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What vibe coding means—and what it does not
Vibe coding commonly describes directing an AI coding tool with natural-language prompts and judging the result by running the application rather than carefully reading every line. In practice, it can be iterative: prompt, inspect, test, edit, and repeat. Microsoft Research describes this kind of goal-driven cycle and reports that developers’ trust in AI tools can develop through verification rather than blanket acceptance: Microsoft Research’s study of vibe coding.
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That distinction matters. Using AI to draft code is not the same as accepting its output without review. The important question is whether a person checks the result well enough to take responsibility for it.
Why a working demo is not proof that code is safe
A demo establishes only that the application behaved as expected along the paths you tried, under the conditions you observed. It does not show that the code handles unexpected input, protects sensitive data, enforces the right permissions, or recovers safely from errors.
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Research has examined security weaknesses in agent-generated code on particular real-world tasks. A peer-reviewed benchmark published in Proceedings of Machine Learning Research with ICML 2026 raises concerns about deploying agent-generated code in security-sensitive settings. Its findings apply to the agents and tasks studied; they do not establish a single vulnerability rate for every AI tool or project. See the Proceedings of Machine Learning Research.
A separate 2026 preprint examining vibe-coded applications reports patterns such as placeholder logic, unfiltered input, and exposed secrets. The authors describe these as risks found in their examined applications, not a settled estimate of how often all vibe-coded software is vulnerable. Their work also suggests stronger models and prompting can reduce risks without eliminating them: the 2026 preprint on vibe-coded applications.
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These findings support careful review, not the claim that all AI-generated code is insecure. A feature can appear to work while its implementation still makes unsafe assumptions about inputs, access, or data handling.
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You do not need to memorize every line to understand a change well enough to oversee it. You should be able to explain, in plain language:
- What changed and what the feature is supposed to do.
- What data enters the feature, where it goes, and what leaves it.
- Which permissions, credentials, or privileged operations it uses.
- What happens when input is invalid, a service is unavailable, or an operation fails.
- How you tested the important behavior, including relevant failure cases.
- How someone can diagnose and safely change the code later.
If those questions have no clear answers, a green screen or successful click-through is not enough evidence to deploy the change.
Is vibe coding safe? Match review to the consequences
There is no single review burden that fits every experiment. UK National Cyber Security Centre guidance treats vibe coding as a spectrum and recommends calibrating oversight to the code and its risks: NCSC guidance on vibe coding. A disposable local prototype and a public service handling accounts, personal information, payments, or business operations do not have the same consequences if something goes wrong.
| Context | What to consider | Reasonable review emphasis |
|---|---|---|
| Disposable local experiment | Does it stay local, use mock data, and avoid secrets or privileged access? | Run it, inspect the behavior, and avoid treating a prototype as production-ready. |
| Internal tool or limited deployment | Could a bug expose data, disrupt work, or grant unintended access? | Check data handling, permissions, error behavior, and the paths people will rely on. |
| Public or business-critical service | Does it handle accounts, sensitive information, payments, secrets, or important operations? | Use stronger testing and security review; involve an experienced reviewer when the risks exceed your expertise. |
The table is a decision aid, not a formal NCSC classification. As consequences, data sensitivity, or privileges rise, increase the depth of verification.
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- Define the change. Write down what the feature should do, which users and data it affects, and what it must not do. A vague prompt makes it harder to distinguish a correct result from one that merely looks plausible.
- Inspect the implementation around the change. Trace how inputs are accepted, validated, and used; where data is stored or sent; and which checks control access. Look for placeholder behavior, exposed credentials, or assumptions that are not enforced in code.
- Test expected and failure paths. Try valid and invalid input, unauthorized access where relevant, and realistic failure conditions. Check the behavior rather than relying only on the tool’s explanation of its own code.
- Use automated checks, then review context. Scanners and other automated analysis can flag known patterns, but they cannot reliably decide whether a feature’s logic matches the product’s requirements or whether data flows and permissions make sense. OWASP describes manual secure code review as complementary to automated analysis, especially for application logic and contextual implementation: OWASP Secure Code Review Cheat Sheet.
- Make sure the change can be maintained. Confirm that someone responsible can locate the relevant code, understand its assumptions, and investigate a failure. Microsoft Research has reported specification, reliability, debugging, latency, review burden, and collaboration as qualitative pain points in AI-assisted coding—not as measured prevalence rates: Microsoft Research’s study.
- Escalate when the risk is beyond your review capacity. For consequential software, seek an independent secure code review or application-security assessment if you cannot confidently assess the implementation. A reviewer can help examine risks your own tests or scanners miss.
Who is accountable for code made with AI?
The person or organization deploying software remains responsible for understanding and maintaining it, whether the code was written by a human, generated by an AI tool, or produced through a mix of both. AI assistance can speed up a prototype or implementation; it cannot turn an unexplained change into a responsible deployment.
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