AI Code Security Review — Find Vulnerabilities in AI-Generated Code
AI coding assistants generate code fast, but 45% of AI-generated code contains security vulnerabilities (Veracode, 2025). This scanner catches the most common security antipatterns before they reach production.
AI Code Security Scanner
Paste JavaScript or TypeScript code and scan for common security vulnerabilities. Detects hardcoded secrets, injection flaws, XSS, SSRF, prototype pollution, and more. 100% client-side — your code never leaves your browser.
Why AI-generated code needs security review
AI coding assistants like ChatGPT, Claude, GitHub Copilot, and Cursor generate functional code quickly, but they frequently introduce security vulnerabilities. Common issues include hardcoded API keys and secrets in example code, SQL queries built with string concatenation instead of parameterized queries, innerHTML and eval() usage without sanitization, missing input validation on user-controlled data, and insecure cookie or session configurations. These tools optimize for 'code that works' rather than 'code that is secure.' A dedicated security scan catches these patterns before deployment.
The vibe coding security problem
Vibe coding — rapidly building applications with AI assistance — has transformed developer productivity. But speed introduces risk. When you accept AI suggestions without review, you inherit every security shortcut the model made. Production codebases with AI-generated authentication, database queries, and API handlers are particularly vulnerable. This scanner helps you review AI-generated code in seconds: paste the output, see every security issue highlighted with severity, CWE reference, and a specific fix.
Common vulnerabilities in AI-generated JavaScript
The most frequent security issues in AI-generated JavaScript and TypeScript code: (1) Hardcoded secrets — AI often generates example API keys and leaves them in the code, (2) SQL injection — string concatenation in queries instead of parameterized statements, (3) XSS — using innerHTML or dangerouslySetInnerHTML with user data, (4) Command injection — passing user input to exec() or spawn(), (5) Insecure randomness — using Math.random() for tokens or session IDs, (6) Missing rate limiting on authentication endpoints. Each of these is detectable with pattern-based analysis.
Frequently Asked Questions
What percentage of AI-generated code has security vulnerabilities?
According to Veracode's 2025 research, approximately 45% of AI-generated code contains security flaws. The most common issues are hardcoded credentials, injection vulnerabilities, and missing input validation. This rate is higher than human-written code because AI models optimize for functionality over security.
Does this scanner send my code to an AI model?
No. The scanner uses local regex-based pattern matching in your browser — it does not call any AI API, LLM, or external service. Your code stays on your device. This is a deliberate design choice: code security tools should not introduce their own data exposure risks.
Which AI coding tools produce the most vulnerabilities?
All AI coding assistants can produce vulnerable code. The risk depends more on the prompt and context than the specific tool. ChatGPT, Claude, Copilot, and Cursor all generate functional code that may include hardcoded secrets, injection-prone queries, and missing input validation. Always review AI-generated code before deploying.
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