Open_Ultra_SAST/.kiro/specs/model-grounded-detection/tasks.md

Implementation Plan: model-grounded-detection

Guidance for the implementing agent

Read this before starting. It exists so you never have to reverse-engineer a decision this spec already made.

• The failure mode to avoid. The prior work logged eighteen defects, three of them the same shape: a fix applied to one instance while the class of the bug stayed. When you fix a retry, a key, an error path — ask "where else is this shape?" and fix all of them in the one change. That single discipline prevents most of the escalations that happened here.
• The gate baseline is sacred and is your safety net. benchmarks/measurements/2026-09-06-gate-baseline.txt holds the byte-exact output of python -m openultrasast.{gate,map_gate,pair_gate}. Run all three before and after any change and diff against it. If a gate moves, you changed something that was never supposed to change — stop and find it. The whole learning/ package is absent from the gate path (the gates import only pairs, benchmark, findings, mapping, preprocess, rank, complexity.map), so its deletion cannot move a gate; a moved gate means you touched a retained path by mistake.
• RED first, always. Write the failing test, watch it fail for the right reason, then implement. One commit per task with selective git add. Push when a group's independent review passes.
• Zero-dependency core. pyproject.toml dependencies = [] never changes. Joern, the LLM endpoint and the execution tier are capability-detected, never imported at module load. Copy the exact pattern in semantic/extra.py::has_semantic_extra and sandbox/probe.py::resolve_sandbox_probe — probe, don't import.
• Redaction on every prompt and persisted artifact (redaction.redact_secrets). Non-negotiable.
• Two phases can end the design cheaply. Phase 1 (subtraction) is reversible via git and gate-protected. Phase 2 (the Joern spike) is a go/no-go: if Joern cannot produce a deterministic verdict on the injection family at acceptable cost, the design stops there, exactly as the candidate ceiling could have stopped the prior spec. Do not build groups 3–6 until phase 2 is green.

The coupling map (for the subtraction, group 1)

These are every touchpoint of the code being removed. Handle all of them in group 1; do not discover them by running into import errors.

• learning/ package — imported by: cli.py (the learning subcommand group + _run_family_detectors
  • _learning* helpers + from .learning.families import FamilyTaxonomy + from .learning.proposer import Proposer), pairs.py (the hunter-scoring path — _evaluate_hunter_pair, _family_metrics, _hunter_outcome, build_pair_signals, function-local imports of learning.{families,scoring,classify,rounds} at lines ~519, 1151, 1166, 1184), semantic/seed.py and semantic/loo.py (the teacher-rule import from learning.split), and learning/endpoint.py (retained — re-homed, not deleted).
• Mechanism/evolve loop — improve/evolve.py, improve/validator.py, semantic/mechanisms.py, semantic/variants.py, semantic/variant_search.py, semantic/prove_budget.py, semantic/loo.py, semantic/seed.py, and semantic/__init__.py (re-exports). cli.py has the --mechanism-candidates surface and _mechanisms_export.
• config.py — LearningConfig, _load_learning, and the learning field on ResolvedConfig.
• reports.py — the learning= keyword on the report writers and _append_learning; the SARIF family/detector/verifier properties (keep family, drop detector/verifier).
• pairs.py is gate-load-bearing via pair_gate (the overlay/inventory path, _evaluate_overlay_pair/_evaluate_inventory_pair). The hunter-scoring path is the learning coupling and is not on the gate path. Remove the hunter path and its learning imports; keep the overlay/inventory path untouched; confirm pair_gate byte-identical.

What is retained and re-homed (do not delete)

learning/families.py → model/taxonomy.py; learning/candidates.py → model/candidates.py; learning/endpoint.py → model/endpoint.py. semantic/obligations/ stays (its facts feed the dominance queries in group 4). The gates, redaction.py, pairs.py overlay/inventory path — unchanged.

