fix: reject unprovenanced ML artifacts

scoring/engine.py

@@ -5,6 +5,7 @@ import os
 import logging
 
 from scoring.policy import SCORE_VERSION, assessment_for_score
+from ml.artifacts import validate_ml_artifact
 
 log = logging.getLogger(__name__)
 
@@ -580,16 +581,30 @@ _ML_KEY_MAP = {
 }
 
 
+_ml_artifact_status = {"valid": False, "errors": ["not_loaded"]}
+
+
 def load_ml_weights():
-    """Load ML-optimized weights from config."""
+    """Load weights only when their schema and training provenance are valid."""
+    global _ml_artifact_status
     try:
         with open(ML_WEIGHTS_PATH) as f:
             data = json.load(f)
+        _ml_artifact_status = validate_ml_artifact(data)
+        if not _ml_artifact_status["valid"]:
+            log.error("Rejected invalid ML artifact: %s", ", ".join(_ml_artifact_status["errors"]))
+            return {}
         return data.get("weights", {})
-    except Exception:
+    except Exception as exc:
+        _ml_artifact_status = {"valid": False, "errors": [f"load_error:{exc}"]}
         return {}
 
 
+def get_ml_artifact_status():
+    """Return the status from the most recent artifact load attempt."""
+    return dict(_ml_artifact_status)
+
+
 def score_all_ml(metrics):
     """Score all metrics using ML-optimized weights.
 
@@ -604,7 +619,12 @@ def score_all_ml(metrics):
     if not ml_weights:
         # Fallback to classic if no ML weights available
         classic["ml_mode"] = False
-        classic["ml_error"] = "ML weights not found — run ml/optimizer.py"
+        status = get_ml_artifact_status()
+        if status.get("errors") and status["errors"] != ["not_loaded"]:
+            classic["ml_error"] = "ML artifact invalid: " + ", ".join(status["errors"])
+        else:
+            classic["ml_error"] = "ML weights not found — run ml/optimizer.py"
+        classic["ml_artifact"] = status
         return classic
 
     results = classic["metrics"]