29 Security Rules Every AI-Assisted Developer Must Know
The Complete Checklist to Lock Down Your Vibe-Coded Platform.
The Uncomfortable Truth About AI-Coded Apps
You shipped in record time. The features work. Users are signing up. Everything feels perfect, until it isn’t. The dirty secret of vibe coding isn’t that AI writes bad code. It’s that AI writes ‘incomplete code’. Code that works, but wasn’t built to survive.
When you ask an AI to “build me a login system,” it obliges. What it doesn’t tell you is that authentication is where 90% of data breaches start. When you say “let users upload profile pictures,” it makes it happen-without mentioning that one misconfigured storage bucket exposes every user file to Google search. These aren’t edge cases or theoretical vulnerabilities. They’re the predictable consequences of asking for features without specifying security constraints.
The good news? Most security holes in vibe-coded apps follow predictable patterns. AI assistants don’t think about session expiration, but you can explicitly request it. They don’t sanitize inputs by default, but a single prompt fixes that. The gap between a vulnerable app and a secure one isn’t expertise, it’s knowing which questions to ask.
This guide catalogs 30 security vulnerabilities commonly introduced during AI-assisted development, organized by category, with the exact prompts to fix each one. Read it before your next deploy. Or read it after-when you’re explaining to users why their data ended up on a hacker’s server.
Authentication & Session Security
Authentication is the front door to your application, and the most common entry point for attackers. AI-generated auth systems often appear functional while harboring critical vulnerabilities that only surface after a breach. The following rules address the most dangerous authentication pitfalls in vibe-coded applications, from session management disasters to the temptation of rolling your own login system.
Rule 1: Set Session Expiration
The Problem: Default AI authentication implementations often keep users logged in indefinitely. This creates a critical vulnerability where a single stolen cookie grants an attacker permanent access to the victim’s account. Session tokens, once compromised, become keys that never expire.
The Fix: Set JWT expiration to 7 days maximum and implement refresh token rotation. This ensures that even if a token is stolen, the window of opportunity for the attacker is limited. Refresh token rotation adds an additional layer of security by invalidating old tokens when new ones are issued.
Prompt: “Configure JWT tokens to expire after 7 days and implement refresh token rotation for secure session management”
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Rule 2: Never Trust AI-Built Authentication
The Problem: AI will happily generate a custom login system that appears to function correctly. It will handle registration, password hashing, and session creation. However, it will also contain security holes that aren’t immediately visible-weak password policies, improper token handling, missing rate limiting, and vulnerable password reset flows.
The Fix: Use battle-tested authentication providers that have been scrutinized by security professionals for years. Services like Clerk, Supabase Auth, Auth0, and NextAuth handle edge cases and attack vectors that most developers don’t even know exist. Their security teams monitor for new threats and patch vulnerabilities automatically.
Prompt: “Implement authentication using Clerk (or Supabase Auth/Auth0/NextAuth) instead of building a custom login system.”
Why It Matters: Authentication is where 90% of data breaches start. A single vulnerability in your login system can expose every user account in your database.
Secrets & Credentials Management
API keys, database credentials, and authentication tokens are the crown jewels of your application. AI assistants often suggest convenient but dangerous patterns for handling these secrets-hardcoding them during development, committing them to version control, or exposing them through error messages. The rules in this section establish proper secrets hygiene to prevent catastrophic credential leaks.
Rule 3: Never Paste Secrets Into AI Chats
The Problem: AI models learn from data. When you paste API keys, passwords, or database URLs into a chat interface, you’re potentially exposing those credentials. Your conversation may be logged, reviewed for training purposes, or accessible through your account history. A single careless paste can immortalize your production database password in systems beyond your control.
The Fix: Use environment variables with .env files for all sensitive configuration. Train yourself to tell the AI “use process.env.API_KEY” instead of sharing the actual key. This pattern keeps secrets local to your development environment and out of chat logs, version control, and AI training data.
Prompt: “Use process.env.API_KEY instead of hardcoding credentials. Never reference the actual key value in code.”
Why It Matters: One leaked key gives attackers complete control over your entire application infrastructure.
