| --- |
| 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} |
| } |
| ``` |
|
|
