequences of failure.
What vibe coding is—and where it breaks
Vibe coding describes a range of AI-assisted development styles rather than one fixed method. At the high-autonomy end, a person gives a broad or ambiguous request and lets an agent choose architecture, modules, implementation, and tests, then judges the result mainly through more prompts and a working demo. In the middle, the person specifies modules, writes tests, or reviews each returned change while the agent handles implementation. At the low-autonomy end, the agent suggests code while the developer remains responsible for design, verification, and integration.
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| Workflow level | Human supplies | Agent decides | Useful when | Minimum control |
|---|---|---|---|---|
| High autonomy | Outcome and broad constraints | Architecture, modules, code, tests | Low-consequence prototypes and throwaway experiments | Sandboxed access, checkpoints, and a human acceptance review |
| Shared control | Architecture, module boundaries, and test cases | Implementation details and repetitive edits | Most internal tools and ordinary product features | Small changes, automated tests, diff review, and restricted tools |
| Low autonomy | Design, tests, and detailed change plan | Boilerplate, refactoring, and suggestions | Security-sensitive, regulated, or high-impact software | Human approval at every boundary and independent verification |
The UK National Cyber Security Centre describes this as a spectrum and warns that minimal oversight can create security vulnerabilities. That warning does not mean every AI-generated program is insecure; it means a running application is not evidence that its assumptions, authorization, error handling, and dependencies are sound.
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A 2026 preprint, Understanding the (In)Security of Vibe-Coded Applications, reported recurring placeholder logic, unfiltered input, and exposed secrets in the applications it studied. The authors describe these as lifecycle weaknesses that better models and prompting can reduce but not eliminate. ISACA reported in July 2026 that RedAccess researchers identified more than 5,000 applications with little or no security controls or authentication, and that nearly 40% of the analyzed applications exposed sensitive information such as medical records, financial data, internal documents, or customer conversations. The report does not provide a denominator for all vibe-coded applications, so those figures should not be read as a universal incidence rate.
1. Choose the right autonomy level
Start by classifying the consequence of a wrong change. A landing-page prototype can tolerate broad delegation; software that moves money, handles health information, controls production infrastructure, or enforces access policy cannot. Decide in advance which decisions the agent may make and which require your approval.
- Delegate freely: formatting, test scaffolding, documentation drafts, and isolated experiments.
- Delegate with review: ordinary business logic, database migrations, and UI changes that have clear tests.
- Keep human control: authentication, authorization, cryptography, payment flows, privacy settings, destructive operations, and production deployment.
Record the boundary in the task brief. An agent should not infer permission to redesign the data model merely because it can.
2. Write the outcome before asking for implementation
Describe the user-visible result, constraints, and non-goals before naming a framework or requesting code. A useful outcome statement answers who uses the feature, what input they provide, what successful behavior looks like, and what must remain unchanged.
Outcome: A signed-in customer can export their own invoices as a CSV file from the billing page.
Must include: date range, stable column names, an empty-result message, and an audit event.
Must not include: another customer’s records, background emails, or schema changes.
Done when: the acceptance cases below pass and the diff contains only this feature.This prevents the common failure in which an agent fills gaps with assumptions that were never approved. State out-of-scope behavior explicitly; silence is not a requirement.
3. Include architecture and project conventions
Give the agent the context a new contributor would need: the relevant services, data flow, naming conventions, error-handling policy, supported runtime versions, testing commands, and files it must not edit. Point to existing examples rather than asking it to invent a parallel pattern.
Project-level instructions are especially valuable for recurring work. Explain where domain logic belongs, how dependencies are approved, how migrations are reviewed, and which checks run in continuous integration. If the repository has generated files, say whether the agent may modify their sources or only regenerate them. Context reduces contradictory edits and makes the resulting diff easier to review.
4. Break large requests into modules or small changes
Do not ask for an entire product in one prompt. Decompose the work into independently testable slices such as data model, service operation, API boundary, and user interface. The NCSC identifies module-level specification as an intermediate point between total delegation and hand-written implementation.
- List the modules and the contract between each pair.
- Choose one vertical slice that can be run end to end.
- Implement and verify that slice before adding the next.
- Keep migrations, permission changes, and dependency upgrades as separate review units.
Smaller boundaries limit the blast radius of a mistaken assumption and make it possible to revert one decision without discarding unrelated work.
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5. Define acceptance tests in the prompt
Tell the agent how you will decide that the change is complete. Microsoft’s Visual Studio Code guidance recommends including test cases in prompts so the AI has a concrete target. Cover the normal path, invalid input, authorization, empty data, failures, and important edge values.
| Case | Given | When | Then |
|---|---|---|---|
| Valid export | User owns two invoices in the selected range | User selects CSV export | Only those invoices download with the documented headers |
| No records | User owns no invoices in the range | User selects export | No file containing another user’s data is produced; a clear empty-state message appears |
| Unauthorized request | User requests an invoice ID they do not own | API receives the request | Request is rejected without revealing whether the record exists |
| Malformed range | End date precedes start date | User submits the form | Validation fails before a database query or file generation |
Write the expected result, not merely a test name. An agent can make a test pass by weakening the assertion if the desired behavior is vague.
