mlsanityData to 13 Oct 2026

Constitutional Spec-Driven Development: Enforcing Security by Construction in AI-Assisted Code Generation

arXiv 2602.02584 ↗First posted 31 Jan 2026cs.SE cs.AI cs.CRcited by 1

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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

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,205
  2. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,011
  3. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 427
  4. 2022Do Users Write More Insecure Code with AI Assistants?cited by 190
  5. 2026Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scalecited by 101
  6. 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.

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