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scthornton/securecode-v2.1
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SecureCode v2.1: framework-specific secure coding patterns, now on HuggingFace Quick update on the SecureCode dataset. After testing the v2.0 models against real codebases, one gap kept showing up: the models understood *what* was insecure but generated language-generic fixes. A developer using Express.js doesn't need "set security headers"they need `helmet()` middleware chains configured correctly. Spring Boot developers need `@PreAuthorize` annotations, not abstract RBAC pseudocode. What changed in v2.1: - 1,435 total examples (v2.0's 1,216 baseline + 219 new framework-specific additions) - 9 production frameworks: Express.js, Spring Boot, React, Next.js, FastAPI, GraphQL, SQLAlchemy, Flask, Vue.js - 475 unique CVEs (73 new, including framework-specific treatments of Log4Shell, Spring4Shell, and others) - 5-tier quality rubric: Every new example scores 90+/100 across correctness, new dataset average is nearly 97+, security hardening, real-world grounding, educational scaffolding, and production readiness - Structured references: CVE IDs, advisory URLs, discovery/remediation dates, affected versions — not just "related to CVE-XXXX" What stayed the same: - Same 4-turn conversation format (compatible with existing fine-tuning workflows) - Same license (CC BY-NC-SA 4.0) - Full v2.0 baseline included — no need to download both - All 8 fine-tuned models still work; v2.1-specific fine-tuning coming soon The new examples look like this: Instead of generic "use parameterized queries", you get Express.js with `express-validator` input chains, Spring Boot with `@Valid` bean validation + `BCryptPasswordEncoder`, FastAPI with `Depends()` auth injection and Pydantic model validation, React with `DOMPurify` + CSP headers. Framework-native patterns you can actually deploy. Two configs to load: ```python from datasets import load_dataset baseline = load_dataset("scthornton/securecode-v2.1", "v2.0-baseline") # 1,216 additions = load ```
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