Eight of the ten vendored access-control rows had no named handler at all,
so every one of them would have scored `missing_context` forever rather
than as a detector miss. After the re-harvest nine are named and the tenth
states its reason.
The handler-context mode now carries the class its framework registers,
multi-line decorators, and commented-out guards -- `#@token_required`
above a ThreatByte handler is the entire bug. Where the registration lives
in another document (connexion's `operationId`, an express router), a
recipe names `context = [...]`, the harvester writes that stanza per side,
and the pair materializes both files at their repository-relative paths so
the entry-point mapper sees the layout the project has. `harvest_recipe`
fetches every blob before the first write and the driver takes `--report`,
so a failed fetch leaves a reviewed excerpt exactly as it was.
Two defects the re-harvest exposed: `.mjs` was not a known suffix, so the
one pair vendored as an ES module had no file target at all; and
`_python_route_access` matched auth words inside decorator prose, reading
`@ns.doc(description='... without proper authorization checks')` as a
guard. Recorded in the hygiene artifact: nine of the ten duplicate groups
sit on both sides of the split, which the repository-level split cannot
see.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 3.1. A teacher is a vendored pair, at a review tier a maintainer stands
behind, on the train split, and scorable. Every learning path now asks the same
module that one question, so the split cannot be enforced in one place and
forgotten in another. A refusal is data rather than an exception: the path
records which holdout pairs a candidate touched and moves on, so a leak is
visible in the run instead of silently accepted.
The exporter refuses to seed from a holdout pair, leave-one-out teaches from the
train split while still scoring every pair, and the mechanism lever refuses a
candidate a holdout pair taught, because such a shape can only recover the pair
that produced it.
Measured and committed: on the vibe-py holdout the lever's own number falls from
seven detected and a Youden of +0.059 to five detected and -0.059 once holdout
pairs stop teaching, and the candidate set shrinks from ten shapes to six. That
gap is the inflation this rule removes.
Two existing tests encoded the leak and now encode the truth. The documented
export-to-improve flow had a single holdout pair teaching itself, and now needs
a train pair to teach and a holdout pair to recover. The obligation export
claimed the corpus seeds at least one obligation shape, which was true only
because holdout pairs were teaching: every vibe-py absence pair that yields an
obligation lesson sits on the holdout side, so the honest number today is zero.
The derivation stays covered by fixtures; the corpus gap belongs to the absence
re-harvest and to a maintainer pass over the splits.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 1.3. An expected row may carry a family label, stored opaquely because the
classifier owns the taxonomy and the corpus module never imports the learning
package. Every pair carries a computed reason when it cannot be scored: a
declared known limit wins, otherwise identical twin when the two excerpts are
the same code once the provenance header is stripped. No row is ever dropped.
Two corpus defects the new accounting surfaces, committed as an artifact beside
the numbers they cap. Nineteen repositories sit on both sides of the split, so a
learning path could be taught by one file of a repository and evaluated on
another. Ten groups of pairs share a vulnerable excerpt byte for byte, mostly
the same fix harvested twice at different granularity.
The split helper keeps every declared split, lets rows that named none inherit
their repository's side, and otherwise assigns a whole repository at once,
oldest first, so nothing straddles by our doing. Pair signals can now be
restricted to one split, which is what lets a learning path teach from train
rows only.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Measured 2026-09-06 on the vibe-py holdout: the existing tool hunter, with no
learning, scores 12/14 pair-correct (Youden +0.857, no leaks) once detection
and silence are keyed on the labeled class and function and the byte-identical
twins are excluded; the overlay scores 5/14 on the same rows. The published
hunter number (0/17, Youden -0.588) was a scoring artifact: the scorer wanted
the literal CWE id in the finding text and the prompt never asked for it.
The brief proposes an auto-classifier as the routing key, one evolved hunter
configuration per class, a class-aware scorer with unscorable denominators,
the split enforced by construction, and the HarnessX evolve loop with per-class
attribution as the round primitive. Raw measurements are committed under
reports/measurements.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 4.1. A new excerpt mode keeps the labeled handler with its decorators and
every statement outside it that registers the handler by name, so guards living
in decorators, routers and middleware travel with the pair. Comment and string
mentions never count.
The closed mechanism vocabulary gains the three obligation ids, catalog rows
accept an additive obligation field validated against the closed operation
kinds, and the pair scorer counts a finding tagged with that obligation inside
the labeled function as a detection. Rows without the label run the same code
as before.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 8.4 (Req 10.6, 5.1).
