Constitutional Spec-Driven Development: Enforcing Security by Construction in AI-Assisted Code Generation
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Abstract (arXiv)
The proliferation of AI-assisted "vibe coding" enables rapid software development but introduces significant security risks, as Large Language Models (LLMs) prioritize functional correctness over security. We present Constitutional Spec-Driven Development, a methodology that embeds non-negotiable security principles into the specification layer, ensuring AI-generated code adheres to security requirements by construction rather than inspection. Our approach introduces a Constitution: a versioned, machine-readable document encoding security constraints derived from Common Weakness Enumeration (CWE)/MITRE Top 25 vulnerabilities and regulatory frameworks. We demonstrate the methodology through a banking microservices application, selected as a representative example domain due to its stringent regulatory and security requirements, implementing customer management, account operations, and transaction processing. The methodology itself is domain-agnostic. The implementation addresses 10 critical CWE vulnerabilities through constitutional constraints with full traceability from principles to code locations. Our case study shows that constitutional constraints reduce security defects by 73% compared to unconstrained AI generation while maintaining developer velocity. We contribute a formal framework for constitutional security, a complete development methodology, and empirical evidence that proactive security specification outperforms reactive security verification in AI-assisted development workflows.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 31 Jan 2026.
Builds on
6 of 15 references.
References · 6
- 2021Evaluating Large Language Models Trained on Codecited by 8,205
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,011
- 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 427
- 2022Do Users Write More Insecure Code with AI Assistants?cited by 190
- 2026Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scalecited by 101
- 2025Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Taskscited by 20
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Citing papers per year: 2026: 1.
1 paper.