AI-Generated Websites: 7 Security Checks Before Going Live

AI-Generated Websites: The New Frontier and Its Hidden Risks

Artificial intelligence (AI) has democratized web development. Tools promising impressive agility in creating websites and applications can transform ideas into digital products rapidly. However, this convenience comes with significant risks. If you lead a startup or SME and used AI to generate your website, or if you're a developer inheriting this code, this guide is essential. It covers critical security points that need verification before your application goes live, with data revealing the extent of the danger.

Is an AI-generated website safe for business is a question that needs factual answers, not just optimism. While development speed is tempting, neglecting security can be costly. Flaws can range from minor bugs to critical vulnerabilities that expose sensitive customer data and damage your company's reputation. Ignoring these checks is like building a beautiful house but forgetting to install locks on the doors and windows.

Key Points to Check:

• Security Vulnerabilities: AI-generated code often contains known flaws.
• Data Exposure: Configuration errors can expose confidential customer information.
• Code Integrity: Code quality and security must be validated by experts.
• Dependency Management: Outdated libraries are a common attack vector.
• Authentication and Authorization: Access mechanisms may be flawed or easily bypassed.

The Landscape of Vulnerabilities in AI-Generated Code

The statistics are alarming and indicate a worrying trend. A Veracode survey, cited by Kaspersky, reveals that approximately 45% of AI-generated code contains flaws falling under the OWASP Top 10 (a ranking of the most critical security vulnerabilities in web applications), even if much of it compiles without visible errors. Two years ago, this figure was only 20%. Wiz Research estimates that 20% of applications created through "vibe coding" (rapid, intuitive development, often AI-assisted) have critical vulnerabilities or serious configuration errors.

The Lovable Case: A Real Alert of Exposed Data

One of the most documented cases of flaws in applications generated with AI tools is Lovable. In the vulnerability identified as CVE-2025-48757, with a CVSS severity score of 8.26, a scan of 1,645 applications on the platform's own storefront revealed that 170 of them (about 10.3%) had critical Row Level Security (RLS) flaws in Supabase. This flaw exposed 303 endpoints, making data such as users' names, emails, phone numbers, home addresses, and even financial records accessible.

The root cause is structural: tables created directly via raw SQL, migrations, or AI tools do not always configure RLS automatically. In Lovable's case, a public anon_key embedded in the client allowed direct database queries without the need for login. This demonstrates how seemingly technical flaws can have direct and severe consequences for user privacy. To understand more about application security impact, check out our article on why your website isn't generating leads.

The Complexity of Reviewing and Iterating AI-Generated Code

The problem worsens when considering the iterative development process. In tests with GPT-4o, after just 5 review iterations, the code showed 37% more critical vulnerabilities compared to the initial version. More concerningly, over 40 iterations focused on adding new features, 158 new vulnerabilities emerged, 29 of which were considered critical. This suggests that the more you tamper with AI-generated code without adequate security expertise, the greater the risk of introducing new flaws.

Recurring Common Weakness Enumerations (CWEs) include CWE-94 (code injection), CWE-78 (OS command injection), CWE-190 (integer overflow), CWE-306 (missing authentication), and CWE-434 (unrestricted file upload). These types of flaws manifest in common ways:

• Lack of Input Validation: Leading to Cross-Site Scripting (XSS) and SQL injection attacks.
• API Keys in Code: Exposure of sensitive credentials directly in client-side code.
• Browser-Side Authentication: Implementation of client-side authentication, easily bypassed.
• Outdated Dependencies: Use of libraries with known or unknown vulnerabilities.

A concrete example of this insecurity is the Nx case, where a publication token was stolen by exploiting CWE-94 introduced by an AI-generated code snippet. This reinforces the need for heightened attention. For developers working with deployment platforms like Vercel, understanding security at each step is crucial. Learn more about Vercel Services: The New Era of Unified Full-Stack Deployment.