- harvest.merge_recipes appends recipes by name and never rewrites
existing rows.
- vibe-py/build_recipes.py --pointers: the 40 LLM-generated repositories
without a license become vendored = false rows (80 pairs, agent,
seeded) appended to recipes.toml; unlicensed human repos stay skipped.
- agent-vfc/build_recipes.py --from ... --pointers [--cache]: unlicensed
agent-authored fixes become title-tier pointer rows whose function
label is derived from the cached excerpt (30 added from a fresh
662-candidate search); TOML booleans rendered correctly.
- catalogs regenerated: vibe-py 115 rows (35 vendored + 80 pointer),
agent-vfc 59 (29 + 30); no excerpt is vendored for any pointer row.
- `pairs --slice vibe-py --pointers` scores both provenances (human 35,
agent 80); the default run skips 80 pairs with pointer_pair_skipped;
suite and gates unchanged. README/docs name the nightly command.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 8.3 (Req 10.1-10.5).
- pairs.py: catalog rows with vendored = false carry their harvest recipe
and no excerpts; evaluate_catalog harvests them into
OPENULTRASAST_PAIR_CACHE (default ~/.cache/openultrasast/pairs) only
when `pairs --pointers` or OPENULTRASAST_PAIRS_NETWORK=1 allows the
network, otherwise records pointer_pair_skipped (a failed fetch is
pointer_pair_fetch_failed with detail); a warm cache never scores
without the switch. select_vendored for tests and gates.
- harvest.py: materialize_pointer harvests into the cache without the
license requirement (nothing is redistributed); redaction applies.
- catalog_gen.py: emits pointer rows with recipe fields; training
manifests skip them.
- pair_gate and improve/evolve.evaluate_profiles score vendored pairs
only; cli `pairs --pointers`; .gitignore covers the cache path.
- tests: loader, skip/degradation, cache hit, fetch failure, warm cache
with network off, profile gate exclusion, harvest and generator paths;
conftest keeps every test offline whatever the shell exports.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Tasks 8.1, 7.1 (tests) and the re-scoped 5.3.
- pairs.py: review_tier in {seeded, advisory, title, reviewed} with
per-slice defaults (local reviewed, vibe-py seeded, sast/vfc/vfc-js/
github advisory, agent-vfc title); reviewer = "pending" means title, a
named reviewer means reviewed, reviewed without a reviewer is rejected;
per_tier metrics in the payload; select_tier and GATING_TIERS for the
improve loop.
- catalog_gen.py: stamps review_tier (and reviewer) on every generated
row and training manifest row; title rows enter catalog.toml; the
catalog-candidates.toml queue is retired; precedence matches the loader.
- agent-vfc: 29 title-tier rows load and score (0/29, Youden 0.0, the
honest unreviewed baseline); local rows name their reviewer.
- Tests: tier defaults, loader rejections, per_tier payload, the Req 8.1
catalog-on-disk / stage-1 exclusion proof and per-slice CLI checks.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Tasks 1.2, 2.1-2.5 and 6.2 (pair-side filters).
- pairs.py: coverage with a source counts as detection and as leak;
function-scoped matching by line-range containment from the vulnerable
side's parse (unresolvable labels are counted loss, never silent
misses); exact or alias sink matching; weak CWE-only labels never match;
side-by-side inventory/overlay/hunter scorers; per_profile,
per_mechanism, achievable, known_limit and loss payload; loader rejects
unknown mechanism/provenance/split and reserved or missing function
labels on harvested slices; candidate rulesets thread through for the
improve loop.
- overlay.py: OverlayRecord.function (additive) filled from the shared
naming helper in one parse; benchmark.py: ExpectedFinding.mechanism.
- cli pairs: --slice over every slice, --profile, --split, --hunter,
--hunter-model.
- catalogs migrated to the new schema (provenance, split, function,
mechanism, known_limit for owasp-java-hash); baseline recorded in the
spec notes.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Tasks 1.1, 5.1, 5.2, 5.4 and the data half of 5.3.
- mechanisms.toml: closed vocabulary v1 (20 ids) every expected row cites.
- vibe-py: 35 Real-Vuln-Benchmark pairs (Apache-2.0), human provenance only
because the LLM-generated repositories carry no license (escalated).