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Group 1 — Subtract and audit (gate-protected, no Joern yet)

• 1.1 Port the security vocabularies out before deleting their modules

  • Create src/openultrasast/model/specs.py and move into it, as data, the knowledge the deleted modules carry: _GUARD_PATTERNS and the guard-kind vocabulary from semantic/variants.py; the sink token tables (from benchmarks/pairs/catalog_gen.py::_SINK_TOKENS and semantic/facts.py SinkFacts) as the seed TaintSpec source/sink/sanitizer lists per family; a reference to the obligation facts (semantic/obligations/facts.py) for the DominanceSpec operations/dischargers. This is data movement, not logic — the CPG queries that consume it come in groups 2 and 4.
  • Observable: model/specs.py imports nothing from learning/, improve/, semantic/variants, semantic/mechanisms; a test asserts every family in the taxonomy has a TaintSpec or a DominanceSpec seed, and that the guard vocabulary is preserved verbatim. model/specs.py is read-only data — no LLM and no future optimiser writes it, and a test asserts the module exposes no writer (Req 7.4, the verifier boundary).
  • Requirements: 1.2, 7.1, 7.2, 7.3, 7.4

• 1.2 Re-home the three retained modules

  • Move learning/families.py → model/taxonomy.py, learning/candidates.py → model/candidates.py, learning/endpoint.py → model/endpoint.py. Update their imports and every importer. candidates.py keeps its behaviour exactly (it is the suspicion-band feeder, Req 8.4) — its tests move with it and stay green.
  • Observable: the moved modules import no other learning/ module; test_candidate_enumeration.py passes unchanged against the new path; the taxonomy loads and every family resolves.
  • Requirements: 4.4, 7.1
  • Depends: 1.1

• 1.3 Detach the retained paths from the removed code

  • pairs.py: remove the hunter-scoring path (_evaluate_hunter_pair, _hunter_outcome, _family_metrics, PairEvalResult.per_family, build_pair_signals split arg, the k_runs/hunter plumbing) and its learning.* imports; keep _evaluate_overlay_pair/_evaluate_inventory_pair and the context_files machinery. cli.py: remove the learning subcommand group, _run_family_detectors, the _learning* helpers, the --mechanism-candidates surface and _mechanisms_export; keep scan, benchmark, improve (rule-status only), pairs (overlay), mcp/index for now (audited in 1.6). config.py: remove LearningConfig/_load_learning. reports.py: drop the learning= kwarg, _append_learning, and the SARIF detector/verifier properties (keep family). semantic/{seed,loo}.py: remove the teacher-rule import from learning.split.
  • Observable: python -m compileall src clean; the three gates byte-identical to the baseline; the full suite still collects (failures expected only in tests that pin removed behaviour, addressed in 1.5).
  • Requirements: 1.3, 1.4, 11.2
  • Depends: 1.2

• 1.4 Remove the noise architecture and the mechanism/evolve loop

  • Delete learning/{rounds,acceptance,proposer,journal,canaries,verifiers,difficulty,scoring,classify, detectors,split,publish,slices,predicates,judgments}.py (everything under learning/ except the three modules re-homed in 1.2, which are already gone from the directory), then remove the now-empty learning/ package. Delete improve/evolve.py, improve/validator.py (mechanism parts — keep rule-status validation if improve is retained by the 1.6 audit), semantic/{mechanisms,variants,variant_search,prove_budget, loo,seed}.py, and their re-exports in semantic/__init__.py.
  • Guardrail: run this after 1.1–1.3, so every vocabulary is ported and every importer detached. If a delete breaks an import, the fix is in the importer (a 1.3 miss), never a re-addition of the module.
  • Observable: an import-graph check (a test that walks ast over every src/**/*.py and asserts no import names a removed module) passes; compileall clean; three gates byte-identical; the extra-free matrix and the full matrix both collect.
  • Requirements: 1.1, 1.2, 1.2b, 1.3, 1.4
  • Depends: 1.3

• 1.5 Reduce the test suite to surviving behaviour

  • For every deleted module, delete the test files that existed only to pin it (test_learning_*, test_mechanism_*, test_obligation_* only where it tested the lever not the checker, test_pair_* hunter-scoring cases, etc.) in this same change. For every surviving test, confirm it maps to a surviving requirement of this spec or a still-shipping feature; delete any that map to nothing, and delete outright any test that asserts a shape "does nothing", greps --help, or passes with its capability absent.
  • Observable: the suite is smaller than 832 tests; a one-line-per-cluster note records the before/after count and the requirement each removed cluster served; the pinned floor (gate-baseline, redaction, per-slice honesty, test_candidate_enumeration) is green.
  • Requirements: 2.1, 2.2, 2.3, 2.4, 2.5
  • Depends: 1.4

