Vibe Coding Security Check: How to Audit an AI-Built App Before Deployment

The main risk with vibe-coded software is false confidence: the app works, so the code looks ready to ship. Security problems often sit outside the right path, which means they stay invisible until someone actively tests for them. Recent research shows how often working AI-generated code still fails security checks.

A 2025 Carnegie Mellon-led benchmark found that 61% of AI-generated solutions passed functional tests, while only 10.5% were secure. In practical terms, code can work exactly as expected and still hide an exploitable security vulnerability. The researchers tested 200 real-world software engineering tasks and found that security-focused prompting did not reliably close the gap.

That gap matters because a vibe code workflow often measures success through visible behavior: the feature runs and the expected output appears on screen. A vibe coding security check must go further and test how the application behaves under invalid input, unauthorized access, manipulated requests, and other conditions that expose vulnerabilities the happy path never reaches.

AI-based code review has a clear limit when the same model writes the code and checks it afterward. A vibe coder may prompt the agent to avoid known risks, then scan the repository for flaws, yet the model can miss the same weakness during both creation and review. Because these tools generate code at speed, one blind spot can repeat the same insecure pattern across an entire codebase.

Security therefore needs an independent verification layer. Your AI engineering team should test the running application, reproduce attack conditions, trace findings back to the affected code, and verify each fix means the product is safe. This approach also applies when you use AI for application modernization, where generated changes still require technical validation before release.

Based on our engineers' experience reviewing client vibe-coded apps and building AI-generated prototypes internally, the same security gaps tend to recur. So we've identified an efficient vibe code security checklist that starts with the highest-impact risks, helping you fix the largest attack surface before lower-priority issues.

The table below gives you the short version of the checklist, while this section covers it in detail: what each issue looks like, why it matters, and what to fix. These 7 checks cover the areas our engineers most often find require additional security review before a vibe-coded app reaches real users and data.

Look first for credentials baked into client-side code or a committed config file. An exposed API key in a public bundle or a .env pushed to the repo gives an attacker a working credential with no exploit required. In a 2025 Invicti Security Labs experiment, researchers generated 20,000 apps with AI coding tools and then analyzed them for security flaws. The literal string supersecretkey appeared in 1,182 of those generated apps. A predictable JWT secret like this can let an attacker forge a valid admin token and gain privileged access.

The fix is to move every credential into environment variables or a dedicated secrets manager, and to rotate any key that was already committed, since git history keeps the old value. A proper vibe coding security check should also verify that no active secret remains exposed in the repository or client-side bundle. Treat anything that grants access to a paid service or a database as sensitive data that never belongs in the client.

• Self-check prompt: "List every hardcoded credential, token, or connection string in this codebase and tell me which are reachable from the client bundle."

Broken authorization means the app confirms who you are but never checks what you are allowed to touch. A logged-in user changes an ID in the request and reads another customer's record. This is an Insecure Direct Object Reference (IDOR). The same flaw can also let a regular user reach an admin route that was never locked down.

Typical broken authorization scenarios include:

• Changing /users/123 to /users/124 exposes another user's data.
• Replacing an account, order, or document ID returns a resource owned by someone else.
• A regular user can call an admin endpoint directly through the API.
• The backend checks authentication but skips ownership or role validation.

The fix is resource-level authorization on every endpoint, so the server re-checks ownership and role on each request. This class rarely shows up in a source-code read, so a thorough vibe code security check tests these permissions against the running app with multiple accounts.

• Self-check prompt: "Review every API endpoint that reads, updates, or deletes user-owned resources. Identify where changing a resource ID, account ID, or role could let one user access another user's data or reach an admin-only action."

Unsafe queries are ones built by pasting user input straight into a query string. String concatenation opens the door to Structured Query Language (SQL) injection, letting an attacker read or delete data by shaping the input. The fix is parameterized queries or prepared statements, which treat user input as data.

• Self-check prompt: "Find every query that concatenates user input and rewrite it as a parameterized query."

AI tools regularly invent package names that do not exist, and attackers pre-register those exact names on npm or PyPI with malware inside, a technique called slopsquatting. So the check is not only whether a dependency is outdated, but whether it should exist at all. This is a known weak spot of AI coding assistants, which suggest plausible-sounding imports with full confidence.

