AI Coding Security

Security for the AI-assisted workflow itself · AI-generated code risks, agent and MCP security, prompt injection, and AI code review.

  • Is Vibe Coding Safe? Security Risks of AI-Generated Code · Vibe coding is safe only with a security layer the AI doesn't provide. SimplyScan's scans of 177 AI-built apps show 33% carry a high or critical issue. AI often prioritizes functionality over safety, leading to exposed secrets and broken auth. Prompt for security explicitly and scan every app before launch.
  • AI Code Review vs Security Review: Why You Need Both · Code review asks whether code works; security review asks whether it can be exploited. They are not interchangeable. While AI tools like Cursor and Bolt.new excel at functional logic, they often skip critical security checks like RLS policies and secret management. You need both to ensure your app is functional and defensible.
  • AI Security Risks: Prompt Injection, LLM Abuse, and API Key Exposure · AI features introduce three critical risks: prompt injection, where user input overrides system instructions; LLM abuse, where unprotected endpoints lead to massive API costs; and API key exposure, where hardcoded secrets allow attackers to hijack your accounts. Defend your app with server-side keys, per-user rate limits, and role-separated prompts.
  • OWASP Top 10 for AI-Built Apps: What Vibe Coders Need to Know · AI-built apps are vulnerable to the OWASP Top 10 because AI models reproduce insecure patterns like frontend-only access control and string-concatenated SQL. To secure your app, you must enforce server-side RLS, use parameterized queries, and validate all inputs. SimplyScan's data shows 30% of AI apps have high-severity issues.
  • API Security Best Practices for AI-Built Applications · Every API endpoint is a public attack surface. Secure AI-built backends by enforcing authentication on all sensitive routes, using schema-based input validation (Zod), applying object-level authorization (BOLA/IDOR) checks, and locking down CORS. SimplyScan's research shows 30% of AI-built apps ship with high-severity security issues that these practices mitigate.
  • Why Your AI-Built App Is Slow (And How to Fix It) · AI-built apps are typically 500ms to 1500ms slower than necessary due to redundant auth calls, heavy JS bundles, and render-blocking spinners. SimplyScan's analysis shows many apps suffer from these speed issues. Fixing them via code splitting and CSS animations can reduce bundle sizes and significantly boost user conversions.
  • Why Cursor, Lovable, and Bolt Don't Optimize Your App's Speed (And What Does) · Cursor, Lovable, and Bolt.new optimize for development speed, not runtime performance. They often skip code splitting, query deduplication, and asset optimization. SimplyScan’s data shows 71% of AI-built apps have speed issues. Our scanner identifies these AI-specific bottlenecks in 30 seconds, providing exact code fixes to recover lost conversions.
  • Scan Your App for Security From Inside Cursor and Claude · SimplyScan's Model Context Protocol (MCP) server allows AI assistants like Claude, Cursor, and Windsurf to run security, speed, and AI-visibility scans directly from your editor. By connecting to api.simplyscan.io/mcp, your agent can find vulnerabilities, write fixes, and verify them in a single conversation, closing the gap between vibe-coding and secure shipping.
  • Is ChatGPT-Generated Code Safe to Ship? What to Check First · ChatGPT-generated code is safe to ship only after verification. It runs on the happy path but routinely carries outdated security patterns, hallucinated package names attackers can squat, placeholder credentials that become real leaked keys, and missing server-side validation. Verify every dependency, sweep for secrets, and confirm authorization before production.
  • Claude Code Security Checklist: Ship Agent-Written Code Safely · Secure Claude Code by securing the session: keep auto-approval off for shell commands, use deny-rules for .env files so secrets never enter the context, treat external content as a potential prompt-injection vector, and always scan the deployed app to catch configuration drift and exposed secrets.
  • Vibe Coding Guardrails · How to Let AI Write Code Without Getting Burned · Vibe coding guardrails are essential rules—like forbidding hardcoded API keys and mandating Row-Level Security—that prevent AI builders from shipping vulnerable code. By combining standing instructions, targeted security prompts, and automated scans, you can build at AI speed without sacrificing security or exposing sensitive user data.
  • The Best AI Code Security Tools in 2026 · What Actually Catches AI-Written Bugs · The best AI code security tools in 2026 include SimplyScan for no-setup black-box testing, Snyk for dependency audits, and Semgrep for static analysis. Vibe coders should prioritize scanning their deployed URLs first to catch infrastructure-level risks like exposed API keys and missing RLS policies that AI frequently introduces.
