remove README temporarily

README.md

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----
-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.
-
-## Data Files
-
-```
-dataset/
-  train.jsonl       (434 records)
-  validation.jsonl  (69 records)
-  test.jsonl        (69 records)
-```
-
-## 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}
-}
-```
-
-<!-- last updated: 2026-09-10T08:49:49Z -->