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Rule 4: Master the .gitignore File
The Problem: Vibe coding prioritizes speed, which creates the perfect conditions for accidents. In the rush to ship features, developers frequently commit their .env files containing all their secrets directly to GitHub. Public repositories are scanned by bots 24/7 looking for exposed credentials, and private repositories aren’t much safer, any team member or compromised account can expose them.
The Fix: Make creating a comprehensive .gitignore file your first action in any new project. Include .env files, node_modules, API keys, system logs, build artifacts, and any IDE-specific files. Review the .gitignore before every commit to ensure nothing sensitive slipped through.
Prompt: “Create a .gitignore that excludes .env, node_modules, API keys, system logs, build artifacts, and IDE configuration files.”
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Rule 5: Rotate Your Secrets Regularly
The Problem: Your API keys exist in more places than you realize. They’re in old git commits that you thought were deleted, Slack messages from debugging sessions, screenshots shared in support tickets, and tutorial videos you recorded. That database password from six months ago? It might still work, and someone might still have it.
The Fix: Establish a regular schedule for rotating all API keys and credentials-every 90 days at minimum. Use GitHub’s secret scanning feature to identify leaked keys in your repositories. When rotating, generate new keys before revoking old ones to avoid service interruptions.
Prompt: “Enable GitHub secret scanning and create a process for rotating API keys every 90 days.”
Dependencies & Supply Chain Security
Modern applications are built on layers of third-party dependencies, each representing a potential attack vector. AI assistants frequently suggest packages without verifying their legitimacy or security status. The rules in this section protect against supply chain attacks, phantom packages, and the known vulnerabilities hiding in outdated dependencies.
Rule 6: Beware of “Ghost” Packages
The Problem: AI sometimes hallucinates libraries that don’t actually exist. A sophisticated attack technique involves creating malware packages with these predicted names, then waiting for developers to install them. When an AI suggests a package you’ve never heard of, you might be installing a trojan horse created specifically to catch vibe coders.
The Fix: Always verify that any package the AI suggests actually exists before installing. Check npm, PyPI, or the relevant package registry directly. Look for established packages with significant download counts, recent updates, and active maintenance-not new packages with generic names.
Prompt: “Show me the npm or PyPI link for this package so I can verify it exists and check its security status.”
Why It Matters: Most vulnerabilities in modern applications come from supply chain attacks through compromised or malicious dependencies.
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Rule 7: Use Current Package Versions Only
The Problem: AI training data includes package versions from months or years ago versions with known security vulnerabilities that have since been patched. When the AI suggests installing a library, it may recommend a version with documented exploits available in public databases. Attackers actively scan for applications using outdated versions.
The Fix: When the AI suggests installing packages, explicitly ask about newer versions. Before deploying, run npm audit or equivalent tools to identify known vulnerabilities. Update any flagged packages and test for compatibility. Make dependency updates a regular part of your maintenance routine.
Prompt: “Are there newer, more secure versions of these libraries I should use instead? Check for any known security vulnerabilities.”
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Rule 8: Audit and Update Dependencies Regularly
The Problem: An AI might scaffold your application with packages from 2022. These older versions carry known exploits that have been documented in CVE databases. The initial setup works fine, but you’ve inherited a time bomb. As your application grows, so does the complexity of updating these foundational dependencies.
The Fix: After building with AI assistance, immediately run npm audit fix to address known vulnerabilities. Then ask the AI to check for breaking changes in the latest versions of your core dependencies. Schedule regular dependency audits-at least monthly-to catch newly discovered vulnerabilities.
Why It Matters: 80% of breaches exploit known vulnerabilities in outdated packages—vulnerabilities that have already been patched in newer versions.
Input Validation & Data Security
User input is the primary attack surface for web applications. AI-generated code often prioritizes functionality over security, accepting user input and passing it directly to databases, file systems, or other users. The rules in this section establish defensive boundaries around your data, preventing injection attacks, unauthorized access, and data exposure.
Rule 9: Sanitize Every Single Input
The Problem: AI frequently writes code that accepts user input and passes it directly to database queries without validation or sanitization. This enables SQL injection attacks where malicious users can execute arbitrary database commands, potentially extracting sensitive data, modifying records, or deleting entire tables.
The Fix: Explicitly instruct the AI to use parameterized queries for all database operations. Parameterized queries separate data from code, making it impossible for attackers to inject malicious SQL. Apply input validation on top of parameterization-reject unexpected characters, enforce length limits, and validate data types.
