Bolt.new vs Lovable vs Cursor: Which Produces the Most Secure Code?

Quick answer: Lovable produces the most secure code out of the box by generating RLS policies and auth flows by default. Cursor is safest for experts who can prompt for specific security requirements, while Bolt.new requires the most hardening. SimplyScan found 33% of AI-built apps contain high or critical severity vulnerabilities.

By Paula C · Kraftwire Software

Which AI Coding Tool Writes the Most Secure Code?

Lovable produces the most secure code out of the box because it generates Row-Level Security (RLS) policies and complete authentication flows by default. Cursor is the safest for experienced developers who can explicitly prompt for security requirements, while Bolt.new typically requires the most hardening due to its focus on rapid prototyping and browser-based execution.

This highlights a "vibe coding" paradox: the code looks professional and functions correctly, but underlying security gaps often go unnoticed. In SimplyScan's scans of 177 AI-built apps, 58 of those apps (33%) had at least one HIGH or CRITICAL severity issue. This suggests that regardless of the tool chosen, the "vibe" of a working app does not guarantee a secure production environment. When you are building with AI, you are essentially outsourcing your security posture to a probabilistic model that prioritizes "making it work" over "making it safe."

How Does Each Tool Work?

Understanding the security posture of these tools requires looking at their architectural approach to code generation. Each platform makes trade-offs between developer speed and system integrity.

Lovable

Lovable is a full-stack AI app builder that generates React applications with a native Supabase backend. It handles the database schema, authentication, and edge functions in one pass. It is designed to be a "full-stack engineer in a box," meaning it makes many architectural decisions for you.

Security implication: Because Lovable generates the entire stack, security is systemic. It includes Supabase RLS by default, which is a major advantage. However, if the AI misconfigures a global policy, the entire data layer may be exposed. SimplyScan's data shows that architecture issues (medium) appeared in 81 apps (46%), often stemming from how these automated builders structure their data access layers. When the AI designs the architecture, it may create circular dependencies or inefficient data fetching patterns that inadvertently expose more data than necessary to the frontend.

Bolt.new

Bolt.new provides a full-stack development environment that runs entirely in the browser using WebContainers. It allows for instant previews and one-click deployments to platforms like Netlify or Vercel. It is optimized for speed and "vibe-to-code" efficiency, allowing a user to go from a prompt to a deployed URL in under a minute.

Security implication: Bolt.new prioritizes functional completeness. In the rush to make an app "work," security configurations like security headers and environment variable isolation are often skipped. Because the environment is browser-centric, there is a higher risk of developers accidentally exposing secrets that should remain server-side. Bolt often suggests client-side logic for tasks that should be handled by a secure backend, such as direct database writes or API calls to sensitive third-party services.

Cursor

Cursor is an AI-native code editor (a fork of VS Code). It doesn't "host" your app; it helps you write it. It uses project-wide context to suggest edits and generate new files. It is the most flexible tool because it works with any language, framework, or cloud provider.

Security implication: Cursor is a "bring your own architecture" tool. It won't force security on you. If you don't know to ask for CSRF protection or input validation, Cursor likely won't suggest them proactively. However, because it has access to your local files, it can perform better context-aware security checks if prompted correctly. The risk here is human error; if the developer accepts a "hallucinated" security fix without verification, the vulnerability remains.

Secret Management: Where the Leaks Happen

Hardcoded secrets are the most common critical failure in AI-generated code. SimplyScan found that security issues (high) appeared in 19 apps (11%), with exposed API keys being a primary culprit. This often happens because the AI does not understand the boundary between the build-time environment and the runtime environment.

Lovable and the Client-Side Leak

Lovable applications have historically struggled with distinguishing between public and private environment variables. A notable risk involves leaked API keys where Supabase service-role keys (which bypass RLS) are bundled into the frontend JavaScript. While Lovable has improved its guardrails, developers must still ensure that no sensitive keys are prefixed with VITE_, as these are automatically exposed to the browser. If an AI suggests putting a Stripe Secret Key into a .env file with a VITE_ prefix, that key is no longer a secret; it is a public asset.

Bolt.new and Third-Party Integrations

When prompting Bolt.new to "add Stripe" or "connect to OpenAI," the AI frequently generates code that performs fetch requests directly from the frontend. This requires the API key to be present in the client's browser. Without a backend proxy, these keys are easily extracted by anyone viewing the page source. This is a common pitfall for those using Bolt.new for rapid prototyping without a dedicated backend. The "vibe" of a working integration masks the fact that your OpenAI billing account is now public property.

