Reject non-finite numbers in GenAI review descriptions

A model returning NaN for confidence or potential_threat_level slipped through the model_construct fallback, which skips validation, and was written into the review segment's JSON data. NaN is not valid JSON, so every subsequent /review request failed with "Out of range float values are not JSON compliant", blanking the review page for any time range containing the poisoned row.

frigate/genai/__init__.py

@@ -23,6 +23,7 @@ from frigate.genai.prompts import (
     build_review_summary_prompt,
 )
 from frigate.models import Event
+from frigate.util.builtin import has_non_finite_number
 
 logger = logging.getLogger(__name__)
 
@@ -164,6 +165,15 @@ class GenAIClient:
                 except json.JSONDecodeError as je:
                     logger.error("Failed to parse review description JSON: %s", je)
                     return None
+
+                # model_construct skips validation, so non-finite numbers that
+                # the validated path would have rejected have to be caught here
+                if has_non_finite_number(raw):
+                    logger.error(
+                        "Discarding review description containing non-finite numbers."
+                    )
+                    return None
+
                 # observations and confidence are required on the model; fill an empty default
                 # if the response omitted it so attribute access stays safe.
                 raw.setdefault("observations", [])

frigate/util/builtin.py

@@ -472,6 +472,18 @@ def sanitize_float(value):
     return value
 
 
+def has_non_finite_number(value: Any) -> bool:
+    """Return True if any number in a parsed JSON value is NaN or infinite."""
+    if isinstance(value, float):
+        return not math.isfinite(value)
+    if isinstance(value, dict):
+        return any(has_non_finite_number(v) for v in value.values())
+    if isinstance(value, list):
+        return any(has_non_finite_number(v) for v in value)
+
+    return False
+
+
 def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
     return 1 - cosine_distance(a, b)