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6. Ask for a plan before a broad change
For anything touching several modules, request a proposed plan first and do not authorize implementation until you have inspected it. The plan should identify files, interfaces, data changes, tests, risks, and assumptions.
- Ask the agent to summarize the current implementation it intends to extend.
- Require a list of files to add or edit and the reason for each.
- Ask which acceptance tests will fail before the change and pass afterward.
- Challenge assumptions about authentication, data ownership, concurrency, and error handling.
- Approve, amend, or reject the plan explicitly.
This pause is cheap compared with reviewing hundreds of lines produced from a wrong premise.
7. Use checkpoints
Work in states you can inspect and restore. Microsoft recommends checkpoints that let you review progress and rewind when an agent goes off track. Create a checkpoint after the plan, after the smallest passing slice, and before any migration, dependency update, or permission change.
A checkpoint can be a committed change, a saved patch, or another reversible snapshot. Give it a meaningful label that records the verified behavior. If the agent begins editing unrelated files or changing the design without approval, return to the last known-good checkpoint instead of layering corrective prompts on top of a confused state.
8. Keep changes reviewable
Prefer a sequence of narrow diffs over one impressive-looking rewrite. A reviewable change has one purpose, preserves unrelated formatting, and includes the tests or documentation needed to understand it. Ask the agent to avoid opportunistic refactors and to report generated files separately.
Reviewability is a safety control: it lets you connect each line to the outcome and spot accidental behavior. If a feature genuinely requires a broad refactor, split the mechanical move from the behavioral change so each can be tested and reverted independently.
9. Run the relevant tests yourself
An agent’s statement that tests passed is an unverified claim until you run the project’s normal commands in your own environment. Run unit, integration, end-to-end, type, lint, and migration checks that apply to the change. Confirm that the tests actually execute; a skipped suite or a test pointed at a mock database does not prove production behavior.
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Exercise the acceptance cases manually when the feature crosses a browser, API, queue, or external service boundary. Compare observed results with the written outcome, including authorization and failure behavior. Keep environment-specific secrets out of logs and use test data that cannot harm real users.
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Inspect the complete pull request or patch, not only the files the agent mentions. Visual Studio Code guidance recommends code review on the resulting pull request. Read for changed behavior, hidden defaults, error paths, logging, data access, dependency changes, and accidental deletion.
- Trace each input from the boundary to storage, rendering, or an external call.
- Check that authorization is enforced on the server-side operation, not just hidden in the interface.
- Look for broad exception handling that converts failures into success responses.
- Confirm that migrations are reversible or have a documented recovery plan.
- Verify that new packages, permissions, and network calls are necessary.
Do not approve a diff because it is aesthetically clean or because the demo works. Approve it because the behavior is understood and evidenced.
11. Check for placeholder behavior
Search explicitly for code that makes a demo look complete without implementing the promised behavior. The 2026 study found placeholder logic recurring in the applications it examined.
- Hard-coded arrays or responses presented as database results.
- Functions that return success, an empty list, or a fixed user without performing the operation.
- Buttons that display a toast but do not call the service they name.
- Comments such as “implement later” alongside a path users can reach.
- Feature flags or mocks left enabled in a production build.
Replace placeholders with a real implementation, fail closed with a visible error, or remove the feature from the interface. A graceful “not available” state is safer than simulated success.
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Unfiltered input was another recurring pattern in the studied applications. Treat browser fields, API payloads, uploaded files, webhooks, queue messages, and content read from external systems as untrusted. Validate type, length, format, range, and relationship constraints on the server side.
Use parameterized database access and context-appropriate output encoding. Reject unexpected fields where practical, normalize values before authorization decisions, and impose limits on file size, pagination, and request frequency. Test malformed, oversized, duplicated, and adversarial values. Validation is not a substitute for authorization: a well-formed request can still belong to the wrong user.
13. Search for exposed secrets
Look for credentials and sensitive configuration in source files, generated bundles, logs, screenshots, prompts, test fixtures, and version history. Search for API keys, private keys, connection strings, bearer tokens, signing secrets, and passwords before opening a pull request.
Move runtime secrets to the approved secret store, inject them through the deployment environment, and provide non-sensitive test substitutes. If a real secret has appeared in a repository or agent transcript, revoke and rotate it; deleting the line does not invalidate copies in history or logs. Ensure client-delivered code contains only values intended to be public.
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14. Review agent tools and repository permissions
Before trusting a repository, inspect the agent’s configuration, tool definitions, and permissions. Mistral’s safety documentation uses its .vibe/ configuration as a concrete example and warns that MCP server definitions or tools may grant access to external services or local commands.