  • AI API Security · Protecting LLM-Powered Apps, Keys, and Endpoints · AI API security involves protecting LLM keys (OpenAI, Anthropic) and hardening the endpoints you build. To secure your app, keep keys server-side in a proxy, implement per-user rate limits to prevent "denial of wallet" attacks, and sanitize all model outputs to block XSS and prompt injection.
  • Windsurf Security Risks: How to Secure Your AI-Flow Codebase · Windsurf security risks include hardcoded secrets, insecure AI-generated code patterns, and unauthorized terminal executions. While Windsurf is a powerful agentic IDE, it requires strict human oversight, environment variable management, and automated security scanning to prevent vulnerabilities like XSS, SQL injection, and data leaks in AI-built applications.
  • The Ultimate Vibe Coding Security Checklist: Ship AI Apps Safely · A vibe coding security checklist ensures AI-generated apps are safe for production. Key steps include auditing for exposed API keys, verifying Supabase RLS policies, and validating security headers. Using automated tools like SimplyScan allows developers to maintain the speed of vibe coding without compromising on essential security best practices.
  • Firebase Rules for AI Apps: A Security Guide for LLM Architectures · Firebase rules for AI apps must prioritize data ownership and input validation to prevent prompt injection and unauthorized access. Use strict UID checks, limit string lengths for AI-generated content, and enforce immutability for chat histories. Always move beyond default 'Test Mode' rules to protect sensitive LLM context and user data.
  • Vibe Securing: Why 30% of AI-Built Apps Have Critical Vulnerabilities · Vibe securing implements automated guardrails for AI-generated apps. SimplyScan's data from 170 scans reveals 30% of vibe-coded apps have high/critical vulnerabilities. To ship safely, developers must use independent scanning, verify Row Level Security (RLS), and use MCP servers to catch vulnerabilities like exposed keys and broken access control in real-time.
  • Managing Your Cursor Library: How to Index Code Without Leaking Secrets · Manage your Cursor library by enabling Privacy Mode and using a .cursorignore file to exclude sensitive data. While indexing improves AI context, it can leak secrets if hardcoded keys are included. Use SimplyScan to detect exposed credentials before they are indexed into the LLM context window.
  • SimplyScan vs SonarQube · Do You Need a Full Code Platform? · SonarQube is a deep code-governance platform for enterprise teams, while SimplyScan is a 30-second health scanner for AI-built apps. If you need to enforce strict coding standards across a large team, choose SonarQube. If you want to find exposed secrets and security flaws on a live site instantly, SimplyScan is the better alternative.
  • Replit Security Guide: How to Secure Your Deployed AI Apps · Secure your Replit apps by using the Secrets tab for environment variables, implementing robust security headers, and configuring proper CORS policies. SimplyScan's research shows 46% of AI-built apps have architecture issues; use a specialized scanner to detect exposed keys and broken auth in your Repl before deploying to production.
  • Cursor vs. Bolt.new: Which AI Tool Produces More Secure Code? · Cursor and Bolt.new are safe to install, but the code they generate often contains critical vulnerabilities. SimplyScan's data shows 33% of AI-built apps have high-severity security issues. While Cursor offers local control and Bolt provides a sandboxed browser environment, both require manual auditing for exposed API keys and broken access control.
  • Automated Security Monitoring for Vibe-Coded Applications · Uptime monitoring for AI apps must go beyond simple status checks to include automated security and performance validation. With 33% of AI-built apps containing high-severity security flaws, monitoring must track Supabase RLS regressions, exposed API keys, and performance spikes to ensure autonomous code updates do not compromise user data.
  • GDPR and Compliance Signals · How to Audit Your App for Privacy Risks · GDPR compliance for AI apps in 2026 requires implementing technical measures like security headers, Row Level Security (RLS), and honoring Global Privacy Control (GPC) signals. Beyond privacy policies, regulators look for compliance signals like encrypted data transit and protected API keys to ensure apps meet EU AI Act and GDPR standards.
  • How to Secure Your Groq API Key: Fixing the #1 Leak in AI-Built Apps · To get a Groq API key, sign in to console.groq.com and generate a new secret in the API Keys section. To secure it, never use the key in frontend React code; instead, proxy requests through a backend or serverless function to prevent unauthorized access and billing exhaustion.
  • What is Prompt Engineering for Security? Writing Safer Prompts for AI App Builders · Prompt engineering is the process of crafting specific instructions to guide AI models toward high quality, secure outputs. For app builders, it involves defining security constraints—like RLS policies and input sanitization—within the prompt to ensure AI-generated code is resilient against vulnerabilities like exposed API keys or XSS.

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