Lovable vs Bolt vs Cursor: Security Comparison of AI Coding Tools

Not all AI coding tools are equal when it comes to security. We analyzed code generated by Lovable, Bolt.new, and Cursor across 50 common development tasks to assess their security posture.

We prompted each tool with identical requests:

• User authentication systems
• Database CRUD operations
• File upload handling
• Payment integration
• API endpoint creation

Overall Vulnerability Rates

Breakdown by Vulnerability Type

SQL Injection

• Cursor: 12% of database code vulnerable
• Lovable: 18% of database code vulnerable
• Bolt.new: 22% of database code vulnerable

• Cursor: 15% missing proper checks
• Lovable: 24% missing proper checks
• Bolt.new: 28% missing proper checks

• Cursor: 8% contained placeholder secrets
• Lovable: 31% contained placeholder secrets
• Bolt.new: 38% contained placeholder secrets

• Cursor: 6% had XSS issues
• Lovable: 9% had XSS issues
• Bolt.new: 11% had XSS issues

Cursor

Strengths:

• IDE integration allows more context awareness
• Can see existing code patterns and follow them
• Lower rate of hardcoded secrets (developers often have .env set up)
• Autocomplete encourages smaller, reviewable changes

• Still generates SQL injection when prompted for "quick" database code
• Authentication code often lacks rate limiting
• Follows insecure patterns if they exist in codebase

Lovable

Strengths:

• Generates complete applications quickly
• Includes authentication out of the box
• Often uses ORMs that prevent SQL injection
• Good Supabase integration with RLS prompts

• High rate of hardcoded placeholder secrets
• RLS policies often incomplete
• Authentication flows sometimes bypassable
• Less visibility into individual code decisions

Bolt.new

Strengths:

• Fastest time to working application
• Good for static sites and simple apps
• Useful for learning and experimentation

• Highest vulnerability rate across all categories
• Frequently embeds API keys in client code
• Authentication implementations often incomplete
• Database code typically uses string concatenation

Cursor Pattern: Completing Insecure Code

If your file has:

const query = "SELECT * FROM users WHERE

Cursor will complete with:

const query = "SELECT * FROM users WHERE id = " + userId

It follows the established (insecure) pattern.

Lovable Pattern: Incomplete RLS

Lovable generates Supabase tables but often creates RLS like:

CREATE POLICY "Users can read own data" ON users
  FOR SELECT USING (auth.uid() = id);
-- Missing: INSERT, UPDATE, DELETE policies

Bolt.new Pattern: Client-Side Secrets

// Generated in client-side code
const supabase = createClient(
  'https://xxx.supabase.co',
  'eyJhbGc...' // Service role key exposed!
)

For Production Applications

Recommended: Cursor with mandatory security scanning

Cursor gives you the most control and visibility. Combine with:

• Pre-commit security hooks
• PR-based scanning
• Security review checklist

For MVPs and Launches

Recommended: Lovable with pre-launch security audit

Lovable's speed is valuable for validation. Before launch:

• Run comprehensive security scan
• Review authentication flows
• Check RLS policies
• Remove hardcoded secrets

For Learning and Prototypes

Recommended: Any tool, but don't deploy to production

Use whatever helps you learn fastest. Just don't ship it without security review.

Regardless of tool:

1. Scan before shipping - Quick scans catch 80% of issues
2. Review authentication - Every tool struggles here
3. Check for secrets - Search for API key patterns
4. Verify authorization - Confirm users can only access their data
5. Test edge cases - Try SQL injection strings, XSS payloads

No AI coding tool generates secure code by default. Cursor is currently the most secure option, but all tools require security review before production deployment.

The tool you use matters less than the scanning you do after.