README.md

---
dataset_info:
  features:
    - name: benchmark_id
      dtype: string
    - name: title
      dtype: string
    - name: category
      dtype: string
    - name: language
      dtype: string
    - name: framework
      dtype: string
    - name: application_type
      dtype: string
    - name: source_type
      dtype: string
    - name: vulnerability_name
      dtype: string
    - name: vulnerability_description
      dtype: string
    - name: vulnerable_code
      dtype: string
    - name: secure_code
      dtype: string
    - name: exploit_example
      dtype: string
    - name: exploitability_explanation
      dtype: string
    - name: attack_prerequisites
      dtype: string
    - name: expected_llm_analysis
      dtype: string
    - name: expected_detection
      dtype: string
    - name: expected_fix
      dtype: string
    - name: expected_secure_code
      dtype: string
    - name: expected_severity
      dtype: string
    - name: expected_confidence
      dtype: string
    - name: expected_cwe
      dtype: string
    - name: expected_owasp
      dtype: string
    - name: expected_owasp_api
      dtype: string
    - name: expected_owasp_llm
      dtype: string
    - name: expected_cvss
      dtype: string
    - name: expected_cvss_score
      dtype: float
    - name: expected_false_positive_probability
      dtype: float
    - name: expected_false_negative_probability
      dtype: float
    - name: evaluation_rubric
      dtype: dict
    - name: scoring_criteria
      dtype: list
    - name: tags
      dtype: list
    - name: references
      dtype: list
    - name: metadata
      dtype: dict
version: 1.1.0
splits:
  train:
    num_examples: 434
  validation:
    num_examples: 69
  test:
    num_examples: 69
  citation: >-
    @dataset{tasdelen2026appsecbench,
      title={AppSecBench: A Comprehensive Benchmark Dataset for Application Security Evaluation, Secure Code Review, AI Security Research, LLM Evaluation, and Secure Software Engineering},
      author={Taşdelen, İsmail},
      year={2026},
      version={1.1.0},
      publisher={Hugging Face}
    }
task_categories:
  - text-generation
  - question-answering
  - token-classification
  - other
language:
  - en
  - code
tags:
  - security
  - application-security
  - vulnerability-detection
  - secure-code-review
  - llm-evaluation
  - sast
  - owasp
  - cwe
  - ai-security
  - benchmark
size_categories:
  - 100-999
---

# AppSecBench Dataset Card

## Dataset Summary

AppSecBench is an original benchmark of 406 vulnerable/secure code pairs spanning 12 programming
languages, 18 frameworks, 34 vulnerability classes, and 5 difficulty levels. Each record is a
self-contained evaluation case: a vulnerable snippet, its secure counterpart, an exploit sketch,
and the "ground truth" a detector/model is expected to produce (CWE, OWASP, severity, CVSS 3.1,
explainability, fix, and false-positive/false-negative priors).

The dataset supports measuring whether an LLM or security tool can **detect**, **classify**,
**explain**, **score severity**, **recommend a fix**, and **generate secure code** for real-world
application-security weaknesses — including modern AI/LLM risks (prompt injection, RAG, MCP, agent
security) and infrastructure misconfigurations.

## Supported Tasks

| Task | Input | Expected output |
|------|-------|-----------------|
| Vulnerability detection | `vulnerable_code` | vulnerability flagged + location |
| CWE/OWASP mapping | code | `expected_cwe` / `expected_owasp` |
| Severity estimation | code | `expected_severity` + `expected_cvss_score` |
| Exploit explanation | code | `exploitability_explanation` |
| Secure fix / secure code gen | code | `expected_secure_code` |
| False-positive / false-negative analysis | code | `expected_false_positive_probability` / `expected_false_negative_probability` |

## Languages & Frameworks

Python, Java, JavaScript, TypeScript, Go, Rust, PHP, C#, Kotlin, Swift, C, C++ (code); plus
Infrastructure-as-Code in YAML / Dockerfile / Bash. Frameworks: Flask, FastAPI, Django, Spring
Boot, Express, NestJS, Next.js, Laravel, ASP.NET Core, Gin, Echo, Fiber, Android, iOS.

## Data Fields

Each record is a JSON object (see `README.md` for the field list). `metadata` carries
`difficulty`, `category`, `cwe`, `owasp`, `owasp_api`, `owasp_llm`, `cvss_vector`, `cvss_score`,
`source`, `license`, and `schema_version`.

## Distribution (v1.1.0)

- 17 languages, 18 frameworks, 27 unique CWEs, 9 unique OWASP Top-10 (2021) classes, 34 vulnerability types.
- Difficulty: Beginner, Intermediate, Advanced, Expert, Real-world enterprise.
- Source type: `synthetic` for all records (original, non-derived).
- Full per-dimension counts: `statistics/summary.json` and `statistics/statistics.md`.

## Methodology

Records are generated deterministically (`scripts/build.py`, `seed=42`) from an original catalog
(`scripts/vuln_catalog.py`) and per-language generators (`scripts/generators.py`). CVSS 3.1 base
scores are computed from the official FIRST formulas (`scripts/cvss.py`). See `docs/methodology.md`.

## Quality & Validation

An automated QA suite (`scripts/validate.py`) enforces: JSON validity, no duplicate IDs, required
field presence, enum conformance, CWE/OWASP/CVSS format + recomputation consistency, label
consistency vs the catalog, `vulnerable_code != secure_code`, reference well-formedness, and real
syntax/compile checks. Result for v1.1.0: **PASS (0 errors)** over 572 records. Report: `validation/validation_report.md`.

## Intended Uses

- Evaluating and comparing LLMs on secure-code understanding.
- Benchmarking SAST / SCA / secret-scanning / IaC-scanning tools.
- Training and fine-tuning secure-coding assistants (with proper licensing).
- Academic reproducible experiments in application security.

## Limitations & Out-of-Scope

- Snippets are minimal/synthetic, not full applications; they isolate one weakness at a time.
- Some languages are checked with heuristic balance (not compiled) when no toolchain is present.
- The benchmark measures *recognition/explanation*, not end-to-end offensive capability.
- Not a substitute for manual security review or threat modeling.

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

## Ethical Considerations & Responsible Disclosure

`docs/ETHICAL_CONSIDERATIONS.md` and `docs/RESPONSIBLE_DISCLOSURE.md`. The vulnerable code is
educational, synthetic, and non-weaponized.

## Licensing

MIT. Code snippets are original and provided for defensive use.

## Citation (BibTeX)

```bibtex
@dataset{tasdelen2026appsecbench,
  title  = {AppSecBench: A Comprehensive Benchmark Dataset for Application Security Evaluation, Secure Code Review, AI Security Research, LLM Evaluation, and Secure Software Engineering},
  author = {Taşdelen, İsmail},
  year   = {2026},
  version= {1.1.0},
  publisher = {Hugging Face}
}
```