• 1.6 The module audit manifest

  • Add model/audit.py that classifies every src/**/*.py as load_bearing (reachable from a gate, the scan CLI, or a surviving requirement), standalone_capability (its own CLI/users — e.g. mcp.py, skills.py), or orphaned (no importer, no entry, no requirement). Remove the orphans. Give fusion.py, mcp.py, skills.py, harness_ext.py, hunter_harness.py an explicit keep-or-remove line each with its reason. Commit benchmarks/measurements/2026-09-08-module-audit.json.
  • Observable: the manifest classifies every module; a test asserts no module classified orphaned remains in the tree; the five named subsystems each have a recorded decision.
  • Requirements: 1.5, 3.1, 3.2, 3.3, 3.4
  • Depends: 1.5

Group 2 — The Joern spike on one family (go/no-go)

• 2.1 The CpgBackend seam, out of process

  • src/openultrasast/cpg/capability.py::has_cpg() — Joern on PATH (probe joern-parse --help), the has_semantic_extra pattern exactly. cpg/backend.py: CpgResult (a built cpg.bin path + a run(query, params)->JSON callable); JoernBackend (subprocess: joern-parse <root> then joern --script cpg/queries/<name>.sc, parsing stdout JSON, every step timeout-bounded, fail-closed to None); NullBackend (available() False, build() None); resolve_cpg_backend(). Never import a JVM binding; the subprocess is the only Joern touch-point in the codebase.
  • Observable: with Joern absent, resolve_cpg_backend() is the NullBackend and build() returns None; a scripted fake backend (canned JSON) exercises the parsing without Joern; the seam has no import of any non-stdlib module at load time.
  • Requirements: 4.1, 4.2, 4.3, 4.5, 11.3
  • Depends: 1.6

• 2.2 The injection taint query and its verdict

  • cpg/queries/taint.sc — a CPGQL script parameterised by a TaintSpec (sources, sinks, sanitizers) that emits, per candidate, the JSON {sink, source, path, sanitized: bool}. model/taint.py::verdict(cpg, spec, candidate) → ENTAILED when a source→sink path exists with sanitized=false, CORROBORATED when a path exists but a sanitizer may apply, else None. Injection TaintSpec from model/specs.py.
  • Observable: on a fixture with os.system(request.args['x']) the verdict is ENTAILED; on the parameterised-query fixed twin it is not; the query script is deterministic.
  • Requirements: 5.1, 5.2, 6.1
  • Depends: 2.1, 1.1

• 2.3 Determinism, and the go/no-go reading

  • test_two_runs_over_one_cpg_give_an_identical_verdict_sequence. Run the injection taint verdict over the injection slice, regenerate the entailment ceiling for injection, and write the reading into the Implementation Notes: does Joern produce a deterministic verdict at acceptable per-target cost and time? If not, stop the design here and record why.
  • Observable: the determinism test passes repeatedly; the injection entailment number is committed against the 2.2%/multi-hop baseline; the notes state go or no-go.
  • Requirements: 6.4, 6.5
  • Depends: 2.2

Group 3 — The ladder and the judge

• 3.1 The evidence ladder as the verdict type

  • model/ladder.py: Rung enum (suspicion/model_corroborated/model_entailed/execution_confirmed), Verdict (rung, family, witness, contradiction). Findings carry the rung in markdown, SARIF and JSON.
  • Observable: a finding at each rung round-trips through every output format with its rung intact; nothing is reported above suspicion without a Verdict establishing it.
  • Requirements: 5.1, 5.3, 5.4
  • Depends: 2.3

• 3.2 The judge: one bounded question, checked against the model

  • model/judge.py::judge(candidate, cpg, spec, *, client, model) — if the model entails, report model_entailed with no LLM call; else ask the LLM one typed question (source expression / guard / family) with no tools; then check the answer against the CPG — contradiction drops the claim (records why), corroboration advances to model_corroborated, neither leaves suspicion. Reuse model/endpoint.py.
  • Observable: an entailed candidate makes zero LLM calls; a claim the CPG contradicts is dropped with the contradiction recorded; a claim the CPG supports advances; there is no rounds/acceptance/floor/evolve code left to import.
  • Requirements: 8.1, 8.2, 8.3, 8.4, 8.5, 11.4
  • Depends: 3.1

Group 4 — Absence and the rest of the taxonomy

• 4.1 Guard dominance for the absence families

  • cpg/queries/dominance.sc + model/dominance.py::verdict(cpg, spec, candidate) — ENTAILED when an obligated operation is dominated by no discharger while its siblings are, seeded from the obligation facts. This is the arbiter for the classes that never crash (access_control).
  • Observable: on the ThreatByte/VAmPI absence fixtures the vulnerable handler is ENTAILED (guard absent) and the fixed twin is not; a handler with no obligated operation yields no verdict.
  • Requirements: 6.2, 7.3
  • Depends: 3.2