Confirm every unfamiliar package resolves to a real, maintained project before installing it, pin versions with a lockfile, and run a dependency and malware scan (npm audit, pip-audit, or a software composition analysis tool) as part of the build.

• Self-check prompt: "List every third-party package in this codebase. Flag any dependency that may be hallucinated, unmaintained, suspicious, or missing from the official npm or PyPI registry, and identify packages with known security issues."

According to Alex Penzov, CTO at Cleveroad, these issues are especially easy to miss because they often sit outside what a standard scanner can verify:

Weak session handling is another check a fast review usually skips because the login flow works and nothing looks broken. The failures sit underneath it: how tokens are stored, how passwords are hashed, and whether logout actually ends the session.

Watch for these patterns specifically:

• Access tokens in localStorage, where an XSS payload can read them
• Passwords stored in plaintext or hashed with a fast algorithm like MD5 or SHA-1
• No server-side invalidation on logout, so a stolen token stays valid
• Long-lived JSON Web Tokens (JWTs) with expiry measured in weeks instead of minutes

Fix each one directly: move tokens into httpOnly cookies, hash with bcrypt or argon2, invalidate sessions on logout, and cut JWT lifetimes to a short window backed by refresh tokens.

• Self-check prompt: "Review how this app stores access tokens, hashes passwords, expires JWTs, refreshes tokens, and handles logout. Flag any case where credentials or sessions could remain exposed, reusable, or valid longer than necessary."

Every field you can type into is a place the app can be attacked, and missing input validation can turn functional code into an entry point for XSS, path traversal, or other injection vulnerabilities. The check is whether the app validates and sanitizes input on the server, because browser-side rules alone are trivial to bypass. A vibe-coded app security checklist should verify both input validation and output encoding, so untrusted values can neither execute in the page nor climb the file system.

• Self-check prompt: "Review every user-controlled input in this app. Flag any field, parameter, file path, or request value that reaches the server without validation, sanitization, or safe output encoding, and identify where it could lead to XSS, path traversal, or injection."

The last check is what the app reveals when it ships. Leaving debug mode on in production, plus verbose error pages that expose stack traces, file paths, and even database schema, lets anyone who triggers an error see sensitive details. Disable debug, return generic error messages to the user while logging the details server-side, and set the core security headers (CSP, HSTS, X-Frame-Options). These fixes follow standard deployment security best practices and take minutes to apply, yet they close a wide band of application security gaps that AI-generated defaults leave open.

• Self-check prompt: "Review the production configuration and error handling for debug output, stack traces, file paths, database details, or other sensitive information exposed to users. Also identify missing CSP, HSTS, and X-Frame-Options security headers."

The 7 checks above help you find existing security flaws. At Cleveroad, we apply the practices below to keep the same issues from returning as new features, dependencies, and AI-generated changes enter the codebase. We recommend treating them as recurring release controls rather than one-time audit steps.

Decide what "secure" means for the feature before you generate a line of code. Set the auth rules, data-handling boundaries, input limits, and rate caps up front, so the model has a target to hit and the security measures are defined. In Cleveroad security reviews, we pay particular attention to features where authorization and data-access rules were never defined before implementation. These are common places to find endpoints with incomplete ownership or role checks.

For each feature, we recommend writing a short security contract before the prompt: who can call the endpoint, what data it may read or change, which inputs are accepted, and what happens when a limit is exceeded. We use these requirements as acceptance criteria so QA can verify security independently of the generated code.

Treat every AI-generated change like code from an unknown contributor. A feature can work as expected and still introduce unsafe dependencies, weak authorization logic, exposed data, or insecure error handling.

Review the full change and check:

• Dependencies: verify every new package is real, maintained, and necessary.
• Permissions: confirm the code checks user roles and resource ownership on the server.
• Input handling: inspect validation, sanitization, and error paths for hostile or malformed input.
• Data access: verify which records, files, and services the new logic can read or modify.
• Secrets and logs: make sure credentials, tokens, and sensitive user data never appear in source code or logs.

At Cleveroad, we apply the same review principle to AI-generated code that we use during code audits: reviewers assess the change beyond its expected functionality and trace its permissions, dependencies, data access, and failure paths. It helps separate code that simply works from code that is ready for production.

