The uncomfortable part of vibe coded SaaS security is that the same AI assistant that left the hole can find it, if you ask a specific question. Generic prompts return generic answers; naming the five failure patterns returns file and line references you can act on.
Vibe Coded SaaS Security: The Five Flaws That Matter
Vibe coded saas security problems concentrate in five repeatable patterns: disabled row-level security, authorization decided in the browser, object routes that accept any ID, secrets compiled into the front end, and user input executed as code. None of the five needs a custom exploit; a browser inspector and a modified request are enough.
The term vibe coding describes building software by describing it to an AI assistant and accepting the generated result, a practice Business Insider popularised in February 2025 according to a Qodo analysis of the trend. The assistant optimises for a working screen, not for the threat model behind it, so the security defaults it picks are the ones that make the demo succeed.
That is the mechanism behind all five flaws. A login screen that renders, a list that loads and a form that saves look identical whether or not the server checked who was asking. The defects sit in what the browser is allowed to request and what the database returns, and neither shows up in a click-through test.
The five flaws below are ordered from broadest exposure to most deceptive. Each one has a check you can run without a scanner, and each one has a fix that lives on the server or in the database, not in the UI.
Missing or Disabled RLS in Supabase and Firebase
Row-level security is the database rule that restricts each row to its owner, and it is the first thing to verify in a vibe coded app. Supabase, the Postgres backend with auth, storage and edge functions, exposes its API directly to the browser, so the access policy attached to each row becomes the only barrier.
Firebase, Google's app development platform, follows the same architecture through its client SDKs and security rules. When an AI assistant wires a front end to either platform, it typically does the minimum needed to read and write records. Whether the policy is enabled and correct is a separate question the assistant does not raise unless asked.
The symptom appears in a support thread in April 2026 on TabNews, a Brazilian developer community, where builders describe applications that leak data because the shipped build was treated as a finished product rather than a prototype. The pattern is architectural, not a language or framework bug.
Front-End Admin Flags and the Local Storage Trick
Any permission decision made in the browser can be reversed in the browser, which is why front-end admin flags are a recurring flaw. A route that renders an admin panel when a stored value reads true will render that panel for anyone who edits the value, and the server doing the work will not notice the difference.
The concrete case: a stored flag was changed from a student-style value to an administrator value in browser storage, the page reloaded, and lessons that had not been released became visible. No server was compromised and no exploit was written. The application had simply moved a business rule into a place the visitor controls.
The rule that follows is blunt. A front end renders; it does not decide. Next.js, the React framework used for many generated applications, supports server components and server actions that can own that decision, but only if the authorization check lives there rather than in the component that draws the button.
What the video does not do is prove how common the flaw is across the ecosystem, and neither does this article. The right reading is narrower: whenever an interface hides a page behind a client-side condition, verify that the underlying data request is checked again on the server.
Server Definitions: What Each Check Enforces
A server-side check answers a different question than a client-side one, and separating them is the practical route to fixing all five flaws. The table below maps each check to the layer that must enforce it and the failure it prevents.
Use it as a review list rather than a ranking. A missing database policy and a missing ownership check can leak the same table through different requests, and fixing one leaves the other open.
| Check | Enforced where | Flaw it prevents |
|---|---|---|
| Row-level policy | Database policy | Missing or disabled RLS |
| Role verification | Server session | Front-end admin flag |
| Ownership scoping | Server route | IDOR |
| Secret storage | Server environment | Leaked key in the build |
| Output encoding | Render layer | Stored XSS |
IDOR: When Changing the ID Returns Someone Else's Data
Insecure direct object reference, usually shortened to IDOR, is a route that returns a record based on an ID without confirming that the caller owns it. The fifth edition draft of the OWASP Top 10, the widely cited list of application security risk categories, files broken object level authorization under API security risks, and the mechanism is old enough that it carries its own borrowed acronym.
The attack is mechanical. A request for the profile with ID 100 returns your data; the same request with ID 101 returns the next account's data, including fields such as phone number and account state. A loop over sequential IDs collects them all. Sequential primary keys make the enumeration trivial, and a missing rate limit makes it quiet.
AI assistants contribute here because a sequential integer is the easiest primary key to generate. A route that fetches a record by ID is valid work, and nothing in the request forces the assistant to ask whether that ID belongs to the caller. The fix is to pass ownership into the query and return nothing when the pair does not match.
Random identifiers raise the cost of enumeration but do not fix the flaw. An attacker who learns another user's identifier can still request it, so authorization has to be checked on the server regardless of the identifier format.
Leaked Keys: Git History Never Forgets
Leaked keys are the most expensive flaw to fix after the fact because they hand over a working credential. A payment gateway secret, an email provider key or a database connection string written into source that reaches a public repository is readable by anyone who finds it, and one scan of the GitHub secret scanning documentation shows how much automated detection now runs against public code.