Prompt: “Ensure all database queries use parameterized queries to prevent SQL injection. Validate and sanitize all user inputs.”
Why It Matters: One unsanitized form field can lead to total database compromise-every user record, every password hash, every piece of sensitive data exposed.
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Rule 10: Enable Row-Level Security from Day One
The Problem: By default, most database configurations allow any authenticated user to access all data in tables they can query. Your application code might restrict what users see, but the database itself has no such restrictions. A bug in your API layer, a forgotten endpoint, or direct database access immediately exposes every user’s data.
The Fix: Implement Row-Level Security (RLS) policies at the database level. RLS ensures that users can only access rows where their user ID matches the record’s owner column. This creates a security boundary that survives application bugs and forgotten endpoints. Verify RLS is actually enabled-don’t assume the AI implemented it correctly.
Prompt: “Set up Row Level Security (RLS) policies so users can only access their own data. Verify this is enforced at the database level.”
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Rule 11: Remove Debug Statements Before Deployment
The Problem: AI assistants love to add console.log(userData) or equivalent debug statements to help you understand what’s happening during development. These statements often end up in production, where they expose sensitive user data to anyone who opens browser DevTools. Session tokens, personal information, and internal system details all leak through forgotten debug logs.
The Fix: Before every deployment, systematically remove all console.log and similar debug statements. Replace them with proper logging infrastructure that only logs necessary information and respects user privacy. Use linters and CI/CD checks to catch debug statements before they reach production.
Prompt: “Remove all console.log statements and replace with proper error logging that doesn’t expose sensitive user data.”
API & Network Security
APIs are the nervous system of modern applications, connecting front-end interfaces to back-end services and third-party integrations. Each endpoint represents an entry point that attackers can probe for vulnerabilities. The rules in this section secure your API layer against common attack patterns including cross-origin attacks, open redirects, and abuse through unprotected endpoints.
Rule 12: Configure CORS Properly
The Problem: AI often sets CORS (Cross-Origin Resource Sharing) to allow all domains (*) during development for convenience. This configuration frequently survives into production, allowing attackers to call your API from their malicious websites using your users’ authenticated sessions. Your API becomes an open proxy for data theft.
The Fix: Configure CORS to explicitly allow only your production domain. Specify the exact origin(s) that should be permitted to make requests to your API. Never use wildcard (*) in production. If you need to support multiple domains, maintain an explicit allow-list.
Prompt: “Configure CORS to only allow requests from my production domain: myapp.com. Never use wildcard origins in production.”
Why It Matters: Open CORS allows anyone to steal your users’ data through their own browsers-no hacking required.
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Rule 13: Validate All Redirect URLs
The Problem: Login pages commonly accept redirect parameters (?redirect=/dashboard) to send users to their intended destination after authentication. Attackers can manipulate this parameter to redirect users to malicious sites after they’ve logged in (?redirect=evil.com/phishing). Users see your login page, enter credentials, then land on a convincing fake page asking for additional information.
The Fix: Implement strict validation for all redirect URLs against an allow-list of permitted destinations. Only allow relative URLs (starting with /) or explicit matches against your approved domains. Reject any redirect that points outside your application.
Prompt: “Ensure all redirect URLs are validated against an allow-list before redirecting the user. Only allow relative URLs or explicitly approved domains.”
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Rule 14: Protect All API Endpoints Equally
The Problem: Your web application might have robust security, but attackers will find your weakest entry point. Mobile APIs, internal endpoints, web-hook receivers, and development servers often lack the same protections as your primary web interface. One unprotected endpoint undermines all your other security measures.
The Fix: Apply consistent security measures across all entry points. Every API endpoint-whether for web, mobile, internal services, or third-party integrations-should have proper authentication, rate limiting, input validation, and authorization checks. Attackers always target the path of least resistance.
Prompt: “Apply the same authentication, rate limits, and input validation to ALL endpoints including mobile APIs and web-hooks.”
Rate Limiting & Abuse Prevention
Without rate limiting, your application is defenseless against automated attacks. Bots can spam your contact forms, brute-force passwords, drain your API budgets, and overwhelm your infrastructure. AI assistants rarely implement rate limiting by default, leaving applications vulnerable from day one. The rules in this section establish the guardrails that protect your resources and your users.
