IS CURSOR SAFE?

AI code assistants frequently generate raw SQL queries with string concatenation or template literals instead of parameterized queries. This introduces SQL injection vulnerabilities that let attackers read, modify, or delete database contents through crafted input.

AI-generated form handlers and API routes typically lack CSRF token validation. Attackers can craft malicious pages that trick authenticated users into performing unintended actions like changing account settings or making transactions.

Cursor-generated code often includes debug routes, verbose error pages, and development-only endpoints that leak stack traces, environment variables, or internal application state. These endpoints are frequently left in production deployments.

AI models suggest package versions from their training data, which may include libraries with known security vulnerabilities. Without explicit version auditing, projects accumulate dependencies with published CVEs that attackers can exploit.

AI-generated authentication code may use weak session management, store tokens in localStorage instead of httpOnly cookies, or implement password hashing with outdated algorithms. These subtle flaws compromise user account security.

Does Cursor-generated code have security vulnerabilities?

AI-generated code can contain subtle security flaws like SQL injection, missing CSRF protection, and insecure authentication patterns. These issues are not unique to Cursor; any AI code assistant can produce vulnerable code. UNPWNED identifies externally observable exposure, configuration, dependency, form, endpoint, and error-disclosure signals regardless of how the code was written. CSRF and authentication logic require direct review. SQL-injection, reflected-XSS, and path-traversal probes are not enabled in the current production profile.

What are the most common security issues in AI-generated code?

The top issues are SQL injection through string concatenation in queries, missing CSRF tokens, exposed debug endpoints, and outdated dependencies with known CVEs. AI models also tend to use insecure defaults for authentication and session management. UNPWNED identifies externally observable exposure, configuration, dependency, form, endpoint, and error-disclosure signals. Database-query, CSRF, authentication, and session logic still require code review or targeted testing. SQL-injection, reflected-XSS, and path-traversal probes are not enabled in the current production profile.

How should I review Cursor-generated code for security?

Focus on database queries (ensure parameterized), authentication flows (check token storage and session handling), and API routes (verify authorization checks). Remove any debug endpoints and audit dependencies. Run an UNPWNED scan for bounded outside-in exposure, configuration, dependency, endpoint, CORS, form, rate-limit, and error-disclosure signals. Database, authentication, session, and authorization logic remain part of direct review.

Can AI code assistants introduce vulnerabilities that static analysis misses?

Yes, AI-generated code can contain logic-level vulnerabilities like broken access control and business logic flaws that static analyzers struggle to detect. Runtime testing and dynamic scanning can complement static tools. UNPWNED performs a bounded outside-in assessment of observable exposure, configuration, dependency, endpoint, CORS, form, rate-limit, and error-disclosure signals. Broken access control and business logic require API-specific testing or code review.

Can UNPWNED help secure code written with AI assistants?

Absolutely. UNPWNED scans your deployed application for externally observable client-secret, security-header, endpoint, dependency, CORS, form, rate-limit, and error-disclosure signals. It works regardless of whether the code was written by hand or generated by AI. SQL-injection, reflected-XSS, and path-traversal probes are not enabled in the current production profile.