Datasets:

ismailtasdelen
/

AppSecBench

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)

@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}
}