Cursor and Git History

Cursor's risk is more traditional. It may suggest hardcoding a key in a config.ts file for "testing purposes." If the developer isn't careful, that secret is committed to GitHub. Because Cursor has access to your entire codebase, it might also "hallucinate" that a secret is already managed in an .env file when it is actually hardcoded in a different module. Developers using Cursor must be vigilant about .gitignore files and ensure that the AI isn't suggesting "temporary" hardcoded values that eventually become permanent.

Winner: Cursor · Because it doesn't manage the deployment or the full environment, it is less likely to "auto-bundle" a secret into a public build, provided the developer uses a proper environment variables setup and follows standard Git hygiene.

Database Security: The RLS Battle

Database authorization is where most "vibecoded" apps fail. If the database is not secured at the row level, any user with the public API key can potentially read or write any data in the system.

Lovable: The RLS Default

Lovable is the only tool in this group that attempts to write Row-Level Security policies by default. It generates SQL snippets to ensure users can only see their own data. This is a massive step forward for AI safety. However, these policies are often "hallucinated" or overly permissive, such as using USING (true) for read access, which allows any visitor to scrape the entire table. SimplyScan often detects these "leaky" RLS policies where the AI intended to secure the data but failed to implement the correct SQL logic.

Bolt.new: The "Open by Default" Risk

Bolt.new often generates Firebase or Supabase configurations that are set to "Test Mode." This means the database is open to the world for 30 days or until rules are manually written. This "speed-first" approach is dangerous for production-bound applications. Because Bolt.new focuses on the frontend experience, it often treats the database as a simple JSON store rather than a secured infrastructure component.

Cursor: Context-Dependent Security

Cursor will write perfect RLS policies if you provide the schema and ask for them. If you don't, it will generate standard SELECT * queries that assume the database is already secured. It lacks the "proactive" security layer found in Lovable, making it riskier for beginners who don't know to ask for database security. However, for an expert, Cursor is the best tool for auditing existing policies because you can highlight a block of SQL and ask, "What are the security flaws in this policy?"

Winner: Lovable · It is better to have a permissive policy that you can tighten than to have no policy at all. Lovable's integration with Supabase makes it the clear leader in structured data security for AI builders.

Authentication and Authorization Gaps

• Lovable: Uses Supabase Auth, which is robust. The risk is usually in the implementation, such as checking for an is_admin flag in localStorage rather than verifying the JWT on the server. This is a classic "vibe coding" error where the UI looks restricted, but the API is wide open.
• Bolt.new: Often generates "mock" auth or simple JWT implementations that lack rate limiting and brute-force protection. If you ask Bolt to "make a login page," it might build a beautiful UI that stores passwords in plain text or uses a weak hashing algorithm.
• Cursor: Security depends on the library you tell it to use. If you use next-auth, it follows those patterns. If you ask it to "write a login script," it might use insecure bcrypt rounds or plain-text cookies. Cursor's output is only as secure as the developer's instructions.

XSS and Code Injection Prevention

All three tools primarily use React, which provides built-in protection against Cross-Site Scripting (XSS) by escaping variables. However, risks remain when the AI tries to be "clever" with dynamic content.

• DangerouslySetInnerHTML: AI tools often use this to render Markdown or CMS content, creating an XSS vector. If the AI doesn't also suggest a sanitization library like dompurify, the app is vulnerable.
• URL Injection: Bolt.new and Lovable often generate code that takes URL parameters and inserts them directly into the DOM or a database query. This can lead to reflected XSS or even SQL injection if the backend isn't properly parameterized.
• Code Injection: In SimplyScan's scans, code injection risks are a primary focus for AI-built apps because the AI might suggest using eval() for dynamic logic to solve complex state problems. This is particularly common in Bolt.new projects where the AI is trying to bridge the gap between the browser and the simulated server environment.

Winner: Tie · All three rely on the underlying framework (React/Next.js) for security, but none are immune to the AI's tendency to take "shortcuts" with dangerous functions.

Performance and Speed: The Hidden Security Risk

Speed isn't just about UX; it's about availability. A slow site is more vulnerable to Denial of Service (DoS) because a few heavy requests can exhaust the server's resources. Bolt.new apps, being browser-heavy, often struggle with initial load times and "Main Thread" blocking.

