CITATION.bib

@dataset{securecode_v2_2025,
  author       = {Thornton, Scott},
  title        = {SecureCode v2: Production-Grade Security Vulnerability Training Dataset},
  year         = {2025},
  month        = {12},
  publisher    = {HuggingFace},
  url          = {https://huggingface.co/datasets/perfecXion/securecode-v2},
  note         = {2,418 examples covering 14+ vulnerability types including Authentication, Authorization, SQL Injection, XSS, SSRF, Cryptography, and AI/ML Security across 6 programming languages. 100\% language fidelity and SIEM coverage.},
  keywords     = {security, vulnerability detection, OWASP, SIEM, secure coding, machine learning, AI security, dataset},
  language     = {en},
  version      = {2.0}
}

@misc{securecode_v2_huggingface_2025,
  title        = {perfecXion/securecode-v2},
  author       = {Thornton, Scott},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/perfecXion/securecode-v2}},
  note         = {HuggingFace Datasets repository}
}

@misc{securecode_v2_github_2025,
  title        = {perfecXion/securecode-v2},
  author       = {Thornton, Scott},
  year         = {2025},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/perfecXion/securecode-v2}},
  note         = {GitHub repository}
}

@article{securecode_v2_technical_report_2025,
  title        = {SecureCode v2: Design and Implementation of a Production-Grade Security Vulnerability Training Dataset},
  author       = {Thornton, Scott},
  journal      = {Technical Report},
  year         = {2025},
  month        = {12},
  url          = {https://github.com/perfecXion/securecode-v2},
  abstract     = {We present SecureCode v2, a production-grade dataset designed for training large language models on security vulnerability detection and secure coding practices. The dataset contains 2,418 comprehensive examples covering 14+ vulnerability types including Authentication, Authorization, SQL Injection, XSS, SSRF, Command Injection, Cryptography, and AI/ML Security across 6 programming languages (JavaScript, Python, PHP, Java, Go, Ruby). Each example includes real-world breach scenarios with financial impact, vulnerable and secure code patterns, exploitation scenarios, comprehensive testing suites, SIEM detection rules (Splunk SPL + Elasticsearch Query DSL), and infrastructure hardening guides. The dataset achieves 100\% language fidelity, 100\% SIEM coverage, and 62.1\% CVE uniqueness, making it suitable for both ML model training and developer education.}
}