# Ethical Considerations

AppSecBench intentionally contains **vulnerable code**. This document explains how the data is
constructed and the safeguards around its release.

## What the data is

- **Synthetic and minimal.** Every vulnerable snippet isolates one weakness in a few lines. It is
  not extracted from, and does not reproduce, any real application, product, or copyrighted source.
- **Non-weaponized.** Exploit sketches are illustrative proof-of-concept patterns (e.g. a sample
  malicious URL or payload shape), not turnkey exploits, tooling, or targeting information.
- **Educational framing.** Each record pairs the vulnerable code with secure code, an explanation,
  and references to authoritative guidance (OWASP, CWE, ASVS).

## Why release it

The security community needs open, reproducible benchmarks to (a) measure and improve LLM secure-
code capability, (b) benchmark SAST/secret-scanning/IaC-scanning tools on equal footing, and
(c) support defensive academic research. Withholding such data would cede evaluation to opaque,
closed benchmarks.

## Safeguards

- The dataset is MIT-licensed for **defensive** use (research, education, tool evaluation, training).
- AI/LLM cases (prompt injection, RAG, MCP, agent security) demonstrate *defensive* controls, not
  attack tooling.
- Infrastructure cases show misconfiguration **and** the corrected, hardened configuration side by
  side.
- The dataset does **not** contain working exploits against third parties, credential material
  beyond obvious placeholders (`sk_live_9f8a...`), or instructions for unauthorized access.

## Responsible use

- Do not use the vulnerable snippets to attack systems you are not authorized to test.
- When training models, ensure the secure-code pairs are presented with their explanations so the
  model learns *remediation*, not mere vulnerability replication.
- Cite the dataset and link to this document when publishing results.

## Contact

Report concerns via the repository's SECURITY.md disclosure process.