`model-grounded-detection` finished 16/16 with the model layer having zero
production callers. This closes that. A real scan of a Flask file now reports:
by_rung: {"model_entailed": 3, "model_corroborated": 0, "suspicion": 0}
model:injection:ping:10 <- os.system("ping -c 1 " + request.args["host"])
The stage is the fifth MAP proposer, mirroring _check_obligations, and is
additive: pattern, overlay and obligation findings all keep working, and without
a CPG engine it records cpg_unavailable and changes nothing else. Two scan
integrity tests pin the exact degradation set and both broke -- correctly. Their
`requested == baseline` assertion, that findings are byte-identical, still
passed, so the only change is that the scan now SAYS the arbiter was absent
instead of being silently without one. The guards are updated to assert the
model degradation specifically rather than being loosened.
ModelLayerConfig, not ModelConfig: that name is already the [models] block, and
the type checker caught the collision immediately. Two meanings of one name is
how a scan silently reads the wrong settings.
Every payload carries the same shape, skipped ones included. A payload that is
sometimes a full record and sometimes a single "skipped" key is how a report
ends up quietly omitting a section.
unjudged_sample names the lowest-ranked regions the budget did not reach.
Counting them is the honesty requirement; naming them is what lets a maintainer
write a large-repository exclusion list from evidence rather than guesswork.
The run also produced the finding behind new task 1.4: 275 seconds for a
ten-line file. cost_usd is 0.0, so those 21 invocations were not model calls --
they were Joern query launches, one per region per family, each a JVM start.
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
records whether its decision beat noise
Two things the maintainer was right about.
Thinking was disabled so `temperature: 0` would not be silently ignored --
a trade that only pays if it buys determinism. Round zero says it did not:
11 of 20 injection pairs disagreed with themselves across five runs, so the
variance is in the agentic path, not the decoder. We paid the reasoning cost
on a reasoning model and got nothing. It is now `[learning] thinking`,
recorded in the artifact and part of its key so the two modes cannot be read
as one number. The default stays off only because that is what round zero
was measured under.
And the deeper point: the spec built `sign_test` and `reliable_change` and
the acceptance rule calls neither -- `decide` compares raw deltas. On a
detector with that floor a raw delta is mostly noise, and a rule that
brittle is exactly what makes a deterministic decoder look necessary. It is
not; the alternative is an acceptance rule that models the score as noisy,
the way reflective-evolution and Bayesian prompt optimizers do. Changing
Req 9.5 is a maintainer's call, so this does not change the decision: every
round now journals the two-sided sign test over the pairs that moved, and
after four rounds the journal will say whether any decision could be told
from a coin flip.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 3.4. A standard scan now classifies each hotspot region and runs the
detectors of every admitted family plus the generalist, reading the
configurations round zero wrote. A scan never creates them: a missing directory
is a recorded reason and the stage is skipped, which is also what happens when
no chat endpoint resolves.
Every claim passes through its family's verifier and carries what produced it:
the family, the detector version and what the verifier said. Nothing rises above
suspicion here, because the verifier writes a tag and the evidence ladder
belongs to another boundary. Reports gain a section naming the three, SARIF
gains the matching properties, and the manifest gains a learning block with the
taxonomy version, the per-family counts, the classifier report and the round.
The feature is off unless a project turns it on, so an existing scan is
unchanged, and all three gate outputs stay byte-identical.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 1.2. Detectors, the classifier's model tier, the proposer and the judge
resolve one chat endpoint: an explicit override first, then the scripted client
flag, then an endpoint named in the config, then DeepSeek, then OpenRouter.
Prove-order retrieval keeps its own embedding endpoint untouched, because the
chat provider we default to offers none.
The adapter handles the provider's documented quirks: thinking is on by default
there and silently ignores temperature, so it is disabled explicitly; the
reasoning field is kept on the reply for the caller to replay across tool turns;
JSON mode has no schema and occasionally returns empty content, so one retry is
built in, never a loop; token log-probabilities are requested only when the
caller wants them, and their mean is what lets the classifier abstain.
Cost comes from the provider's own usage fields, cache hits priced separately,
and a model with no recorded price reports no cost rather than a made-up one.
The endpoint record deliberately holds no key, so it is safe to journal.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 3.1. The obligations proposer runs after variant search in standard and
deep scans, reads the declared policy from the target, and never writes it.
