Over the last two years, Vibe Coding has completely changed how software is built.
Today, almost anyone can create a SaaS platform, CRM, internal dashboard, marketplace, or business application using nothing more than natural language and AI-powered development tools.
Platforms such as Claude Code, Lovable, Google Antigravity, Cursor, Bolt, Replit, Google AI Studio, and many others have dramatically reduced the time required to turn an idea into a working application.
What used to take weeks or even months, can now be accomplished in just a few hours. But there is one question that surprisingly few people ask before deploying their projects:
Is AI-generated code actually secure?
The short answer is: Not always.
Modern AI models are incredibly effective at accelerating software development, but they do not replace proper security reviews. AI can generate code, authentication systems, APIs, and databases, but the responsibility for protecting an application still belongs to the developer.
In this article, we'll explore the biggest security risks associated with AI-generated applications, highlight real issues reported by the developer community, and share the security practices we personally follow before deploying any production project.
The Biggest Mistake in Vibe Coding
One of the biggest misconceptions we see today is believing that code generated by Artificial Intelligence is automatically secure.
There's another issue that's even more common.
Many people simply don't think about security. Not because they're careless.
Most are focused on getting the application to work. Once they finally see the project running, they assume the job is finished.
In reality, that's exactly when one of the most important phases of software development begins:
Securing the application.
Today's AI tools can generate:
- Authentication systems
- APIs
- Databases
- Dashboards
- Third-party integrations
- Complete applications
However, that doesn't mean everything has been configured correctly.
A beautiful interface can still hide dozens of security vulnerabilities that won't become apparent until the application is already in production.
The Biggest Security Risks in Vibe Coding
SQL Injection
Even in 2026, SQL Injection remains one of the most common web security vulnerabilities.
It occurs when user input reaches the database without proper validation or parameterization.
Although modern frameworks reduce this risk considerably, poorly implemented AI-generated code can still expose applications to SQL Injection attacks.
Cross-Site Scripting (XSS)
Cross-Site Scripting allows malicious JavaScript to execute inside a user's browser.
Attackers may steal session cookies, redirect users, or inject harmful scripts.
Applications that display user-generated content without proper sanitization remain vulnerable to XSS attacks.
Cross-Site Request Forgery (CSRF)
CSRF attacks trick authenticated users into performing actions they never intended.
Depending on the application, this could result in unauthorized account changes, financial transactions, or administrative actions.
Exposed API Keys
This is one of the most common mistakes beginners make.
Developers often place sensitive credentials directly inside frontend applications, including:
- OpenAI API Keys
- Anthropic API Keys
- Gemini API Keys
- Stripe Secret Keys
- Supabase Service Role Keys
If these secrets are exposed, anyone can retrieve them using the browser's developer tools.
Depending on the key, attackers may gain access to paid APIs, databases, or critical backend infrastructure.
Secrets Published on GitHub
Another frequent issue is accidentally committing sensitive files into Git repositories.
Examples include:
.env.env.localconfig.js- credential files
Even private repositories can accidentally expose sensitive information through incorrect sharing settings or human error.
Always review every file before committing code.
Poorly Configured Supabase Projects
Many people blame Supabase when security issues arise.
In reality, the platform is rarely the problem.
The issue usually comes from incorrect configuration.
Some of the most common mistakes include:
- Disabled Row Level Security (RLS)
- Overly permissive security policies
- Using the Service Role Key in frontend code
- Public storage buckets that shouldn't be public
- Incorrect database permissions
Supabase provides excellent security features—but only when configured correctly.
Open Firebase Security Rules
Firebase projects often face a similar problem.
During development, developers commonly use rules like:
allow read, write: if true;
The issue occurs when those temporary rules remain active after deployment.
At that point, anyone can read or modify your application's data.
Poorly Implemented Row Level Security (RLS)
Enabling RLS isn't enough.
Your security policies also need to be written correctly.
Otherwise, users may gain access to records belonging to other customers or organizations.
If you're building SaaS applications with Supabase, understanding RLS is no longer optional.
It's a fundamental requirement.
Incorrect JWT Implementation
Tokens without expiration dates, weak validation, or insecure storage significantly increase security risks.
Always verify your authentication flow before deploying your application.
Insecure File Uploads
Accepting any uploaded file without validation may allow malicious files to reach your servers.
Always validate:
- File type
- Extension
- File size
- MIME type
before storing uploaded content.
Vulnerable Dependencies
AI coding assistants may generate projects using outdated libraries or packages with known security vulnerabilities.
Keeping dependencies updated should be part of every deployment workflow.
Prompt Injection
Applications powered by AI can also become vulnerable to Prompt Injection attacks.
Attackers intentionally craft prompts that manipulate the behavior of Large Language Models (LLMs).
As AI agents become more common, Prompt Injection is becoming one of the fastest-growing security concerns.
AI Hallucinations
AI-generated code isn't always correct.
Models sometimes invent APIs, reference nonexistent functions, or generate insecure implementations.
Never assume generated code is production-ready without manual review.
