This is the deciding experiment of the strategy review, fixed before any v2 pin is
hunted.
- Hunter: the unchanged tool hunter (default prompt, tools and 4 steps) on
deepseek-flash.
- Scope: the ranker's top 20 regions for the case family, in four hunts. The model
never sees the fix diff or the declared sites.
- Families: a fixed keyword table maps the model's family to the protocol's three.
- Scoring: exactly protocol v2's scorer.
- Cost: a hard $10 ceiling.
hunt.py runs it and scores it (--score). A pilot on a v1 development case
(mongo-express) found the vulnerability at lib/bson.js:60, at $0.031 per pin,
alongside a lot of speculative noise.
Transport fix found by the pilot: the DeepSeek endpoint the hunter builds was
unpriced, so the cost meter read $0.00 for any number of calls. It is now priced.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Every model call has failed since 2026-09-10 (83ab533):
- The default detector and judge model was `deepseek-v4.1-flash`, the product name.
The API rejects it with HTTP 400 ("The supported API model names are
deepseek-flash, deepseek-v4-pro"). The judge and pipeline record an endpoint
failure as a degradation, never a verdict, so nothing failed loudly.
- `tool_hunter.resolve_hunter_client` resolved OpenRouter only. In this project
DeepSeek is the chat provider and the OpenRouter key is scoped to embeddings, so
the hunter could not reach a chat model at all; the call came back 401.
The defaults are now `deepseek-flash` (priced). The hunter resolves DeepSeek first
when its key is set, and OpenRouter stays as the explicit or fallback route.
Verified live: plain completion and a tool call through both the endpoint resolver
and the hunter client.
Not affected: the independent evaluations (finding_dump runs with
max_model_calls=0) and the 2026-09-06 hunter measurement, which predates the id.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Three corrections and one design requirement, from measurement and from
maintainer direction.
MEASURED. 4,463 PMPro regions carry five distinct ranks, and 3,742 of them --
83.8% -- share rank 0.30, ordered alphabetically by path. That tier spans
positions 159..3,900, and 341 of the 500 examined regions come from it: two
thirds of the budget spent on an arbitrary alphabetical slice. It is not a
weak ranker, it is barely a ranker.
CORRECTED. CVE-2023-23488's region is at position 390 of 4,463 -- INSIDE the
budget and examined. I stated repeatedly that it ranked ~1036 and was never
reached; that number came from before the array-callable and method-recogniser
fixes moved it. The reason it goes unreported is the taint query timing out,
so no region got a verdict at all. Ranking is a real problem; it is not this
miss, and conflating them would have sent the fix to the wrong place.
DIRECTION. Ranking is the LLM's job, not a tie-breaker for a residue. The
judgement "is this worth looking at" is a judgement about unfamiliar code.
Facts and generic features stay as cheap evidence for the model to read and as
the fallback when none is configured.
AND THE LOOP IS CLOSED, which is worse than train-on-test and was the sharper
point. A region the ranker excludes is never arbitrated, so it never produces
a label, so the ranker never learns it was wrong -- the feedback is censored by
the policy being trained, and every run confirms the ordering it already had.
Optimising on that makes it more confident, not more correct.
Breaking the loop is therefore a design requirement, with two cheap mechanisms
recorded: an exploration slice (~10% of budget sampled from outside the top-K,
the only labels the policy did not choose) and full labelling on repositories
small enough to arbitrate every region, which is policy-independent ground
truth. Inverse-propensity weighting is noted as available if the ranking is
made stochastic, and as unnecessary otherwise.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
99 source modules -> 81. 832 tests -> 646. The whole learning/ package (16
modules, ~4,100 lines) and six semantic/ modules removed, with the three gates
byte-identical to 2026-09-06-gate-baseline.txt at every step.
The sequence is what made it safe: vocabularies ported first (1.1), importers
detached second (1.2-1.3), deletion third (1.4). Every import break during the
deletion was therefore a detachment miss in the importer and was fixed there --
no module was restored to satisfy one. Both misses were the same shape and were
fixed as a class: operations.py and shapes.py each imported trailing_name from
the deleted variants.py, so it moved to semantic/ir.py, its natural home, and
both importers changed in one edit.
Ported before deleting: the closed guard vocabulary (verbatim, nothing else
holds it) into model/specs.py, which derives TaintSpec and DominanceSpec from
the retained ruleset facts rather than copying them. Re-homed: families ->
model/taxonomy, candidates -> model/candidates, endpoint -> model/endpoint,
plus Region, labeled_family and labeled_spans. labeled_family no longer falls
back to the LLM classifier -- an unlabeled pair is honestly unknown.
Four deviations, each recorded in the spec notes with its reason. improve/
evolve.py and validator.py were stripped, not deleted: each mixes the mechanism
lever (gone) with the deterministic rule-status loop `ousast improve` still
runs, which has no LLM in it; a test now asserts the lever's absence by symbol
so it cannot creep back. The learning CLI group became `model candidates`,
because deleting it outright would have orphaned the retained enumerator by
removing its only entry point. thinking moved from [learning] to [models].
A gap named rather than papered over: no retained sink fact routes by CWE to
path, output_encoding, untrusted_destination or prototype -- the same hole the
entailment ceiling diagnosed. The test asserts the seeded set and the gap set
exactly, so task 4.2 must update it when it authors those sinks.
The audit found exactly one genuine orphan: vectorstore.py, a bakeoff harness
with no production caller and no CLI, alive only through its own test. The five
named subsystems each carry an explicit decision. Manifest committed, with a
test asserting the tree still matches it.
Group 2 (the Joern go/no-go) has not started.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
