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AI coding agents optimize for making code run rather than making it secure, creating a growing security debt crisis. Research from Columbia University identifies three core failure patterns: prioritizing speed over safety, lacking awareness of side effects across codebases, and pattern-matching without understanding security implications. Real-world examples include agents hardcoding API keys in frontend code, setting Supabase/Firebase policies to public access, and using dangerouslySetInnerHTML without sanitization. Mitigations include spec-driven prompting grounded in OWASP Top 10, chain-of-thought security reasoning, thorough code diff reviews, and automated guardrails via CI/CD pipeline scanners like GitGuardian or TruffleHog.

The Reality of Vibe Coding: AI Agents and the Security Debt Crisis

3 Vibe Coding Security Bugs I’ve Seen Recently