docs/methodology.md

# AppSecBench — Benchmark Methodology

## 1. Objective

Produce an original, reproducible, broadly-covered benchmark that measures whether an LLM or
security tool can **detect, classify, explain, score, and remediate** application-security
weaknesses across languages, frameworks, and difficulty levels.

## 2. Design Principles

- **Originality.** No record is copied from another dataset or from proprietary source. Each
  vulnerable/secure pair is authored from scratch, inspired by public secure-coding knowledge
  (OWASP Top 10 2021, OWASP API Security Top 10 2023, OWASP LLM Top 10 2025, CWE, ASVS).
- **Realism over trivia.** Every snippet reflects a realistic, idiomatic anti-pattern and its
  idiomatic fix for the target language/framework — not a toy example.
- **Reproducibility.** Generation is deterministic (`seed=42`). `scripts/build.py` yields
  byte-identical records on re-run.
- **Gradability.** Each record ships the "ground truth" a grader needs: CWE, OWASP (and
  API/LLM mappings where relevant), expected severity, a CVSS 3.1 vector + computed base score,
  an explanation, a fix description, secure code, and false-positive / false-negative priors.

## 3. Construction Pipeline

1. **Catalog** (`vuln_catalog.py`): 34 vulnerability archetypes × canonical CWE, OWASP 2021,
   OWASP API 2023, OWASP LLM 2025, category, and public references. Defines which languages/
   frameworks are realistic per archetype (e.g. infra vulns → IaC; AI vulns → Python/JS/TS).
2. **Generators** (`generators.py`): per-(vulnerability × language) functions emit a distinct
   vulnerable snippet, a distinct secure snippet, and an exploit sketch. Variation pools
   (endpoints, params, tables, functions) + a seeded RNG make each record's identifiers unique,
   so no two records are duplicates while sharing a carefully authored secure pattern.
3. **CVSS** (`cvss.py`): implements the official FIRST CVSS v3.1 base-score formulas. Each record
   gets a mathematically consistent vector + score; `expected_cvss_score` is recomputed during
   validation to guarantee consistency.
4. **Build** (`build.py`): enumerates realistic (vuln × language × framework × difficulty)
   combinations, samples a bounded number per (vuln × difficulty), assigns sequential
   `ASB-000001` IDs, injects CVSS, and splits into train/validation/test with a fixed seed.
5. **Validate** (`validate.py`): full QA suite (see §4).
6. **Statistics** (`statistics.py`): distributions as CSV/JSON/Markdown.

## 4. Validation & QA

`validate.py` enforces, per record:

- JSON validity and **no duplicate `benchmark_id`**.
- All 33 required fields present and non-empty.
- Enums: `language`, `framework`, `expected_severity`, `difficulty`, `expected_confidence`,
  `source_type`.
- `expected_cwe` matches `^CWE-\d+$`; `expected_owasp` matches `^A\d{2}:2021$`; API/LLM mappings
  format-checked when present.
- `expected_cvss` matches `^CVSS:3.1/...$` and its **recomputed** base score equals
  `expected_cvss_score` (±0.05).
- Reference URLs are well-formed `http(s)` links.
- `tags` is a non-empty list; `vulnerable_code != secure_code`.
- `expected_cwe` / `expected_owasp` equal the catalog values for that `vulnerability_name`
  (label consistency).
- `metadata` is internally consistent (cwe, cvss_vector, cvss_score).
- **Syntax checks**: Python (`compile`), JavaScript (`node --check`), TypeScript
  (`ts.transpileModule`), Go (`go build` with common stdlib imports), YAML (`yaml.safe_load`),
  Bash (`bash -n`); other languages use delimiter-balance heuristics and are reported as
  "unchecked" rather than assumed valid.

v1.0.0 result: **PASS — 406 records, 0 errors.**

## 5. Difficulty Levels

- **Beginner** — single obvious sink, direct taint flow.
- **Intermediate** — framework-specific idiom, slightly indirect flow.
- **Advanced** — multiple steps / missing cross-cutting control (authz, crypto).
- **Expert** — subtle, context-dependent weakness.
- **Real-world enterprise** — multi-component, requires foothold/user interaction; CVSS nudged
  to reflect PR/UI preconditions.

## 6. Scoring Rubric

Weighted, max 100 (see `evaluation/rubrics/rubric_v1.json` and the per-record
`evaluation_rubric`): detection 15, CWE 10, OWASP 10, severity 10, exploit 10, fix 20, code
quality 10, explanation 10, false-positive avoidance 5. Passing threshold recommended at 70.

## 7. Reproducing

```bash
python scripts/build.py && python scripts/validate.py && python scripts/statistics.py
```

## 8. Versioning

Semantic Versioning. v1.0.0 is the initial public release. See `CHANGELOG.md`.