- vfc-js: 17 Node fix-commit pairs harvested upstream (MIT/Apache-2.0),
SecBench.js itself is not vendored (no license); sinks derived on 13/17.
- agent-vfc: 29 agent-trailer fixes as catalog-candidates.toml plus
REVIEW.md; the loaded catalog stays empty until a human reviews them.
- datasets.toml and README document every slice and its review status.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Tasks 3.3 and 4.1.
- vfc curl and openssl-cve-2021-3449 excerpts re-harvested through the
shared library: whole functions with upstream_start headers, the
vendored key redacted code-safely, no truncated bodies.
- vfc/recipes.toml carries mode, fix_path and function anchors;
training/manifest.jsonl regenerated.
- Juliet CWE-121 strcpy pair gets its buffer declarations back so the
vulnerable side is a real overflow again.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Tasks 3.1, 3.2 (harvest library) and the Scorer-owned naming helper.
- semantic/functions.py: one source of function names for scorer, overlay
and catalog generator (declarator wins over parser name; JS/TS bare-param
arrows and undeclared methods are <anon>; C++ Class::method resolves to
its unqualified tail; Perl sub; statements never name).
- benchmarks/pairs/harvest.py: shared library with name / line_range /
hunk / enclosing modes, comment-blind masking (triple-quote aware),
upstream_start headers, code-safe quoted-only redaction.
- benchmarks/pairs/catalog_gen.py: derive function labels and sinks from
the declarator at the mapped sink location, CWE-family sink tokens,
--prune with refusal when nothing derived, fails loud without the
package; vfc/harvest.py and vfc/generate_catalog.py become shims.
- tests/test_pair_harvest.py replaces test_vfc_harvest.py.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Harvest 176 isolated vuln-vs-fix functions (109 CVEs, 352 training rows)
into benchmarks/pairs/vfc with provenance and a JSONL training manifest.
Score the slice as an honesty dashboard (ousast pairs --slice vfc);
keep the pair gate on the local fixtures only.
Regex inventory stays the proposer. MAP attaches promote/demote/unadjudicated/coverage
from intra-file taint (tree-sitter when a grammar parses, Python ast as last resort).
Deep sandboxes promotions only, ordered by the existing heuristic ranker plus optional
OpenRouter mechanism recall. The SAST pair slice scores overlay outcomes; the stage-1
detection gate and local pair gate stay inventory-only.
Rebuild the quick-scan detection engine and ground-truth benchmarks to hit
>=90% recall / <10% false positives for C/C++, Python, JavaScript, and Java.
Engine (findings.py, preprocess.py):
- Scope each PatternRule by language (fixes cross-language false positives such
as the Python eval/exec rule firing on JS .exec()).
- Add JavaScript/TypeScript rules (command exec, eval/Function, reflected XSS,
SQLi, path traversal, SSRF, weak hash, deserialization); execFile/spawn-argv
excluded as safe alternatives.
- Add Java rules (Runtime exec, SQL string concatenation, weak hash, readObject)
and a Groovy server-side template rule (yieldUnescaped of a bound variable).
- Broaden C/C++ and Python coverage; precision guards for format strings and
parameterised vs concatenated SQL; #-comment skipping; CWE in rationale.
- Register .java/.groovy/.tpl source extensions.
Benchmarks:
- Add realistic corpora modeled on Damn Vulnerable C/C++, PyGoat/DVPWA, and
NodeGoat, plus the real kiview/damn-vulnerable-spring-boot-app vendored
verbatim (MIT, attribution in LICENSE/NOTICE.md). Each manifest carries
complete, independently-labeled ground truth including genuinely
hard-to-detect vulns (integer overflow, second-order SQLi, prototype
pollution) so recall is measured honestly, plus safe-API files for precision.
- Fix pre-existing smoke-manifest ground-truth errors (Python/JS line offsets,
missing C/C++ strcpy entry, Java SQLi line).
benchmark.py:
- Fix sink matching to normalise the expected sink consistently so sinks with
underscores (e.g. render_template_string) can match.
Tests:
- Add tests/test_detection_benchmarks.py enforcing the >=90% recall / <10% FP
goal per language and overall.
Measured: C/C++ 93.3%, Python 93.3%, JavaScript 92.3%, Java 100%;
overall 93.6% recall, 0.0% false-positive rate.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
