Error code:   ConfigNamesError
Exception:    TypeError
Message:      list_() takes at least 1 positional argument (0 given)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
                  config_names = get_dataset_config_names(
                      path=dataset,
                      token=hf_token,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                      path,
                  ...<4 lines>...
                      **download_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1192, in dataset_module_factory
                  ).get_module()
                    ~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 622, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 396, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                                          ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2148, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                                       ~~~~~~~~~~~~~~~^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2144, in from_yaml_inner
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                                ~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2133, in from_yaml_inner
                  Value(obj["dtype"])
                  ~~~~~^^^^^^^^^^^^^^
                File "<string>", line 5, in __init__
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 553, in __post_init__
                  self.pa_type = string_to_arrow(self.dtype)
                                 ~~~~~~~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 157, in string_to_arrow
                  return pa.__dict__[datasets_dtype + "_"]()
                         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "pyarrow/types.pxi", line 4951, in pyarrow.lib.list_
              TypeError: list_() takes at least 1 positional argument (0 given)

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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}
}

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