The 7 Essential Checks Before Publishing Your AI-Generated Website

Given these risks, it's crucial to implement a rigorous checklist before putting your application online. Below, we detail 7 checks that every startup owner, SME, or developer should perform:

1. Input Validation

• What to check: Ensure all user data inputs (forms, URL parameters, HTTP headers) are rigorously validated and sanitized to prevent attacks like XSS and SQL injection. AI tools may generate code without these basic validations.
• Example: If a name field accepts special characters that can be used in scripts, it should be rejected or sanitized before processing.

2. Credential and API Key Management

• What to check: Ensure API keys, passwords, and other secrets are not hardcoded (written directly) in the source code, especially in client-side (frontend) code. Use environment variables and secret managers.
• Example: Never put your email marketing service API key directly into your website's JavaScript code. Use a secure backend to mediate these calls.

3. Robust Authentication and Authorization

• What to check: Implement secure server-side authentication mechanisms. Verify that access permissions (authorization) are correctly applied to each resource, preventing unauthorized users from accessing data or functionalities.
• Example: A regular user should not have access to administrative functions, even if they try to access them directly via the URL.

4. Database Security (RLS and Queries)

• What to check: If you use databases like Supabase, ensure Row Level Security (RLS) is correctly configured to restrict user access to specific data rows based on policies. Also, review SQL queries to prevent injections.
• Example: As in the Lovable case, tables without RLS can allow anyone with the anonymous key to query all data. Check if your database access configuration is granular.

5. Dependency and Library Management

• What to check: Keep all third-party libraries and dependencies updated. Use dependency analysis tools to identify packages with known vulnerabilities. Be wary of non-existent or discontinued libraries that AI might have suggested.
• Example: An old version of React or a UI library might contain security flaws that have already been fixed in newer versions. Use npm audit or yarn audit.

6. File Upload Validation

• What to check: If your application allows file uploads, implement strict validations on file type, size, and content to prevent the execution of malicious code (CWE-434).
• Example: Only allow uploads of images in secure formats and verify file content, not just the extension.

7. Penetration Testing and Security Scans

• What to check: Conduct penetration tests (pentests) and use automated vulnerability scanning tools (like those that identified flaws in Lovable) to simulate attacks and find breaches before attackers do.
• Example: Use tools like OWASP ZAP or Burp Suite to identify common flaws in your web application.

Frequently Asked Questions about Security in AI-Generated Websites

Is an AI-generated website safe for business?

Generally, an AI-generated website is not inherently secure without proper reviews and testing. AI-generated code can contain known security flaws, such as those in the OWASP Top 10. It is crucial to perform rigorous checks before putting any application live.

What are OWASP Top 10 vulnerabilities?

The OWASP Top 10 is a standard representing the most critical security risks to web applications. It includes vulnerabilities like SQL injection, broken authentication, sensitive data exposure, and security misconfiguration flaws. AI-generated code often contains some of these flaws.

How can I ensure the security of my AI-developed website?

Ensuring security involves a combination of code validation by experienced developers, penetration testing, automated vulnerability scanning, and implementing robust security practices across all application layers, from the database to the user interface.

What is Row Level Security (RLS) and why is it important?

Row Level Security (RLS) is a security feature that restricts user access to specific rows in a database table based on defined policies. It is crucial for ensuring that each user sees only the data they are permitted to, preventing exposure of sensitive data, as seen in the Lovable case.

What are the risks of using outdated dependencies?

Outdated dependencies often contain known security vulnerabilities that attackers can exploit. Keeping libraries and frameworks updated is one of the most effective ways to protect your application against common attacks.

Conclusion: Security First, Innovation Second

The agility provided by AI tools in web development is undeniable and represents the future. However, the rush to launch products cannot compromise the security and privacy of users and the company. An AI-generated website or application requires, at a minimum, the same rigorous security checks as a traditionally developed project, and in many cases, demands even greater attention.

Recommendation: Before publishing your AI-generated website or application, dedicate time and resources to perform the 7 essential checks listed in this guide. If your team lacks security expertise, consider hiring a specialized professional or company to conduct a thorough audit.