Rule 15: Add Rate Limiting from Day One
The Problem: If you vibe code a contact form, API endpoint, or any user-facing feature without rate limiting, bots will discover it. They will submit it thousands of times per second. Your email inbox will flood, your database will fill with spam, and your API costs will explode. This isn’t hypothetical-it’s the default outcome for unprotected endpoints.
The Fix: Implement rate limiting on every user-facing endpoint from the beginning. Set sensible defaults like 100 requests per hour per IP address for general endpoints, with stricter limits for sensitive operations. Use sliding window algorithms for smoother enforcement and better user experience.
Prompt: “Add rate limiting to this API route - max 100 requests per hour per IP address.”
Why It Matters: Without rate limits, someone can drain your API costs overnight or use your infrastructure to attack others.
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Rule 16: Rate Limit Password Reset Requests
The Problem: Password reset endpoints are particularly attractive targets for abuse. Attackers can spam reset requests to flood a victim’s email inbox (email bombing), making it impossible to find legitimate messages. They can also attempt to brute-force reset tokens by generating thousands of requests. Both attacks are trivial to execute against unprotected endpoints.
The Fix: Apply strict rate limiting specifically to password reset routes. Limit to 3-5 requests per email address per hour. Implement exponential back-off for repeated attempts. Consider adding CAPTCHA challenges for multiple reset requests from the same source.
Prompt: “Add rate limiting to the password reset route: max 3 requests per email per hour to prevent email bombing and token brute-forcing.”
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Rule 17: Cap Your AI API Costs
The Problem: AI features don’t enforce their own spending limits. An attacker who discovers your OpenAI endpoint can hammer it with requests, racking up massive bills in hours. Real-world incidents have resulted in $10,000+ charges overnight. Your application works great until you get the bill-and by then, the money is already spent.
The Fix: Implement usage limits at two levels: in your provider dashboard (OpenAI, Anthropic, etc.) and in your application code. Set hard spending caps in the provider dashboard as an emergency brake. Implement per-user rate limiting in your application-max 50 AI requests per user per day is a reasonable starting point.
Prompt: “Add rate limiting to AI endpoints: max 50 requests per user per day, with usage tracking and alerts.”
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Rule 18: Get DDoS Protection
The Problem: Distributed Denial of Service attacks can hit any public application. Attackers can rent botnets to flood your server with 100,000+ requests per second for a few dollars. Your hosting costs spike, your legitimate users can’t access the service, and your site goes down. Application-level rate limiting can’t stop attacks that overwhelm your infrastructure before requests reach your code.
The Fix: Implement protection at the CDN or edge level before traffic reaches your application. Cloudflare’s free tier provides basic DDoS protection. Vercel’s Edge Config enables rate limiting at the edge. These services filter malicious traffic before it reaches your servers, keeping your application available and your costs predictable.
Prompt: “Configure DDoS protection and edge-level rate limiting using Cloudflare or Vercel Edge Config.”
File Uploads & Storage Security
File upload features introduce unique security challenges. Users can upload malicious content, excessively large files, or access other users’ files through misconfigured permissions. AI-generated file handling code often skips critical security measures, creating vulnerabilities that persist until a breach occurs. The rules in this section secure your file storage against common attack patterns.
Rule 19: Lock Down Your Storage Buckets
The Problem: When you vibe code file upload functionality, the AI often configures storage buckets as public by default. This makes every uploaded file immediately accessible to anyone with the URL-and those URLs are easily discoverable. User documents, profile pictures, and private files become searchable on Google. The breach is silent until someone stumbles across the data.
The Fix: Implement storage bucket policies that restrict access based on user authentication and ownership. In Supabase Storage or similar services, set RLS policies so users can only access files they uploaded. Use signed URLs for temporary access rather than making buckets public. Audit your storage configuration regularly.
Prompt: “Create storage policies so users can only access files they uploaded. Implement signed URLs for controlled access.”
Why It Matters: One public storage bucket can expose all user files to Google search and anyone who finds them.
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Rule 20: Limit Upload Sizes and Types
The Problem: AI-generated file upload code rarely includes size validation. A user can upload a 500MB video to your “profile picture” field, and your application will accept it. Your storage costs spiral upward, your bandwidth gets consumed, and your server struggles to process requests. Attackers can use oversized uploads as a denial of service attack.