SimplyScan's study of 177 apps found that speed issues (medium) appeared in 124 apps (70%). Unoptimized AI code often includes massive libraries for simple tasks, increasing the attack surface and slowing down the response time for security-critical operations like password hashing or token verification. Furthermore, speed issues (high) appeared in 16 apps (9%), which can lead to complete site failure under even moderate traffic. A secure app that is offline is still a failure.

AEO and AI Visibility: Can Engines Find Your App?

If you are building a public-facing site, security includes ensuring your site is visible to the right "people" · including AI agents. Answer Engine Optimization (AEO) is the practice of making your site readable for LLMs. This is the flip side of security: while you want to hide your secrets, you want to expose your content.

If your AI-built app has broken meta tags or a restrictive robots.txt, AI search engines like Perplexity or ChatGPT won't be able to index it. SimplyScan's AI visibility tool checks if your "vibecoded" app is actually discoverable. Many Bolt.new apps fail here because they are deployed as Single Page Applications (SPAs) without proper server-side rendering, making it difficult for bots to "see" the content behind the JavaScript.

The Verdict: Which Tool Should You Use?

• Use Lovable if: You are a non-technical founder or a developer who wants a secure-by-default starting point with Supabase. It handles the "hard parts" of security (Auth/RLS) better than the others. It is the best choice for building data-driven applications that require user isolation.
• Use Cursor if: You are an experienced developer who can perform a manual security audit and knows how to prompt for specific security headers and validation logic. Cursor is a power tool that requires a skilled operator to avoid safety pitfalls.
• Use Bolt.new if: You are prototyping rapidly or building internal tools where speed is more important than a hardened production environment. It is unmatched for "vibe-to-deployed-app" speed, but the technical debt (and security debt) accumulates quickly.

How to Secure Your AI-Built App Today

Regardless of the tool, "vibecoded" apps require a second pair of eyes. The "vibe" might be good, but the code might be leaking. AI is excellent at syntax but poor at systemic security reasoning.

• Run a Security Scan: Use SimplyScan to check for exposed API keys, missing RLS, and weak headers. It takes 30 seconds and requires no signup. It is the only scanner specifically tuned for the patterns found in Lovable, Bolt, and Cursor apps.
• Check Your Headers: Use the Security Headers Tool to ensure you have CSP and HSTS enabled. Most AI tools omit these entirely to ensure the app "just works" in all environments.
• Audit Your Database: If using Lovable or Bolt, verify your Supabase or Firebase rules manually. Never trust an AI-generated SQL policy without testing it against a non-admin user.
• Monitor Uptime: Use Uptime Monitoring to ensure your app stays online and performant. AI-generated code can often have memory leaks or recursive loops that crash your server under load.
• Verify Secrets: Check your bundled JavaScript for any strings that look like API keys. Use the Exposed API Key Scanner to automate this process.

Scan Your Platform

• Lovable Security Scanner · Detect exposed keys and RLS gaps in Lovable projects.
• Bolt.new Security Scanner · Find vulnerabilities in browser-deployed Bolt apps.
• Cursor Security Scanner · Check for hardcoded secrets and logic flaws in your Cursor codebase.
• Windsurf Security Scanner · Audit apps built with the Windsurf IDE.
• v0 Security Scanner · Scan Vercel v0 generated components for XSS and injection risks.

Related Reading

• Is Vibe Coding Safe? · A deep dive into the risks of AI-driven development.
• The Vibe Coding Security Checklist · Essential steps before you go live.
• How to Fix Exposed API Keys · A guide to moving secrets to the backend.
• Understanding Answer Engine Optimization (AEO) · Making your app visible to the next generation of search.
• SimplyScan vs. Platform Security · Why built-in platform checks aren't enough.

The era of vibe coding is here, but security cannot be left to a vibe. Whether you choose Lovable, Bolt, or Cursor, the responsibility for the final "Secure" grade remains with the developer. Use the speed of AI to build, but use the precision of automated scanning to protect. By combining the rapid generation capabilities of these tools with a rigorous security review process, you can ship faster without sacrificing the safety of your users' data. Remember: 33% of your peers are shipping critical vulnerabilities; don't let your app be one of them.",excerpt: