`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
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]>
Adopt HarnessX at the hunter loop and verifier, capability-gated so the default
zero-dependency scan is byte-identical and CI (no extra) exercises the fallback.
- 6.1/6.2 build_sast() (single, lazy HarnessX composition site in harness_ext.py,
mirrors DocuHarnessX make_docgen) + HxScanOrchestrator (hunter_harness.py): a
drop-in for run_hunter_pool that runs one bounded HarnessX agent task per ranked
target (ModelConfig(main=AnthropicProvider).agentic(build_sast()).run(BaseTask)),
parses findings from a JSON structured-output contract, returns the existing
HunterPoolResult, maps scan_id->session_id and emits per-target exit_reason/cost/
steps to the outer trace.
- 6.4 verify_judge.verify_findings_dispatch: a drop-in superset of
verification.verify_findings — structural verifier verbatim by default (rejects
unknown reachability), HarnessX llm-judge (build_judge_prompt/parse_judge_response)
when the extra + a verifier model are present; verdict mapped to VerificationResult
evidence-gated (plausible>=0.6 accepted; unsupported/refused rejected; infra
failure never auto-rejects).
- 6.5 cli capability gate (hx_hunter/hx_verify): selects HxScanOrchestrator /
judge only in standard mode with a configured model AND has_harnessx(); otherwise
falls back and records a {stage,reason:harnessx_extra_unavailable} degradation in
the manifest (new append-only write_manifest degradations kwarg). With no model
configured the manifest stays byte-identical.
- pyproject: mypy override ignore_missing_imports for harnessx.* (absent in the
type-check env).
Verified the HarnessX path constructs against the real extra in a throwaway venv
(build_sast -> HarnessConfig; orchestrator -> Harness; build_judge_prompt/
parse_judge_response/verdict mapping) — only the live agent.run()/provider.complete()
LLM calls are offline-untestable and stay gated. 109 tests pass (2 construction
smokes skip without the extra), ruff + mypy clean, detection gate 93.6%/0% intact.
Tasks 6.1/6.2/6.4/6.5 done; 6.3 (deterministic stages as HarnessX processors) deferred.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
