SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models
arXiv:2512.18542v3 Announce Type: replace-cross Abstract: AI coding assistants produce vulnerable code in 45\% of security-relevant scenarios~\cite{veracode2025}, yet no public training dataset teaches both traditional web security and AI/ML-specific defenses in a format suitable for instruction tuning. We present SecureCode, a production-grade dataset of 2,185 multi-turn security training examples spanning two domains: web application security (1,435 examples covering the OWASP Top 10 2021 across 11 languages and 9 frameworks, 100\% grounded in documented CVEs and security incidents) and AI/M
The proliferation of AI coding assistants has exposed significant vulnerabilities, making purpose-built security datasets critical for their responsible development and deployment.
This dataset directly addresses a major limitation in current AI code generation, improving the security posture of future AI-generated software and mitigating substantial cyber risks.
The availability of SecureCode enables the training of more robust and security-aware AI coding models, shifting the paradigm from 'vulnerable by default' to 'secure by design' for AI-assisted code.
- · Cybersecurity firms
- · Developers using AI coding assistants
- · Organizations relying on AI-generated code
- · AI model developers
- · Malicious actors exploiting AI-generated vulnerabilities
- · Organizations with poor security practices
- · AI coding assistant developers who fail to integrate security training
AI coding assistants will significantly reduce the number of common vulnerabilities in newly generated code.
The cost of application security testing and remediation for AI-generated code may decrease as baseline security improves.
Enhanced security of AI-generated code could accelerate AI adoption in more sensitive and critical infrastructure sectors, increasing overall attack surface complexity.
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Read at arXiv cs.AI