SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models
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Computer Science > Cryptography and Security
arXiv:2512.18542 (cs)
[Submitted on 20 Dec 2025 (v1), last revised 7 Jul 2026 (this version, v3)]
Title:SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models
Authors:Scott Thornton
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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/ML security (750 examples covering all 10 OWASP LLM Top 10 2025 categories across more than 40 frameworks, including LangChain, OpenAI, and Hugging Face). Every example follows a 4-turn conversational structure -- feature request; vulnerable and secure implementations with attack demonstrations; advanced probing; and defense-in-depth operational guidance -- designed for direct use in instruction tuning pipelines.
Quality assurance combines automated structural validation with multi-agent review from seven specialist AI perspectives (more than 10{,}500 assessments) and an 8-phase remediation pipeline, producing a rubric-calibrated mean quality score of 93.8/100 ($\sigma = 0.93$) for the AI/ML component. Each example provides SIEM integration strategies, infrastructure hardening recommendations, and testing approaches using production frameworks. We release the unified dataset on Hugging Face with domain-specific loading configurations (web, aiml, default), alongside eight fine-tuned open-source models (3B--20B parameters, QLoRA), and an evaluation framework with four security-specific metrics. To our knowledge, SecureCode is the first public dataset that jointly provides OWASP Top 10 2021 web coverage and OWASP LLM Top 10 2025 AI/ML coverage in a unified conversational schema suitable for instruction tuning.
| Comments: | 30 pages, 12 figures, 10 tables. Dataset available at this https URL. Code and validation tools at this https URL |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2512.18542 [cs.CR] |
| (or arXiv:2512.18542v3 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2512.18542
arXiv-issued DOI via DataCite
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Submission history
From: Scott Thornton [view email][v1] Sat, 20 Dec 2025 23:52:12 UTC (16,453 KB)
[v2] Tue, 10 Feb 2026 21:29:07 UTC (351 KB)
[v3] Tue, 7 Jul 2026 00:01:21 UTC (359 KB)
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