• 4.2 The remaining TaintSpecs and the ceiling regeneration

  • Fill TaintSpecs for path, deserialization, untrusted_destination, output_encoding, prototype, and the config constant-abstraction arm (model/taint.py). Regenerate the full entailment ceiling per family and per slice and commit it; name where each family falls back to suspicion.
  • Observable: the entailment ceiling artifact carries every web/logic family with its entailed/corroborated split; the number is higher than the flat-IR 2.2% and the fallbacks are named.
  • Requirements: 6.3, 6.5, 7.2
  • Depends: 4.1

Group 5 — Corpus calibration and reporting

• 5.1 The corpus as the model's calibration set

  • model/calibrate.py — over the vulnerable-parent/fix pairs, run the model on both sides and record whether it distinguishes them; a pair it cannot split is a named model gap (with the reason), not a miss. Provide the CVE-history harvest seam (recipe → parent/fix, the fix diff read as oracle).
  • Observable: the calibration report lists model gaps by family with reasons; a pair the model splits is recorded as covered; per-slice with the real-world slice deciding.
  • Requirements: 9.1, 9.2, 9.4, 11.1
  • Depends: 4.2

• 5.2 Reporting: per-slice, overfitting gap, rung

  • model/report.py — per-slice numbers, the overfitting gap beside every headline, the evidence rung on every finding; no cross-slice aggregate without its per-slice rows. Re-home the surviving per-slice honesty discipline (it was in the deleted scoring/publish).
  • Observable: the report carries a row per slice and refuses a bare aggregate; the overfitting gap is published; every finding shows its rung.
  • Requirements: 9.3, 11.1
  • Depends: 5.1

Group 6 — The execution tier stub (deferred)

• 6.1 The execution_confirmed rung and the Clearwing adoption seam
  • Wire Rung.execution_confirmed as an optional escalation reached only where the model cannot decide (memory/C first), behind a capability probe for the Clearwing fork. Do not reimplement any sandbox machinery; the seam calls the adopted tool. Not required for any web/logic class at v1.
  • Observable: a memory-family finding the model leaves at suspicion is the only kind eligible for the execution tier; with Clearwing absent the rung is simply never reached and a reason is recorded; no web/logic path depends on it.
  • Requirements: 10.1, 10.2, 10.3
  • Depends: 5.2

Task-plan review notes

• Group 1 is the largest and the only one with no new capability — it is pure subtraction, ~5,000 lines removed, gate-protected at every step. It must land and pass an independent review before group 2 begins.
• Group 2 is a go/no-go. If task 2.3 reads no-go on Joern, groups 3–6 do not start; the model layer either finds another CPG engine or the design returns to the maintainer. Building past a red 2.3 is the exact mistake this whole spec was written to stop.
• (P) parallelism is deliberately absent: the subtraction is sequential by dependency, and the Joern spike gates everything after it. Do not fan out.

Implementation Notes — group 1 (2026-09-08)

Landed as one change. Gate baseline captured before the first edit and re-checked after every step; the three gates are byte-identical to 2026-09-06-gate-baseline.txt at the end, as Req 1.4 and 11.2 require.

What the subtraction cost and bought. 99 source modules -> 81; 832 tests -> 646 (642 passing, 4 skipped); the whole learning/ package (16 modules, ~4,100 lines) plus six semantic/ modules removed. mypy clean over 82 files, ruff clean, core dependencies = [] untouched.

Sequence that made it safe. Vocabularies ported first (1.1), importers detached second (1.2-1.3), deletion third (1.4). Every import break during 1.4 was therefore a detachment miss in the importer, fixed there — no module was ever restored to satisfy one. Two such misses appeared, both handled as a class rather than an instance: semantic/obligations/{operations,shapes}.py both imported trailing_name from the deleted variants.py, so the helper was re-homed to semantic/ir.py (its natural place — it is a pure helper over CallSite.name) and both importers updated in the same edit.

Deviations from the design, each recorded with its reason.