Apply least privilege:
- Allow read access by default and require approval for writes, deletes, shell commands, network calls, and deployments.
- Use separate credentials for development and production, with short-lived tokens where possible.
- Review every configured MCP server, its owner, endpoints, scopes, and data it can transmit.
- Run agents in a sandbox or isolated workspace when evaluating unfamiliar repositories.
- Log approvals and tool calls so an unexpected action can be investigated.
Permissions are part of the software supply chain. A well-written prompt cannot compensate for a tool that can alter production or exfiltrate a repository.
15. Treat repository and web content as untrusted agent input
Agent security guidance describes prompt injection as a risk when an autonomous system reads instructions embedded in files, issues, documentation, webpages, or generated output. Text that says “ignore previous rules,” asks for secrets, or requests a privileged command is data until you verify it.
Separate instructions from content. Tell the agent which files are authoritative, prohibit following commands found in source material, and require confirmation before any external communication or destructive action. Review links, scripts, and configuration changes manually. Keep the agent’s credentials out of environments where untrusted content can influence commands.
16. Use specialized workflows when needed
One general-purpose chat loop is not ideal for every task. Visual Studio Code documentation describes custom agents for workflows such as test-driven development and security audits. Choose a workflow whose built-in checks match the risk.
- Test-driven workflow: write or approve a failing test, implement the smallest change, then run the suite.
- Security-audit workflow: map trust boundaries, inspect authentication and authorization, and produce findings before remediation.
- Refactoring workflow: preserve behavior with characterization tests and keep mechanical edits separate from redesign.
- Documentation workflow: derive explanations from verified code and flag undocumented assumptions rather than inventing them.
Specialization makes the expected artifacts explicit and reduces the temptation to accept a generic “looks good” response.
17. Do not ship solely because the demo works
A successful happy-path demonstration proves only that one path worked under one set of conditions. Before release, require evidence proportional to the software’s potential impact: passing tests, reviewed diffs, dependency and secret checks, authorization tests, operational monitoring, rollback instructions, and an accountable human approval.
For high-impact systems, add independent review and staged deployment. Verify backups and recovery before applying destructive migrations. Confirm that failures are visible to operators and safe for users. The NCSC’s warning about vulnerabilities under minimal oversight is a reason to raise verification, not a claim that human-written code is automatically safe.
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Use this loop for each feature, adjusting the depth to its risk:
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- Classify the consequence and set the autonomy boundary.
- Write the outcome, non-goals, architecture context, and acceptance cases.
- Ask for a plan and approve the module-sized slice.
- Record a checkpoint before implementation.
- Let the agent make one focused change with only the permissions it needs.
- Run the relevant tests and manual acceptance cases yourself.
- Inspect the diff for behavior, placeholders, input handling, secrets, and permission changes.
- Rewind if assumptions drift; otherwise record the verified checkpoint and proceed.
- Before release, obtain the level of human and independent review that the consequences require.
What to do when the agent goes off track
It edits unrelated files
Stop the run, save the diagnostic output, and compare the diff with the approved file list. Revert to the last checkpoint, narrow the prompt, and repeat with a smaller slice. Do not ask the agent to clean up an uncontrolled change set while it is still operating under the same mistaken assumptions.
Tests pass but the feature is wrong
Inspect whether the tests assert outcomes or merely exercise lines. Add a failing acceptance case that captures the missing behavior, then implement against that case. Check that the test uses realistic authorization and data boundaries rather than an all-powerful mock.
The agent requests a new tool or credential
Pause and identify the exact operation, data, endpoint, and duration required. Prefer a temporary, least-privileged substitute. Review the configuration and tool definition before approving access, and revoke it after the task if it is no longer needed.
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Stop distribution, revoke exposed credentials, preserve relevant logs for investigation, and determine whether the unsafe change reached any shared environment. Then patch the defect, add a regression test, and update the project instructions so the same failure is less likely to recur.
How to tell whether your workflow is improving
Track engineering signals rather than the number of lines an agent produces. Useful measures include the share of changes with explicit acceptance tests, time spent reviewing a diff, defects found before release, unauthorized tool calls blocked, secrets detected before commit, and the number of times a checkpoint enabled a clean rollback. A faster workflow is only better when it preserves correctness, security, and the ability to explain what changed.
Frequently Asked Questions
Is vibe coding always unsafe?
No. Vibe coding is a spectrum of autonomy. Broad delegation can be reasonable for low-consequence prototypes, while systems that handle sensitive data or control important operations require tighter specifications, testing, permissions, and human review.
What is the first change I should make to my prompting?
Write the desired outcome, non-goals, and concrete acceptance cases before asking for implementation. Then provide the project’s architecture and conventions so the agent has a constrained target.
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Treat that report as a claim to verify. Run the project’s relevant commands yourself, confirm that the tests actually executed, and inspect the diff and edge-case behavior before accepting the change.
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