A comprehensive whitepaper on security best practices for AI-assisted development (Vibe Coding).
This whitepaper aims to address the security challenges introduced by "vibe coding" - a development approach where developers use AI to generate code without reviewing it. As AI-assisted development becomes mainstream, understanding and mitigating its security risks is crucial.
- Input Validation & Injection Attacks
- Authentication & Authorization Defects
- Sensitive Information Exposure
- Insecure Dependencies & Supply Chain Risks
- Business Logic Vulnerabilities
- Resource Exhaustion & Denial of Service
- Security Tools & Automation
Based on 2024-2025 research:
- Up to 36% of AI-generated code contains security vulnerabilities
- 72% vulnerability rate for Java applications
- 90% of AI-generated code hardcodes sensitive information
- 67% of suggested dependencies contain known vulnerabilities
- 45% of the time, AI models pick insecure code patterns
- Chapter 2: Layered Security Architecture (Coming Soon)
- Chapter 3: Secure Vibe Coding Workflow
- Chapter 4: Tools and Technology Stack
- Chapter 5: Scenario-based Security Practices
Each chapter includes:
- Vulnerable Code Examples marked with ❌
- Secure Implementations marked with ✅
- Real-world Case Studies with citations
- Practical Mitigation Strategies
- Code snippets in multiple languages (Python, JavaScript, Java)
- Never trust AI-generated code without security review
- Implement automated security scanning in CI/CD
- Use parameterized queries for all database operations
- Store secrets in environment variables or secret management systems
- Implement proper input validation and sanitization
- Use current, maintained dependencies
- Apply rate limiting and resource constraints
- Enable comprehensive security logging
Contributions are welcome! Please feel free to submit issues or pull requests. Areas where we especially welcome contributions:
- Additional vulnerability examples
- Security tool recommendations
- Case studies from production environments
- Translations to other languages
- Updates on latest AI model behaviors
The whitepaper includes extensive references to:
- Academic research from Georgetown CSET
- Industry reports from Veracode, Contrast Security, and others
- Real-world incidents and case studies
- Security best practices from OWASP
This work is licensed under MIT License.
For questions or feedback, please open an issue in this repository.
Remember: In the age of AI-generated code, security is not optional—it's existential.