1. improve/evolve.py and improve/validator.py were stripped, not deleted. The design's Removed list named them whole, but each is a mix: the mechanism lever (removed) and the deterministic rule-status improve loop that ousast improve still runs from benchmark evidence. That loop has no LLM in it, predates the noise architecture, and is not what this feature supersedes; deleting the files would have taken a retained capability with them. test_subtraction_complete now asserts the lever's absence by symbol, so it cannot creep back.
2. The learning CLI group became model candidates. Deleting the group outright would have orphaned model/candidates.py — the retained suspicion-band enumerator — by removing its only entry point, and Req 3 would then have flagged it for deletion. The offline ceiling report survives as ousast model candidates.
3. thinking moved from [learning] to [models]. It is a model setting, and LearningConfig was removed; its coverage moved to test_model_endpoint rather than being dropped.
4. Three helpers were re-homed rather than lost: Region (promoted from _Region, since the detector module that owned the type is gone), labeled_family and labeled_spans (from the deleted rounds/scoring). labeled_family no longer falls back to the LLM classifier — an unlabeled pair is honestly unknown, the same discipline the evidence ladder applies to a claim the model cannot arbitrate.

A measured gap, named rather than papered over (Req 9.2). Task 1.1's observable asked that every family have a TaintSpec or DominanceSpec seed. It cannot today: the retained sink facts have no sink whose CWE routes to path, output_encoding, untrusted_destination or prototype — the same hole the entailment ceiling diagnosed ("closed literal sink table: misses per-family sinks"). Rather than weaken the test, it now asserts the seeded set and the gap set exactly, so neither can change silently, and task 4.2 must update it when it authors those sinks.

Test reduction (Req 2), by cluster. 34 files deleted whole: 21 test_learning_* (rounds, acceptance, scoring, classify, detectors, proposer, journal, canaries, verifiers, publish, split, cli, scan, pairs_path, cutoff, reliability, aggregation, classifier_report, pairs_evidence, round evidence x2, thinking), 4 test_mechanism_*, test_variants, test_variant_search, test_loo, test_obligation_export, test_mechanism_reports, test_fixture_difficulty. Partial trims where a file mixed retained and removed behaviour: test_semantic_pipeline (3 mechanism tests of 12), test_degradation_matrix (3 rows for removed capabilities), test_pair_corpus (the hunter scorer), test_obligation_check (the known_fix assertion, which depended on the deleted store — the id contract, suspicion rung and tags it also covered are kept), test_index (the bakeoff test). Two files re-homed with their subject: test_learning_families -> test_model_taxonomy, test_learning_endpoint -> test_model_endpoint.

The audit (Req 3) found exactly one genuine orphan: vectorstore.py, a store bakeoff harness with no production caller and no CLI entry, alive only through its own test — removed with it. The five named subsystems each carry a decision: mcp, skills, fusion, harness_ext, hunter_harness are all standalone_capability (own entry point or protocol), joined by stage_processors and slot_contract, which the zero-dependency guard in test_gate pins. Manifest committed at benchmarks/measurements/2026-09-08-module-audit.json; a test asserts the tree still matches it.

Group 2 (the Joern go/no-go) has not started. semantic/engines.py already exposes joern_available, which the CpgBackend capability probe in task 2.1 should build on rather than duplicate.

Implementation Notes — group 2 (2026-09-08)

Verdict: GO. Joern is installed at ~/joern/joern-cli (maintainer chose a permanent install). Artifact: benchmarks/measurements/2026-09-08-joern-go-no-go.json.

	injection slice
model_entailed on the vulnerable side	54% (27/50) — against the flat IR's 2.2%
... on minimal-fix pairs only	61.5% (16/26)
pairs distinguished (vuln entailed, fix clean)	32% overall, 38.5% on minimal-fix pairs
determinism (Req 6.4)	every verdict identical on re-run
cost	median 32s/pair, max 58s, 30 min for the slice
CPG build failures	1 (a C translation unit that does not parse standalone)

The engine is not the constraint. Every gap below is in our own data model, which is exactly the result that justifies adopting a CPG rather than building one. The 23 misses split three ways: 11 sinks not in the fact tables (Django ORM objects.raw, self.query, GraphQL resolvers), 11 where the sink is modelled but no source reaches it, and 1 build failure — counted separately so it can never read as "no vulnerability".

The finding that changes the design. Classifying what the 50 injection fixes actually do:

shape introduced at the sink              guard kind of the added lines
  other_or_validation   37                  none                24
  bound_parameters      10                  parameterized_call  11
  argv_list              2                  null_test            7
  sanitizer_call         1                  allowlist_test       5

The dominant injection fix is a validation guard on the path — allowlist, type coercion, bounds or null test — not a sanitizer call and not a sink-shape change. Taint reachability can never separate those pairs: the flow still reaches the sink; what changed is that a guard now dominates the path. So the arbiter is taint + dominance, not taint alone — the same primitive task 4.1 builds for access_control, with the guard vocabulary ported verbatim in task 1.1 as its lookup list. Two planned mechanisms collapse into one, and task 4.1 should be built before the remaining TaintSpecs rather than after.