A quick review that confirms the feature runs is not enough. For a fuller process, see how to run a code audit and structure the review around security, architecture, dependencies, and remediation.

Add automated scanning to your Continuous Integration and Continuous Delivery (CI/CD) pipeline: secret detection for committed credentials, dependency scanning for vulnerable packages, Static Application Security Testing (SAST) for code, and image checks before deployment. Start each scanner in report-only mode, then switch it to blocking once you control false positives.

This way, every security check runs automatically on each commit instead of depending on manual review. Cleveroad DevOps specialists can integrate these checks into the CI/CD pipeline and configure blocking rules, helping AI development teams introduce automated security controls without disrupting the release process.

Static review reads the code, so it misses context-dependent flaws like broken access control and business-logic errors. A dynamic security audit against the live app surfaces those, because it exercises the real request flow with real accounts.

This is why Cleveroad security reviews do not stop at source-code analysis when the risk depends on application behavior. We test the running product with different roles, request variations, and failure scenarios to verify that server-side controls hold under real requests.

Test the app under conditions that source review cannot reproduce:

• Change a resource ID and check whether one user can access another user's data.
• Call admin-only endpoints from a regular account.
• Send malformed or oversized input to forms and APIs.
• Reuse expired or invalid tokens.
• Repeat sensitive requests to verify rate limits and abuse controls.
• Manipulate request parameters to test whether the server trusts values sent by the client.

Consider a finding closed only after the same test that exposed the flaw no longer succeeds after the fix. This verification step prevents teams from marking an issue as resolved based on a code change alone.

These practices make security part of the release process instead of a one-time review after the app is already exposed. These are the same principles we apply when assessing code quality and application security at Cleveroad. If your team has built a product with AI coding tools, contact us for an independent review of the code and running application, with security findings prioritized by risk and remediation effort.

Cleveroad is a custom software development company with 15+ years of experience in AI development, based in the CEE region, Estonia. We build web and mobile products and modernize existing systems through AI-assisted development, with AI coding tools integrated into the engineering workflow. Our team also reviews AI-generated code, remediating security and quality issues, and preparing applications for production release. This is the core of our AI development services: a code review of every generated change sits alongside automated scanning.

To demonstrate our experience with AI-assisted development and security review, we'd like to highlight our recent case with AI-assisted ERP development for NetSuite SaaS Platform.

Our customer is Proprio Cloud Solutions, a Michigan-based SaaS company building NetSuite-integrated platforms for the contract furniture industry. The client needed a development partner to speed up delivery of the Orion platform, standardize development across multiple client environments, and launch a Field Service mobile MVP with work order management, crew tracking, punch lists, offline access, and real-time NetSuite synchronization. They also wanted to remove manual regression testing as a release bottleneck by building reusable components and establishing automated end-to-end test coverage across all three environments.

Cleveroad embedded an AI-assisted team into Proprio's development workflow, using Claude Code to trace Orion's SuiteScript logic, generate tested pull requests, turn acceptance criteria into Playwright scripts, and speed up sprint documentation. Human review remained mandatory throughout: code followed standard PR review, QA validated generated tests against test data, BA and PM approved acceptance criteria, and security-sensitive code required two-engineer review.

As a result, our experts shipped four major Orion platform releases on schedule and delivered the Field Service mobile app MVP. With AI-assisted development, their sprint output increased by 30 to 40% without adding more specialists, while Claude Code cut sprint summary preparation from 2 to 3 hours to under 10 minutes by compiling engineering and QA data automatically.

This is what Luke Abbott, CTO & Co-Founder at Proprio Cloud Solutions, says about cooperation with the Cleveroad team:

Choosing Cleveroad gives you an independent layer between AI-generated code and production. Here is how you benefit by working with us:

• Our team of 280+ in-house specialists, 25% of whom are senior-level developers, will examine your codebase for security and architecture issues that automated self-review misses.
• Cleveroad AWS-certified engineers review cloud architecture, IAM, secrets management, and deployment configurations to catch infrastructure-level risks introduced by AI-generated changes.
• Our QA and engineering experts test the running product with multiple user roles, modified requests, invalid tokens, and edge-case inputs to uncover broken access control and business-logic flaws before release.
• Cleveroad holds ISO/IEC 27001 for information security and ISO 9001 for quality management, so our review processes and access policies align with international standards.