# Intended Uses

AppSecBench is built for **defensive security** research, evaluation, and education.

## In scope

- **LLM evaluation** — measuring a model's ability to detect, classify (CWE/OWASP), explain,
  score severity (CVSS), recommend fixes, and generate secure code for the covered weakness types.
- **SAST / scanner benchmarking** — comparing static-analysis, secret-scanning, and
  IaC-scanning tools on a common, versioned set of cases.
- **Secure-coding assistants** — fine-tuning or prompting models for secure code review and
  remediation (subject to the MIT license and the ethical-use note).
- **Academic research** — reproducible experiments in application security, AI security, and
  secure software engineering.
- **Curricula** — teaching secure-coding patterns via paired vulnerable/secure examples.

## Out of scope / prohibited

- Offensive operations against systems you are not authorized to test.
- Training models to *generate* exploits or attacks rather than defenses.
- Repackaging the dataset to violate the licenses of referenced standards or to misrepresent its
  provenance.
- Any use that breaks applicable law or the terms of the platforms where models/tools are deployed.

## Recommended evaluation protocol

1. Pick a split (`dataset/test.jsonl` for held-out eval).
2. Run your model/tool; collect outputs keyed by `benchmark_id`.
3. Score with `scripts/evaluate.py` (or your own judge using the weighted rubric).
4. Report overall and per-language / per-vulnerability / per-difficulty breakdowns, the dataset
   version, and the commit hash.

See `docs/methodology.md` and `docs/LIMITATIONS.md`.