Developers spend hours comparing AI coding tools on speed, code quality, and UX. But almost nobody asks the most important question: which tool writes the most secure code?
We decided to find out. Over the past quarter, we used ClearAudit to scan hundreds of production applications built with the three dominant AI coding tools: Cursor, Claude Code, and Lovable. We also looked at apps from Bolt, Windsurf, and v0 where we had enough sample size.
This isn't a hit piece on any tool. All three are excellent at what they do. But they each have different security blind spots, and understanding those blind spots is essential if you're shipping production software.
How We Tested
We scanned publicly accessible applications using ClearAudit's full 120+ point security audit. For each tool, we categorized vulnerabilities into six areas:
Privacy & Compliance (privacy policy, cookie consent, data handling disclosures)
We looked at apps across different deployment targets: Vercel, Netlify, Cloudflare Pages, Lovable hosting, Railway, and self-hosted.
Cursor: Inherits Your Patterns (Good and Bad)
Average ClearAudit Score: 42/100
Cursor works differently from the other tools. It operates inside your existing codebase, using your project structure, dependencies, and configuration as context. This gives it a major advantage: if you've already set up security headers, Cursor's generated code tends to respect them.
Where Cursor apps do well:
SSL/TLS configuration is usually solid because developers deploying Cursor-built apps typically configure their hosting provider properly
Code-level security patterns (input validation, parameterized queries) are better than average because Cursor inherits context from existing secure code in the project
Authentication implementations tend to be more complete because Cursor can see and follow the patterns in your existing auth setup
Where Cursor apps struggle:
Security headers are only present if the developer already knew to add them. Cursor doesn't proactively suggest adding CSP or HSTS
DNS security (SPF/DKIM/DMARC) is almost never configured because it's outside the codebase -- Cursor has no way to know about it
Rate limiting is rarely implemented unless the developer specifically asks for it
Cursor generates .env.example files but doesn't warn when environment variables containing API keys end up in the client bundle
Bottom line: Cursor produces the most secure code if you already know what secure code looks like. It amplifies your existing security practices -- but it also amplifies your existing security gaps. If you've never configured security headers, Cursor won't tell you to.
Claude Code: Strong Reasoning, Missing Infrastructure
Average ClearAudit Score: 38/100
Claude Code (Anthropic's command-line AI coding agent) produces impressively thoughtful code. Claude's underlying model has strong reasoning about security concepts, and it sometimes proactively adds input validation or warns about potential SQL injection.
Where Claude Code apps do well:
Code-level security is often the strongest of the three. Claude frequently adds input validation, output encoding, and proper error handling without being asked
Authentication flows tend to include important details like CSRF protection and secure session configuration
Claude sometimes adds security-relevant comments explaining why certain patterns are used
Where Claude Code apps struggle:
HTTP security headers are almost never added. Claude generates application code but doesn't typically touch server configuration, _headers files, or deployment configs
Like Cursor, DNS security is a complete blind spot -- it's infrastructure, not code
CORS is frequently misconfigured with Access-Control-Allow-Origin: * because it's the easiest way to make cross-origin requests work during development, and Claude doesn't differentiate between dev and production configs
Privacy compliance (privacy policies, cookie consent banners) is never generated unless specifically requested
API keys sometimes end up in client-side code because Claude focuses on making the feature work rather than considering the deployment security implications
Bottom line: Claude Code writes the best code-level security but completely misses infrastructure and deployment security. The code itself might handle input validation beautifully, but the deployed application is missing critical headers and DNS records.
Lovable: Full-Stack Speed, Full-Stack Gaps
Average ClearAudit Score: 35/100
Lovable takes a fundamentally different approach: it generates complete full-stack applications from natural language prompts. You describe what you want, and Lovable builds the frontend, backend, database, and authentication in minutes. This speed is incredible for prototyping and shipping MVPs.
Where Lovable apps do well:
SSL/TLS is automatically configured because Lovable handles deployment. Every app gets HTTPS out of the box
Database setup with Supabase is standardized, which means the baseline infrastructure is consistent
Authentication through Supabase Auth provides a solid foundation with built-in features like email verification and password hashing
Where Lovable apps struggle:
Security headers are the weakest of the three tools. Because Lovable handles deployment internally, developers have less control over server configuration, and headers like CSP and HSTS are rarely present
Rate limiting on custom endpoints is almost never implemented
DNS security records are never configured because Lovable generates the app but doesn't manage your domain's DNS
Privacy compliance features are rarely included in generated apps
Bottom line: Lovable's strength -- handling the entire stack -- is also its weakness for security. Because the developer doesn't manually configure infrastructure, they often don't realize what's missing. The deployment "just works," which creates a false sense of security.
The Comparison Table
Here's how the tools compare across our six security categories (percentage of apps with adequate security in each category):
Category
Cursor
Claude Code
Lovable
HTTP Security Headers
12%
8%
6%
SSL/TLS Configuration
89%
71%
95%
DNS Security
14%
11%
9%
API & Data Exposure
34%
41%
24%
Auth Security
45%
52%
38%
Privacy & Compliance
18%
15%
12%
What ALL Three Tools Get Wrong
The pattern is clear: no AI coding tool produces secure deployments by default. They all share these common gaps:
1. Security Headers Are Never Default
Not one of the three tools proactively adds Content-Security-Policy, Strict-Transport-Security, or Permissions-Policy. These HTTP security headers are the most basic layer of web security, and they're missing from the vast majority of AI-built apps.
2. DNS Security Is a Blind Spot
SPF, DKIM, and DMARC records live in DNS, not in code. AI tools operate on code. This means email authentication is a structural blind spot for every AI coding tool, regardless of how good their code generation is.
3. Development Defaults Ship to Production
The most dangerous pattern we see is development-time configurations making it to production: CORS: *, debug logging enabled, verbose error messages exposing stack traces, and .env files with API keys committed to repositories.
4. Security Is Reactive, Not Proactive
All three tools will add security features if you ask for them. None of them will tell you what's missing. If you prompt "add rate limiting to the login endpoint," they'll do it. But if you just say "build a login page," you get a login page without rate limiting.
The Solution: External Security Verification
The answer isn't to stop using AI coding tools -- they're too productive to give up. The answer is to add an external security verification layer that checks what the AI built after it's deployed.
Scan your deployed app -- not your source code, but the actual running application
Check 120+ security items across headers, SSL, DNS, API exposure, auth, and compliance
Generate an AI fix prompt that you paste back into your AI tool to fix every issue found
Rescan to verify that the fixes were applied correctly
It works with any AI tool because it checks the output, not the process. Whether you built with Cursor, Claude Code, Lovable, Bolt, or a combination -- ClearAudit checks the end result.
Which Tool Should You Use?
The best tool depends on your situation:
Use Cursor if you have an existing codebase with good security practices you want to maintain
Use Claude Code if you're building complex logic and want the strongest code-level security
Use Lovable if you want to ship fast and handle security as a separate step after deployment
But regardless of which tool you choose, run a security scan before you launch. The AI wrote functional code. It almost certainly didn't write secure code.
How secure is your app? Get your free security report -- ClearAudit runs 120+ security checks and 40+ SEO checks in about 2 minutes. No login. No credit card. Get Your Free Report