Five bugs the live engine exposed, none visible offline. Two of them inverted a verdict rather than weakening it, which is why each was fixed as a class:

1. A sink was its own sanitizer — facts carrying a shape qualifier (parameterized, literal_format_arg) describe a safe form of the sink call, not a cleansing call. Flattened in, execute became both the injection sink and its own sanitizer and nothing could ever be entailed. Fixing the class immediately caught C's entire printf family.
2. labeled_family had lost its deterministic CWE fallback in group 1 (my error) — the injection slice resolved to zero pairs. Restored, still with no model call; the census again matches the committed ceiling exactly.
3. Taint alone entailed both sides of the canonical SQL pair, so the model arbitrated nothing → the safe-shape test, using the safe_shape_sinks that bug 1 had preserved.
4. Joern's call.argument includes the receiver, so a one-argument interpolated call looked like a two-argument bound one — the exact inversion of the shape test. Fixed with argumentIndexGt(0).
5. The source model was framework-only, while 39/50 pairs receive untrusted input as a function parameter. Parameter sources are now opt-in (right for a function-level pair whose boundary is the trust boundary, wrong for a whole repository) — and required for the comparison against the flat-IR baseline to be like-for-like, since that baseline counted them.

Corpus caveat, and why the differential is reported per stratum. Only 26 of 50 injection pairs are genuine minimal-fix pairs; 12 are different programs (local-python-vulnerable is 7% similar with 83 changed lines; owasp-java-cmdi pairs BenchmarkTest00007 against BenchmarkTest00090) and 12 are large rewrites. Where the fixed side is a different program, both sides flagging is a corpus property, not a model failure — a raw distinguished_rate would be measuring corpus quality and reporting it as model quality.

A correction to a claim made mid-run. The Flask-shaped source table (no request.POST/request.GET) is a real gap but a minor lever here: adding Django patterns would gain a source for exactly one pair in this slice. The larger levers are the missing sinks and guard dominance.

Next, in this order: 4.1 guard dominance over the tainted path; 4.2 the missing sinks (Django ORM, GraphQL, and the four families with no sink fact at all); then the two remaining safe-shape forms, argv-list and shell=False.

Implementation Notes — group 3 (2026-09-08)

3.1 — the rung is a new field beside evidence_level, not a rename of it. The existing evidence_level carries static_corroboration, emitted by the flat IR overlay. Req 4.4 demotes that path to the suspicion band, so restating its value as model_corroborated would claim a CPG arbitrated something it never saw, while rewriting every legacy finding to suspicion would be a product-visible regression nobody asked for. So StaticFinding.rung is added, defaults to suspicion, and is raised only by ladder.at_rung(finding, verdict) — which is the structural form of Req 5.4. The witness travels with the rung, because a rung without the evidence that justifies it is just a louder assertion. The rung reaches markdown, JSON (asdict, so automatically) and SARIF. evidence_level is now redundant in intent and should be retired once the model layer covers every path; that is a follow-up, not a silent change.

3.2 — the judge, and what it does not contain. Three outcomes, exhaustive: the model entails (report it, never call the model), the model contradicts (drop the claim, record why), the model cannot decide (report suspicion, honestly). The LLM is asked exactly one bounded, typed question, with tools=[] so it cannot search for the site, and only about the residual the graph cannot settle — whether a sanitizer on a real flow is sufficient. Its answer can only ever confirm a flow the CPG already found; it can never conjure one.

Req 8.5 is asserted structurally rather than by prose: a test parses judge.py and fails if the AST contains any name like k_runs, majority, vote, average, acceptance, floor or budget, or any for/while loop at all, or an import from the deleted round machinery. The first draft of that test matched the word "votes" inside the module's own docstring, which is the reason it now inspects code rather than text — the docstring is allowed to name the machinery it replaced.

Verified live against a real CPG, not only against scripted rows:

vuln   rung=model_entailed   witness=request.args["name"] -> db.execute("select * from users ...
fixed  rung=suspicion        contradiction: the model resolved no flow: no source reaches this sink
model calls made: 0

Both sides arbitrated by the graph with the LLM never consulted — which is the entire point of the arbiter, and the thing the prior architecture could not do at any price.

678 tests passing, mypy and ruff clean, three gates byte-identical, zero orphaned modules.