- New Aenebris.ML.Middleware module: Wai middleware that wires the
Aenebris.ML.Engine into the per-request pipeline. Per request:
1. Run extractFeatures (FeatureContext + Request) -> FeatureVector
2. Convert via featureVectorToVector -> VU.Vector Double
3. runEngine eng fv -> DecisionDetails
4. Route by ddDecision:
DecisionHuman -> pass through to inner Application,
optionally attach X-Aenebris-ML-Decision
and X-Aenebris-ML-Score response headers
DecisionBot -> 403 + text/plain block body
DecisionChallenge -> 403 + text/html challenge page
All response builders are operator-overridable via the
MLMiddlewareConfig record. Optional logging callback fires once
per request for structured logs / metrics integration.
- defaultBotResponse, defaultChallengeResponse: sensible defaults
matching the existing WAF middleware pattern (403 + descriptive
body). decisionResponseHeader / scoreResponseHeader are exported
so operators can build custom responses that still surface the
signal headers.
- 12 tests via Network.Wai.Test.runSession covering: pass-through
on Human, 403 on Bot, 403 on Challenge with text/html body,
signal-header attachment toggle, custom response builder override,
logging callback invocation count.
- aenebris.cabal: expose Aenebris.ML.Middleware in the library stanza.
- test/Spec.hs: add modifyTVar' to the Control.Concurrent.STM import
list for the logging-callback test.
- 354 total examples passing, 0 GHC warnings on the new module.
This closes the core Phase 2.5 ML pipeline. The full sequence
(Features -> Model -> Loader -> Inference -> Calibration -> IForest
-> Engine -> Middleware) is now fully implemented and integration-
tested. A hosted Aenebris instance can now be configured with a
trained LightGBM model + optional calibrator + optional IForest
and will block bots, challenge ambiguous traffic, and pass humans
on every HTTP request.
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