Secure Vibe Coding

Secure Vibe Coding #

  • Thesis ID: 25-04
  • Research Proposal: Securing AI-Generated Code: Developing Methods for Vulnerability Detection and Secure Code Generation
  • Collaboration company/organization: Lovable

Abstract #

Recent advances in AI-assisted software development have enabled systems such as GitHub Copilot, OpenAI Codex, and Lovable’s Vibe Coding platform to assist developers by generating functional code snippets and applications. While these tools accelerate development, they also introduce novel security risks, as AI-generated code may contain hidden vulnerabilities such as injection flaws, insecure dependencies, or improper data handling.

The integration of AI models into the software development lifecycle challenges traditional approaches to code review and security assurance. This research seeks to systematically explore how AI-generated code can be evaluated for vulnerabilities, and how secure code generation methods can be developed to mitigate security risks before deployment.

Details

Problem Statement #

AI-generated code often lacks the contextual awareness and security reasoning of expert human developers. Consequently, it may introduce logic errors, unsafe defaults, or insecure coding patterns. There is a need for automated frameworks and methodologies to detect, analyze, and prevent vulnerabilities in AI-generated code, ensuring safe integration into production environments.

Research Objectives #

  1. Analyze vulnerabilities in AI-generated code from Lovable’s Vibe Coding platform.
  2. Develop automated tools or methods to detect security flaws in AI-generated applications.
  3. Propose and evaluate strategies for secure code generation using large language models (LLMs).
  4. Collaborate with Lovable to integrate secure generation principles into their development workflow.

Methodology #

  1. Data Collection: Obtain AI-generated code samples from Lovable’s Vibe Coding system, covering diverse project types and programming languages.
  2. Static and Dynamic Analysis: Apply static analysis tools (e.g., Semgrep, CodeQL) and dynamic testing (e.g., fuzzing, runtime monitoring) to identify common vulnerabilities (e.g., CWE and OWASP Top 10 categories).
  3. LLM Evaluation: Fine-tune or prompt-engineer LLMs for secure code generation, measuring vulnerability rates across models.
  4. Secure Coding Framework Development: Propose a workflow for secure AI code generation, including guidelines for model evaluation, human-in-the-loop review, and automated vulnerability mitigation.
  5. Validation: Compare vulnerability rates between baseline AI-generated code and code produced under the proposed secure coding framework.

Expected Contributions #

  • A comprehensive vulnerability taxonomy for AI-generated code.
  • A prototype vulnerability detection pipeline for AI-assisted development environments.
  • A set of best practices and technical guidelines for secure AI-driven code generation.
  • Contributions to Lovable’s platform by improving security-aware AI model behavior and developer tooling.

Collaboration and Data Access #

This research will be conducted in collaboration with Lovable, which will provide access to anonymized AI-generated code samples, model behavior logs, and developer feedback data. The collaboration ensures real-world relevance and practical validation of the proposed methods.

Tentative Timeline (5 to 9 Months) #

  1. Months 1–2: Literature review and dataset preparation.
  2. Months 3–4: Vulnerability analysis of existing AI-generated code.
  3. Months 5–6: Design and implementation of secure generation framework.
  4. Months 7–8: Evaluation and comparative testing.
  5. Month 9: Final analysis, reporting, and publication preparation.

Expected Outcome #

The project will yield a security-focused methodology for analyzing and improving AI-generated code quality. It will also enhance the Lovable Vibe Coding ecosystem by embedding proactive security mechanisms into its AI development pipeline, contributing to safer and more reliable software creation in the era of AI-driven coding.