The Fix: Set maximum file sizes appropriate to the use case-5MB for profile pictures, 50MB for documents. Validate file types server-side by checking file signatures, not just extensions. Reject executable files and potentially dangerous formats. Consider virus scanning for high-risk applications.
Prompt: “Validate file uploads server-side: max 5MB for images, check file type by signature not extension, reject executable files.”
Web-hooks & Third-Party Integrations
Web-hooks enable real-time integration with payment processors, communication platforms, and other services. However, they also create endpoints that accept external requests without traditional user authentication. AI-generated web-hook handlers often skip verification steps, creating vulnerabilities where attackers can inject fake events or abuse email-sending capabilities. The rules in this section secure your integration points.
Rule 21: Always Verify Web-hook Signatures
The Problem: If you accept Stripe payment, or other web-hooks, anyone who discovers your endpoint URL can POST fake data to it. Your application will process the fake web-hook as if it were legitimate-marking payments as complete when no money was received, updating order statuses, triggering fulfillment for non-existent purchases.
The Fix: Verify the authenticity of every web-hook using the provider’s signing mechanism. Stripe and similar services cryptographically sign their web-hooks. Your application must verify this signature before processing any web-hook data. Reject any web-hook that fails signature verification.
Prompt: “Verify the web-hook signature using Stripe’s SDK before processing any payment data. Reject unsigned or invalid web-hooks.”
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Rule 22: Use Proper Email Infrastructure
The Problem: If your application sends emails-whether intentionally or because an attacker abuses an endpoint-your domain can be blacklisted by email providers. Once blacklisted, your legitimate emails stop working. Password reset emails go to spam, notifications never arrive, and your product effectively dies. Recovery from blacklisting can take weeks.
The Fix: Use a verified sending service like Resend, SendGrid, or Amazon SES. Configure SPF, DKIM, and DMARC records to establish your domain’s email legitimacy. These services handle deliverability, provide analytics, and help you stay off blacklists. Rate limit your email endpoints to prevent abuse.
Prompt: “Configure email sending using Resend or SendGrid with proper SPF/DKIM records. Rate limit email endpoints to prevent abuse.”
Permissions & Access Control
Access control determines what authenticated users can do within your application. AI-generated code often implements authorization checks in the UI layer while neglecting server-side enforcement, creating the illusion of security while leaving APIs unprotected. The rules in this section ensure that permissions are enforced where they actually matter-on the server.
Rule 23: Check Permissions Server-Side
The Problem: Hiding a “Delete All” button in the UI doesn’t prevent anyone from calling the underlying API directly. UI elements are just suggestions-users can open DevTools, craft their own requests, or use command-line tools to interact with your APIs. If your authorization logic lives only in the fron-tend, you have no authorization logic at all.
The Fix: Every protected route must verify permissions on the server before executing any action. Check user.role === ‘admin’ or equivalent before allowing privileged operations. Implement role-based or attribute-based access control consistently across all endpoints. Never trust client-side checks.
Prompt: “Check if user.role === ‘admin’ on the server before executing any privileged action. Never rely on UI-level permission checks.”
Why It Matters: UI security is no security. Anyone can call your APIs with curl, Postman, or browser DevTools.
Security Auditing & Monitoring
Security isn’t a one-time implementation-it’s an ongoing process of verification and monitoring. AI assistants can help identify vulnerabilities when explicitly asked, but they won’t volunteer security concerns without prompting. The rules in this section establish practices for finding vulnerabilities before attackers do and maintaining visibility into your application’s security posture.
Rule 24: Ask AI for a Security Review
The Problem: AI writes code to work, not to be secure. It prioritizes functionality and often skips security checks entirely. The code runs, features work, and vulnerabilities hide in plain sight. You won’t know they exist until someone exploits them-or until you explicitly ask for a security review.
The Fix: After your code works, prompt the AI to act as a security engineer and review for vulnerabilities. Ask specifically about SQL injection, XSS, CSRF, insecure data handling, and authentication weaknesses. You don’t need to understand every fix-just knowing vulnerabilities exist is the critical first step to addressing them.