Its findings are appended after every other finding and never enter the sandbox
candidate set. The manifest block carries the sibling sets, the under-populated
count, the operations examined, the findings by label, the policy version, the
stage degradations, and the sibling sets themselves.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
- variant_search.search_tree skips a malformed store row with a
variants_store_row_invalid degradation instead of aborting the scan;
a hit on a demoted or flowless overlay record is never merged (test).
- seed.pair_lessons is the one shape derivation shared by the exporter
and leave-one-out; labels pass line and sink together; pairs without a
labeled function are reported as no_labeled_function.
- config parses [variants].max_mechanisms with the shared int helper.
- regress.candidate.hotspot_from_finding is the one zero-score hotspot
helper for forced inventory and variant findings.
- design synced (SearchResult, hits_to_findings summaries, candidate.py).
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Task 2.3 (Req 3.4, 3.5, 3.6, 6.2).
- [variants] enabled / max_mechanisms config; MAP runs variant search
after the overlay in standard and deep scans only, appends the variant
findings, rewrites overlay.json with merged ids, and the manifest
records mechanisms_searched, files_searched, findings and
merged_into_overlay.
- REGRESS: variant findings join the candidate list after the promotions
as zero-score hotspots, under the existing safety check and cap.
- Spec notes for group 2 and the design synced (variant_search.py,
seed.py, derive_shape signature).
Co-Authored-By: Claude Fable 5.1 <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
Stdlib parser only; pytest skips it so tests cannot pick up a developer key.
The embedding model is openai/text-embedding-3-small via OpenRouter /embeddings.
Add a [harnessx] config block so the agentic plane can be steered from
openultrasast.toml, and make the LLM provider selectable (the hunter and verifier
previously hardcoded AnthropicProvider).
- config.HarnessxConfig: provider ("anthropic"|"openai"|"litellm", default
anthropic), max_cost_usd (2.0), token_threshold (120000); parsed from [harnessx].
- harness_ext.build_provider: single lazy, capability-guarded provider factory.
The provider name is validated before any import, so an unknown choice fails with
a clear ValueError even when the extra is absent; each provider reads its own
standard key env var (openai -> OPENAI_API_KEY, anthropic -> ANTHROPIC_API_KEY).
- HxScanOrchestrator and verify_findings_dispatch take a provider and route through
build_provider; cli threads config.harnessx.{provider,max_cost_usd,token_threshold}
into both the hunter and the verifier.
- README: "Configuring the HarnessX agentic plane" section (install, models,
provider, budgets, fallback/degradation).
Test isolation: convert the "harnessx not in sys.modules" import-guards to a
hermetic subprocess fixture (tests/conftest.py assert_cold_of_harnessx) and
simulate extra-absent in the CLI dispatch tests, so the suite is deterministic
whether or not the optional extra is installed (it now is, locally). Verified the
OpenAI path constructs end-to-end (build_provider -> OpenAIProvider; orchestrator
-> Harness) with the budget threaded through.
137 passed, 3 skipped; ruff + mypy clean; detection gate PASS.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
Externalize detection rules to versioned TOML and remove rule-local severity, so
rules detect and the central CWE policy decides severity — fixing "rules too
influential / not centrally managed". Zero-dependency; detection is byte-identical
(benchmark recall 44/47 = 93.6%, FP 0% unchanged) and the 90/10 gate holds.
- RulesetStore (ruleset/store.py + ruleset/<lang>/rules.toml): PatternRule as data
(rule_id, title, languages, cwe, tags, pattern, status, min_evidence_level,
precision_estimate, version) with NO severity; load_ruleset (sorted, with
optional rule_policy.json ledger overlay) and a stdlib TOML write_ruleset. The
32 rules were generated from the prior PATTERN_RULES with a verified exact
round-trip (pattern/cwe/languages/tags/title).
- findings.py: drop the hardcoded PatternRule class + PATTERN_RULES tuple; load the
default ruleset from TOML; quick_scan_findings(root, targets, rankings, ruleset,
policy) resolves each finding's severity from policy via resolve_severity
(SEVERITY_LABEL maps 0-5 -> string) and tags findings with their rule status.
- Three-status volume control: StaticFinding gains status (default "enabled");
disabled rules never fire; shadow findings are partitioned out of the report and
score in the CLI (written to shadow_findings.json) — no shadow rules by default.
- Emission floors (RulesetConfig.min_emit_priority / min_emit_precision, default
0.0 = emit all) replace "every match is a finding".
- CLI loads the ruleset as a stage and uses it for the fail-loud policy check,
detection, hunter pool, and the score CWE map. Wheel ships the ruleset TOML.
85 tests pass; ruff + mypy clean. Spec tasks 3.1-3.4 checked off.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
