Updated 2026-07-13 · 13 min read
Vibe coding is safe enough for production only when generated code receives the same threat modeling, review and operational controls as human-written code. The risk is not that AI writes uniquely malicious software. It is that generation makes it easy to ship a large, plausible system before anyone checks the invisible trust boundaries.
A page loading successfully proves almost nothing about authorization, secret exposure, tenancy or webhook integrity. Security behavior appears when a user changes an ID, calls an endpoint directly, submits malformed input, replays an event or acts under a different role.
Generated apps often check whether a user is logged in, then trust identifiers supplied by the browser. Every sensitive read and write must verify on the server that the actor may access that specific resource. Test two accounts: can account A request account B’s record by changing a URL, API body or database filter?
When using a managed backend, row-level security is part of the application — not optional configuration. Deny by default. Define ownership and organization membership explicitly. Test queries outside the UI because a hidden button is not access control.
Anything shipped to the browser is public. Keep service-role keys, payment secrets and private API credentials on the server. Scan the built assets and repository history, not only the current environment file. Rotate a secret immediately if it enters chat, a screenshot, terminal transcript or commit.
Verify webhook signatures using the raw request body. Make handlers idempotent: the provider will retry. Confirm event type, mode, product/price and metadata before granting entitlement. Never trust a “payment successful” redirect alone; users can visit URLs manually.
Validate types, length and allowed values on the server. For uploads, restrict size and content type, randomize storage names, scan where appropriate and serve untrusted files from a separate origin. If the app calls an LLM with user-controlled content, treat prompt injection as untrusted input crossing a tool boundary; constrain tools and permissions rather than relying on a warning inside the prompt.
Write down assets (accounts, documents, payment status, secrets), actors (anonymous visitor, user, admin, integration), entry points and the worst credible action at each boundary. Then ask the coding agent to map every control to a file and test. “Make the app secure” invites generic headers; “prove that a normal user cannot read another organization’s invoice through any route” produces a testable requirement.
Prioritize by impact and reachability. A missing decorative security header is rarely as urgent as a public storage bucket or broken tenant filter. Generated audits should rank findings and include reproduction evidence, not dump an unreviewed checklist.
Agents install packages readily. Remove unused dependencies, lock versions, run vulnerability audits and inspect packages with tiny communities or suspicious names. A generated import is not an endorsement of the package.
A second agent can identify obvious gaps, generate abuse cases and write tests, but it can also agree confidently with an unsafe premise. For products handling payments, health, identity or sensitive business data, involve an experienced security reviewer before launch.
It increases the risk of shipping unreviewed code quickly. The code can be secured, but speed must be paired with explicit controls, testing and review.
Use it to accelerate implementation, not to waive payment security. Verify server-side entitlements, signed idempotent webhooks, secrets and authorization with experienced review.
Assuming that a working UI means the backend is protected. Broken object-level authorization and exposed secrets are common high-impact failures.