Prompt: “Act as a senior security engineer. Review this code for vulnerabilities like SQL injection, XSS, CSRF, and insecure data handling. List each issue and its fix.”
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Rule 25: Ask AI to Hack Your Application
The Problem: You don’t know what you don’t know about security. Vulnerabilities can exist in patterns you’ve never considered, using techniques you’ve never encountered. Traditional code review catches obvious issues, but creative attacks often slip through.
The Fix: Leverage AI’s knowledge of attack patterns defensively. Open your code in the AI editor and ask it to assume an attacker’s perspective. Request a comprehensive enumeration of vulnerabilities and exploitation methods. This adversarial review often reveals issues that normal code review misses.
Prompt: “You’re a hacker trying to break this app. Tell me every vulnerability you find, how you would exploit it, and how to fix each one.”
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Rule 26: Log Critical Actions
The Problem: When something goes wrong-fraud, data breaches, system failures, user complaints-you need to understand what happened and who was responsible. Without comprehensive logging, you’re flying blind. You can’t investigate incidents, prove compliance, or identify attack patterns.
The Fix: Create an audit log that captures every significant action in your application. Log user deletions, role changes, payment events, data exports, configuration changes, and administrative actions. Include timestamps, user IDs, IP addresses, and before/after states. Store logs securely and retain them for an appropriate period.
Prompt: “Create an audit_log table that captures user deletions, role changes, payments, data exports, and administrative actions with timestamps and user IDs.”
Infrastructure & Deployment
Production environments introduce security considerations that don't exist in development. AI assistants often generate code assuming a development context, without considering the infrastructure requirements of a live application. The rules in this section address the operational security measures that protect your application and users in production.
Rule 27: Build Account Deletion
The Problem: AI rarely includes proper account deletion functionality. Users accumulate data across your database, file storage, caches, and third-party integrations. Without a comprehensive deletion process, you’re holding data you no longer need-and potentially violating privacy regulations. GDPR violations can result in fines up to 4% of global revenue.
The Fix: Create a complete account deletion endpoint that removes all user data from every system where it exists. This includes database records, file storage, cached data, analytics, and any third-party services. Document the deletion process and provide users with confirmation.
Prompt: “Create an account deletion endpoint that removes all user data from the database, storage, caches, and third-party integrations.”
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Rule 28: Automate Backups
The Problem: AI doesn’t think about disasters. It builds for success scenarios where everything works correctly. But databases get corrupted, migrations fail, credentials leak, and bad actors delete data. Without backups, one bad migration deletes everything-permanently. There’s no undo button in production.
The Fix: Implement automated, tested backups from day one. If you’re using Supabase, enable Point-in-Time Recovery in your database settings. Whatever platform you use, verify that backups actually work by testing restoration regularly. A backup you can’t restore is not a backup.
Prompt: “Enable automated backups with Point-in-Time Recovery. Document the restoration process and schedule regular recovery tests.”
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Rule 29: Separate Test and Production Environments
The Problem: Testing payment integrations, web-hooks, or data migrations in production creates cascading problems. Test web-hooks hit real systems. Test charges hit real credit cards. Test data pollutes your production database. Debugging becomes a nightmare of distinguishing test artifacts from real issues.
The Fix: Maintain completely separate environments for testing and production. Use Stripe test mode keys with a separate staging database. Mirror your production configuration in a sandboxed environment where you can experiment safely. Never let test code touch production data or systems.
Prompt: “Set up a staging environment with test API keys and a separate database. Document the separation between test and production configurations.”
The Security Mindset Shift
Vibe coding isn't going away. The productivity gains are too significant, and the tools are only getting better. But the speed of AI-assisted development must be matched with intentional security practices. The vulnerabilities described in this guide aren't hypothetical-they're the predictable results of asking AI to build features without specifying security constraints.
The good news is that fixing most of these issues requires minimal effort. A few additional prompts during development, some verification steps before deployment, and regular audits after launch can transform a vulnerable application into a defensible one. The key is shifting from “does it work?” to “does it work securely?”
Keep this checklist visible during your next vibe coding session. Reference the prompts before deploying new features. Run security reviews on existing code. And remember: the attacker probing your application doesn’t care that AI wrote the vulnerability-they only care that it exists.
Ship fast. But ship secure.