AppSecBench / docs /methodology.md
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# 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`.