AI CODING TOOL SECURITY

Where to start

If you’re trying to figure out how secure AI coding is in general, start with Vibe Coding Vulnerabilities — the 14 patterns that ship in nearly every AI-built app.

If you’re evaluating a specific tool, jump to its risk profile below.

If you’re shipping AI-generated code to production today, the Vibe Code Scanner finds the patterns in your live app in under 60 seconds.

The pattern across every AI coding tool

Across 1,400+ scanned applications, the same 14 vulnerability patterns recur regardless of tool. The top 5 by incident count:

1. Missing Row Level Security on Supabase / Firebase tables (~70% of backed-by-Supabase apps)
2. Hardcoded API keys in frontend bundles (~25% of vibe-coded apps)
3. BOLA / IDOR on CRUD endpoints (~85% of apps with CRUD APIs)
4. Missing input validation (near universal)
5. Over-permissive CORS (~40% of vibe-coded APIs)

Full taxonomy in Vibe Coding Vulnerabilities.

Tool-by-tool risk profiles

Editor-based AI tools

• Cursor Security Risks — 12 patterns in Cursor-generated code
• Cursor Enterprise Security — Business plan, SOC 2, admin policy
• Copilot Security Risks — Copilot-specific risk profile
• Is Cursor Safe? — IDE-level audit
• Is Windsurf Safe? — Codeium / Cascade audit
• Is GitHub Copilot Safe?
• Is Claude Code Safe?

Cloud-based AI builders

• Is Lovable Safe? — RLS, BOLA, key-leak gaps
• Is Bolt Safe? — Bolt.new security audit
• Is v0 Safe? — v0.dev security audit
• Is Replit Safe? — public repls, Secrets in forks
• Is Base44 Safe?
• Is Figma Make Safe?

Autonomous AI agents

• Is Devin Safe? — Cognition Labs / autonomous-agent security

Fundamentals

The vulnerability taxonomy

• Vibe Coding Vulnerabilities — 14 patterns common to all AI tools
• AI Code Vulnerabilities — vulnerability classes and how AI introduces them
• AI-Generated Code Risks — risk analysis by threat category
• Low-Code Security Vulnerabilities
• OWASP Top 10 for AI Code

Best practices

• Secure AI Coding Practices — prompts and patterns for generating secure code
• AI Code Review Guide — review framework for AI-generated commits
• AI Code Quality Assessment
• AI Security Testing Tools

Hardening guides

• How to Secure Cursor — 12-step hardening guide
• Firebase Security Rules: 12 Common Mistakes — fixing the #1 critical
• Agentic Code Review Guide

The four-layer defense

Every team shipping AI-generated code needs all four. Skip a layer and the others can’t compensate:

Layer 1 — Lock down what AI sees

.cursorignore (or equivalent for your tool) on every repo. Privacy Mode enabled. MCP servers audited. Codebase context excludes secrets, infra-as-code, customer data fixtures.

Layer 2 — Gate what AI’s output ships

Branch protection on main. Required PR review for every AI-generated commit. PR template that flags AI-generated portions for reviewer focus.

Layer 3 — Static security scan in CI

Secret detection (gitleaks). Dependency audit. Static analysis for the AI-pattern bugs from the vulnerability taxonomy. Block merge on critical.

Layer 4 — Dynamic scan on every deploy

Static catches patterns in source. Dynamic catches the bugs that only surface at runtime — RLS gaps, BOLA, CORS, JWT verification. The Vibe Code Scanner runs against the deployed app in under 60 seconds.

Free tools

• Vibe Code Scanner — full dynamic scan, all 14 patterns
• Token Leak Checker — find API keys in your frontend bundle
• Firebase Scanner — Firestore Security Rules audit
• Supabase RLS Checker — verify every table has RLS
• Package Hallucination Scanner — find AI-invented dependencies
• Security Headers Checker — CSP, HSTS, X-Frame audit

Browse safety reviews by category

• All Platform Safety Reviews — every “is X safe?” guide, hub-indexed
• Alternatives — VibeEval vs other scanners — Snyk, Veracode, Checkmarx, Burp Suite, others
• Comparisons — tool-vs-tool security comparisons
• Updates — recent platform-specific security updates