Open_Ultra_SAST/benchmarks/pairs

Vuln vs fixed pair corpus

This corpus measures harness efficiency, not cheat-sheet recall.

The stage-1 smoke gate (python -m openultrasast.gate) scans one tree that already contains the planted sinks. A pair eval instead scans two isolated trees:

1. vuln — only the vulnerable file. The harness should fire (true positive).
2. fixed — only the patched file, same relative path. The harness should stay silent on that CWE (true negative).

pair_correct = detected on vuln and silent on the fix. That is the signal the improvement loop consumes (miss on the vuln side, fp on the fix side).

Layout

Path	Role
catalog.toml	Runnable offline pairs (local fixtures + vendored GitHub functions)
datasets.toml	Pointers to public VFC datasets — do not vendor the dumps
datasets/cwe-bench-java-project_info.csv	120 vetted Java CVE buggy/fix commits
github/	Isolated function excerpts with provenance headers

Pairs in this directory must not join LANGUAGE_MANIFESTS.

Slices

• local — in-tree fixture counterparts (app.py vs safe.py, …). CI gate: every local pair must pass (python -m openultrasast.pair_gate).
• github — real commit/function pairs (SVEN, CWE-Bench-Java, NVD patterns). Measured, not a 90% smoke gate. Misses here are the honesty / evolve-the-harness dashboard.
• sast — OWASP Benchmark Java/Python true-vs-false files and Juliet bad-vs-good sinks (benchmarks/pairs/sast/). Score is Youden (TPR − FPR). Honesty dashboard, not a CI fail.
• vfc — reviewed isolated functions from OpenSSL, Firefox, Chromium, and curl (benchmarks/pairs/vfc/). Overlay vs inventory scoreboard. Honesty dashboard, not a CI fail. Training labels (vuln vs fixed) are vfc/training/manifest.jsonl (vuln vs fixed, one snapshot per line). Grow with a blobless git log --grep=CVE-202 mine plus vfc/harvest.py, then vfc/generate_catalog.py (maintainer, not CI).
• vibe-py — Real-Vuln-Benchmark functions (benchmarks/pairs/vibe-py/): Python web repos pinned by commit, one vulnerable function per pair with a false-positive trap from the same repo as the fixed twin. Today only the 15 licensed human-authored repos are vendored (provenance = "human"); the 40 LLM-generated repos carry no license file and are excluded until one is stated upstream. Educational/CTF apps, not production. Honesty dashboard.
• vfc-js — server-side JavaScript function pairs harvested from the upstream fix commits that SecBench.js records (benchmarks/pairs/vfc-js/). Command injection, path traversal, and code injection first; prototype pollution and ReDoS carry their mechanism and may be known_limit. No SecBench.js file is vendored. Honesty dashboard.
• agent-vfc — security-fix commits carrying an AI agent co-author trailer or generation marker, in JavaScript, TypeScript, or Python (benchmarks/pairs/agent-vfc/). provenance = "agent". Rows with reviewer = "pending" load at review_tier = "title" (the review queue) and never gate the improve loop (task 8.2); a human sets reviewer to promote a row to reviewed. Honesty dashboard.

Review tiers

Every pair carries review_tier (Req 9): seeded (ground truth reviewed by the upstream benchmark), advisory (public CVE or GHSA with a fix commit), title (mechanism inferred from a commit message, unreviewed), reviewed (a named reviewer in this repository; the loader rejects reviewed without one). Defaults per slice: local reviewed, vibe-py seeded, sast/vfc/vfc-js/github advisory, agent-vfc title. pairs --json reports per_tier; evolve.evaluate_profiles scores only seeded and reviewed pairs, so advisory and title pairs never gate the improve loop (task 8.2).

Labels, profiles, and mechanisms

Every [[pair.expected]] row names a mechanism from the closed vocabulary in benchmarks/pairs/mechanisms.toml; every pair carries provenance (human|agent|mixed|synthetic), a declared split (train|holdout), and an optional known_limit reason. The scorer counts overlay coverage records with a source as detections and as leaks, matches by function when a label names one, never matches a CWE-only row (weak_label), and reports per_profile, per_mechanism, achievable (known-limit pairs excluded), and loss (parse-failed files, unadjudicated proposals). ousast pairs --profile agent --split holdout --json filters; --hunter adds the LLM hunter as a third scorer when a hunter model is configured. The improve loop rejects a change that regresses any profile on the holdout split.

Harvest for every slice goes through benchmarks/pairs/harvest.py --slice <name> (modes name, line_range, hunk, enclosing; comment and string mentions never anchor) and benchmarks/pairs/catalog_gen.py --slice <name> regenerates the catalog from recipes.toml. Maintainer only; CI never fetches.

Commands

uv run ousast pairs
uv run ousast pairs --slice github --json
uv run ousast pairs --slice vfc --json
uv run python -m openultrasast.pair_gate

Growing the corpus

Prefer function-level before/after from a reviewed source (SVEN jsonl, a CWE-Bench-Java fix_info.csv row, a GHSA with a tiny patch). Isolate the changed function into vuln / fixed files with the same relpath. Leave whole-repo clones to --fetch later; CI stays offline.