Duplicate Code
Large AI-generated projects often contain duplicated logic.
Besides making maintenance harder, duplicated code increases the likelihood of bugs and security inconsistencies.
Lack of Testing
Generating software quickly doesn't eliminate the need for testing.
Without proper testing, simple security vulnerabilities can easily reach production environments.
Real-World Security Issues Reported by the Community
The developer community has repeatedly reported security problems in AI-generated applications, including:
- APIs accessible without proper authentication
- Secret keys accidentally committed to GitHub
- Environment variables exposed in frontend code
- Databases left publicly accessible because of weak security policies
- Public cloud storage buckets containing private user documents
These problems don't happen because AI "decides" to write insecure code.
They usually happen because generated code is deployed without proper review.
Security Tools That Can Help
No automated tool replaces a complete security audit, but several solutions can help identify vulnerabilities before deployment.
Some of the most useful include:
- OWASP ZAP — Free web application security testing.
- Semgrep — Static code analysis.
- Snyk — Dependency vulnerability scanning.
- GitHub Advanced Security — Detects exposed secrets and repository vulnerabilities.
- SonarQube — Code quality and security analysis.
- Dependabot — Automatically updates vulnerable dependencies.
- TestSprite — Automated testing that helps identify unexpected application behavior.
Some of these tools may seem intimidating for beginners, but they can detect critical issues long before users encounter them.
If you're planning to build applications that handle real users or sensitive information, investing time in learning security tools is well worth it.
For beginners, we recommend starting with TestSprite, since it offers a simpler learning curve while still providing valuable insights into application behavior.
How We Handle Security
After building multiple real-world applications using AI coding platforms, we've developed our own security checklist before deploying any project.
We Always Review Authentication
We don't just verify that login works.
We intentionally try to perform actions that should not be allowed by simulating different user roles and attack scenarios.
We also implement rate limiting to reduce brute-force attacks and credential stuffing.
We Never Trust the First Implementation
A polished interface doesn't mean the backend is secure.
Even when everything appears to work perfectly, we carefully review the underlying business logic.
We Thoroughly Test Row Level Security
For Supabase projects, we consider RLS one of the most critical security layers.
Before deployment, we verify that every user role can access only the data it's supposed to access.
We Review Every Security Policy
Before implementing new features, we clearly define who should be allowed to view, edit, or delete each resource.
Understanding the application's entire workflow makes writing secure policies much easier.
We Validate Every API
We actively try to break our own applications.
Our goal is to discover whether sensitive information can be exposed under unexpected conditions.
We also strongly recommend never pasting secret API keys directly into AI chat interfaces.
Whenever possible, store secrets in environment variables instead of prompts.
Pre-Deployment Security Checklist
Before publishing any AI-generated application, we recommend confirming the following:
- JWT configured correctly
- Row Level Security reviewed
- HTTPS enabled
- Secrets protected
- Environment variables configured
- APIs secured
- Logs monitored
- Content Security Policy (CSP) enabled
- Rate limiting implemented
- CORS configured
- Input validation completed
- Data sanitization implemented
- Dependencies updated
- Manual review of AI-generated code completed
- Automated security tests executed
Our Opinion
Artificial Intelligence can generate thousands of lines of code in minutes.
It cannot, however, assume responsibility for securing your application.
That responsibility still belongs to the developer.
After working with several Vibe Coding platforms in real-world projects, we've found that AI dramatically accelerates development—but it doesn't replace code reviews, penetration testing, security audits, or software engineering best practices.
It's also important to remember that no system is ever 100% secure.
Even organizations with dedicated cybersecurity teams and multi-million-dollar security budgets continue discovering new vulnerabilities every year.
Security isn't a one-time task.
It's an ongoing process of monitoring, testing, improving, and adapting.
The moment you publish an application, you become responsible for protecting the data of everyone who trusts your product.
That's why security should be considered from the very first prompt—not just during the final deployment phase.
Frequently Asked Questions
Is Vibe Coding secure?
It can be, provided the generated code is properly reviewed and follows modern security best practices. AI accelerates development, but it doesn't guarantee a secure application.
Does AI automatically generate secure code?
Not always. AI can build authentication systems, APIs, databases, and business logic, but incorrect implementations and insecure configurations can still introduce vulnerabilities.
What's the biggest security mistake in Vibe Coding?
One of the most common mistakes is exposing API keys, environment variables, or misconfiguring access controls such as Supabase Row Level Security (RLS) policies.
Should I use AI coding tools like Lovable, Cursor, or Claude Code for production projects?
Yes—but only after thoroughly reviewing, testing, and validating the generated code. These platforms significantly improve productivity, but they don't replace expertise in architecture, authentication, databases, or cybersecurity.
How can I make AI-generated applications more secure?
Review the generated code manually, secure authentication and authorization, use environment variables to protect secrets, keep dependencies updated, implement automated testing, and perform a complete security review before deployment.
This approach dramatically reduces the risk of exposing sensitive information or deploying vulnerable applications.
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