Vibe Coding Security Statistics
Comprehensive data on security vulnerabilities in AI-generated code. Research from Stanford, OWASP, and industry sources on the real risks of vibe coding.
Last updated: January 2025
Key Statistics
Stanford University research found that developers using AI coding assistants produce code with security vulnerabilities approximately 40% of the time when working on security-sensitive tasks.
Source: Stanford University (2023)
Security scans of AI-generated applications reveal that approximately 80% contain at least one exploitable vulnerability before remediation.
Source: OWASP AI Security Research (2024)
The majority of vibe-coded applications launch without essential HTTP security headers like Content-Security-Policy and Strict-Transport-Security.
Source: VAS Internal Research (2025)
Understanding AI Code Security Data
The security statistics for AI-generated code paint a concerning picture that every developer using vibe coding tools should understand. The landmark Stanford University study found that developers using AI assistants not only produced more vulnerable code but also exhibited overconfidence in their code's security. This combination of increased vulnerabilities and decreased vigilance creates significant risk.
The 40% vulnerability rate from Stanford's research applies specifically to security-sensitive tasks—authentication, input validation, cryptography, and data handling. For general coding tasks, AI performs well. But AI tools are particularly weak at security because they optimize for functionality: code that works correctly, compiles without errors, and produces expected output. Security requirements are often implicit and context-dependent, making them difficult for AI to infer.
The 80% figure for AI applications having at least one vulnerability comes from scanning deployed applications rather than code snippets. Real-world applications combine multiple components—frontend, backend, database, authentication—and vulnerabilities can exist at any layer or in the connections between them. The statistic reflects the cumulative probability of security issues across a complete application stack.
Common Vulnerability Types
These are the most frequently encountered security issues in vibe-coded applications, based on security scan data. Understanding these patterns helps prioritize remediation efforts.
Exposed API Keys
54%Over half of AI-generated applications have API keys, database credentials, or other secrets exposed in client-side JavaScript bundles.
Missing Database Security
68%Supabase RLS policies and Firebase Security Rules are frequently missing or misconfigured, allowing unauthorized data access.
Client-Side Auth Only
41%Authentication checks exist in frontend code but are not enforced server-side, allowing bypasses.
Missing Security Headers
72%HTTP security headers like CSP, X-Frame-Options, and HSTS are not configured.
Insufficient Input Validation
63%User input is not properly validated, opening applications to injection attacks and XSS.
Insecure Dependencies
45%AI-selected npm packages have known vulnerabilities or are unmaintained.
AI Coding Adoption Trends
AI coding tools have achieved widespread adoption, but security practices have not kept pace with usage growth.
of developers report using AI coding tools at least weekly
Source: GitHub Developer Survey (2024)
of developers report significant productivity gains from AI tools
Source: GitHub Developer Survey (2024)
of developers receive formal training on AI code security risks
Source: SANS Security Survey (2024)
of developers thoroughly review AI-generated code before committing
Source: Stack Overflow Survey (2024)
The Security Gap
While 78% of developers use AI coding tools weekly, only 23% receive formal training on AI code security risks, and just 34% thoroughly review AI-generated code. This gap between adoption and security awareness creates systemic risk in the software ecosystem.
AI Code Security Timeline
GitHub Copilot Preview
AI code generation becomes mainstream with Copilot's technical preview, sparking research into AI code security.
Stanford Security Study
Researchers publish findings that AI coding assistants lead to more security vulnerabilities. The 40% vulnerability rate becomes a widely-cited statistic.
Full-Stack AI Builders Emerge
Tools like GPT Engineer (now Lovable) and Bolt.new enable complete application generation from prompts, expanding security risks to entire applications.
OWASP AI Security Guidelines
OWASP releases comprehensive guidelines for AI-generated code security. Industry begins formalizing security practices for vibe coding.
Vibe Coding Term Coined
Andrej Karpathy popularizes 'vibe coding' terminology. Security scanning tools emerge specifically for AI-generated applications.
Research Sources
The statistics on this page are drawn from peer-reviewed research, industry surveys, and security organization reports. Here are the primary sources for further reading.
Methodology Notes
Stanford University Study: Conducted controlled experiments with participants completing security-relevant coding tasks with and without AI assistance. Measured vulnerability rates through static analysis and manual code review. Sample included professional developers and computer science students.
Vulnerability Prevalence Data: Based on automated security scanning of publicly accessible web applications built with AI coding tools. Applications were identified through platform indicators (Lovable, Bolt.new deployment patterns) and scanned using DAST methodology.
Adoption Statistics: Drawn from large-scale developer surveys by GitHub, Stack Overflow, and SANS Institute. Sample sizes ranged from 5,000 to 90,000 respondents depending on the survey.
Limitations: Security statistics reflect point-in-time measurements. Vulnerability rates may vary by application type, developer experience, and AI tool used. Industry data may have selection bias toward developers who participate in surveys.
Don't Be a Statistic
80% of AI-built apps have security vulnerabilities. Scan your vibe-coded application before deployment to identify and fix issues that automated research consistently finds in AI-generated code.