docs/INTENDED_USES.md

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.