--- 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} } ```