Building the front end does not hide anything. Variables that are inlined at build time become plain JavaScript in the bundle, and the browser inspector displays them without a plugin. An AI assistant that hard-codes a key into a client component has not hidden it; it has published it.
Two practices close the hole. Keep secrets in server-side environment variables that never reach the client bundle, and treat any key that has ever been committed as compromised, rotating it rather than deleting the line. Deleting a line leaves the value in the commit history.
Untrusted Input and Stored XSS
Stored cross-site scripting, usually Stored XSS, occurs when text a user supplied is rendered as executable code for everyone who later views the page. A custom HTML field, a profile bio, or a filename is enough to carry it. The malicious payload then runs in the browser of every visitor, including the administrator, and can copy a session or redirect a payment.
AI assistants widen this surface because they accept requests to render rich content and may not distinguish between displaying text and executing it. The OWASP XSS Prevention Cheat Sheet sets out the output-encoding rules, and the short version is to encode by context, not to trust a client-side filter.
Treat input as hostile by default. Validate on the server, limit length and shape, encode on output, and never store an uploaded file where the server will serve it back as active content.
Find the Flaws With Open-Source Scanners
Open-source scanners can locate most of these flaws in a single session, and they cover different layers of the stack. A web scanner exercises the running application, a secret scanner reads repository history, and a static analyzer reads source for risky patterns such as shell execution or unescaped output.
The sequence below starts with the running application because it catches what source review can miss, then moves inward.
- Run OWASP ZAP, the open-source web application scanner maintained by the OWASP Foundation, against a staging deployment and review the alert list rather than the raw count.
- Run Gitleaks over the full repository history to list every committed secret, then rotate each one that was real.
- If the backend is Python, run Bandit, a static analyzer for common Python security issues, and read the flagged calls rather than suppressing the output.
- Query the codebase with a specific prompt naming all five patterns and ask for file and line references before any change is made.
- Re-run the scans on the deployment, not on localhost, because cookies, headers and redirects differ between the two.
What AI Reviews Can and Cannot Do
An AI assistant can find many of these flaws when the request names them, and it will usually miss them when the request is open-ended. Asking it to check for security issues returns a broad pass; asking it to list object routes that fetch by ID without an owner check returns something reviewable.
The limits matter. It does not know your business rules, it may invent a fix that breaks the query contract, and it will not run the deployed application. Use Claude Code, Anthropic agentic coding tool that runs in the terminal, or Cursor, the AI-first code editor, to produce a list, then verify each finding against the running system.
The durable fix is not a one-time audit. Add the five checks to the workflow that introduces code, so the next feature request includes the authorization question instead of deferring it to the week after launch.
FAQ: Vibe Coding Security Questions
- Is a vibe coded app always insecure? No. The flaws described here are common because assistants choose defaults for a working demo, not because the approach makes security impossible. An application is insecure when the authorization and data rules are missing or unverified, and those can be added after generation.
- Why is row-level security the first thing to check? Supabase and Firebase both let the browser reach the database through their APIs, so the per-row policy is the barrier that decides who reads what. If it is absent, every other control sits behind an open door.
- What is IDOR in plain terms? It is a request for a record by identifier that does not check whether the caller owns that record. Changing a number in the request returns another account's data, and sequential identifiers make that easy to script.
- Can deleting a leaked key from a commit fix the problem? No. The value remains in commit history, so it has to be rotated at the provider. Treat any key that was ever committed as public from the moment it was pushed.
- How often should you re-run the scans? At minimum before every deploy that changes authentication, data access or payment code, and on a schedule afterwards. Scanners are cheap to run and expensive to skip once a customer notices a leak.
From Scanning to Shipping
The five flaws share one cause: the browser was trusted with a decision it should never have been allowed to make. Every fix moves that decision to a server or to a database policy, where the visitor cannot edit it.
Teams building in public still have to learn the mechanics of authorization and deployment, and Gustavo Dev Doido covers much of that ground in Portuguese-language material, including the Crazystack Typescript program and the Bootcamp do Dev Doido. Crazystack is one place to look if the gap is fundamentals rather than a missing scanner.
The practical order is unglamorous: turn on row-level rules, move role checks to the server, scope every lookup to the owner, rotate anything that reached the client, and encode what users type. Then run the scanners against the deployment and read the results.
If you recorded a walkthrough of how you found and fixed these flaws in your own product, that knowledge can reach readers who will never watch the video. A transcript can become a structured article that names the flaw, the source, and the fix in the order a reviewer needs them, which is where Skala Blog fits: paste a YouTube URL, transcribe the video, and generate a draft you edit before publishing.