diff --git a/src/supervision/detection/core.py b/src/supervision/detection/core.py
index a131b6c9..f629e96e 100644
--- a/src/supervision/detection/core.py
+++ b/src/supervision/detection/core.py
@@ -666,10 +666,10 @@ class Detections:
                 or absent. `detections.tracker_id` is `None` when no
                 predictions carry a tracker ID, or when only a subset do
                 (mixed batch) — in that case all tracker IDs are dropped to
-                preserve alignment with the bounding boxes. Note: mixed
-                batches containing both RLE/polygon predictions and box-only
-                predictions may misalign the `mask` array; this is a
-                pre-existing limitation not addressed by this fix.
+                preserve alignment with the bounding boxes.
+                `detections.mask` is `None` when no predictions include mask
+                data, or when only a subset do (mixed batch) — in that case
+                all masks are dropped to preserve alignment.
 
         Example:
             ```python
diff --git a/src/supervision/detection/utils/internal.py b/src/supervision/detection/utils/internal.py
index 467c84b4..ed84b5cc 100644
--- a/src/supervision/detection/utils/internal.py
+++ b/src/supervision/detection/utils/internal.py
@@ -50,6 +50,23 @@ def extract_ultralytics_masks(yolov8_results: Any) -> npt.NDArray[np.bool_] | No
     return cast(npt.NDArray[np.bool_], np.asarray(mask_maps, dtype=bool))
 
 
+def _all_present_or_none(
+    values: list[Any],
+    label: str,
+    dtype: npt.DTypeLike,
+) -> npt.NDArray[Any] | None:
+    # Identity check (`v is None`) is required when values may contain numpy arrays:
+    # `None in values` triggers element-wise comparison and raises ValueError.
+    missing = sum(v is None for v in values)
+    if 0 < missing < len(values):
+        logger.warning(
+            "Partial %s in batch; dropping all to preserve alignment with xyxy.", label
+        )
+    if not values or missing > 0:
+        return None
+    return np.array(values, dtype=dtype)
+
+
 def process_roboflow_result(
     roboflow_result: dict[str, Any],
 ) -> tuple[
@@ -74,10 +91,14 @@ def process_roboflow_result(
     Returns:
         A 6-tuple of ``(xyxy, confidence, class_id, masks, tracker_ids, data)``
         where each array is aligned with the others. ``masks`` is ``None``
-        when no predictions include mask data. ``tracker_ids`` is ``None``
-        when no predictions carry a tracker ID, or when only a subset do
-        (mixed batch) — in that case all tracker IDs are dropped to preserve
-        alignment with ``xyxy``.
+        when no predictions include mask data, or when only a subset do
+        (mixed batch) — in that case all masks are dropped to preserve
+        alignment with ``xyxy``. A failed RLE decode is treated identically
+        to a box-only prediction, so one corrupt RLE payload in an otherwise
+        fully-masked batch also causes all masks to be dropped.
+        ``tracker_ids`` is ``None`` when no predictions carry a tracker ID,
+        or when only a subset do (mixed batch) — in that case all tracker
+        IDs are dropped to preserve alignment with ``xyxy``.
 
     Examples:
         >>> from supervision.detection.utils.internal import process_roboflow_result
@@ -100,7 +121,7 @@ def process_roboflow_result(
     confidence: list[float] = []
     class_id: list[int] = []
     class_name: list[str] = []
-    masks: list[npt.NDArray[np.bool_]] = []
+    masks: list[npt.NDArray[np.bool_] | None] = []
     tracker_ids: list[int | None] = []
 
     image_width = int(roboflow_result["image"]["width"])
@@ -151,6 +172,7 @@ def process_roboflow_result(
             class_id.append(prediction["class_id"])
             class_name.append(prediction["class"])
             confidence.append(prediction["confidence"])
+            masks.append(None)
             tracker_ids.append(prediction.get("tracker_id"))
         elif len(prediction["points"]) >= 3:
             polygon = np.array(
@@ -180,18 +202,11 @@ def process_roboflow_result(
     class_name_arr: npt.NDArray[np.str_] = (
         np.array(class_name) if len(class_name) > 0 else np.empty(0, dtype=str)
     )
-    masks_arr: npt.NDArray[np.bool_] | None = (
-        np.array(masks, dtype=bool) if len(masks) > 0 else None
+    masks_arr: npt.NDArray[np.bool_] | None = _all_present_or_none(
+        masks, "mask", dtype=bool
     )
-    if tracker_ids and 0 < tracker_ids.count(None) < len(tracker_ids):
-        logger.warning(
-            "Partial tracker_id in batch; dropping all tracker_ids to preserve "
-            "alignment with xyxy."
-        )
-    tracker_id_arr: npt.NDArray[np.integer] | None = (
-        np.array(tracker_ids, dtype=np.int64)
-        if tracker_ids and None not in tracker_ids
-        else None
+    tracker_id_arr: npt.NDArray[np.integer] | None = _all_present_or_none(
+        tracker_ids, "tracker_id", dtype=np.int64
     )
     data: _DetectionDataType = {CLASS_NAME_DATA_FIELD: class_name_arr}
 
diff --git a/tests/detection/test_core.py b/tests/detection/test_core.py
index d4033337..69b4f76f 100644
--- a/tests/detection/test_core.py
+++ b/tests/detection/test_core.py
@@ -1013,6 +1013,46 @@ def test_from_inference_partial_tracker_id_does_not_crash() -> None:
     assert detections["class_name"] is not None
 
 
+def test_from_inference_partial_mask_does_not_crash() -> None:
+    """Results where only some predictions carry a mask must not raise."""
+    result = {
+        "image": {"width": 100, "height": 100},
+        "predictions": [
+            {
+                "x": 20,
+                "y": 20,
+                "width": 20,
+                "height": 20,
+                "confidence": 0.9,
+                "class": "a",
+                "class_id": 0,
+                "points": [
+                    {"x": 10, "y": 10},
+                    {"x": 30, "y": 10},
+                    {"x": 30, "y": 30},
+                    {"x": 10, "y": 30},
+                ],
+            },
+            {
+                "x": 70,
+                "y": 70,
+                "width": 20,
+                "height": 20,
+                "confidence": 0.8,
+                "class": "b",
+                "class_id": 1,
+            },
+        ],
+    }
+
+    detections = Detections.from_inference(result)
+
+    # all detections are kept; masks are dropped rather than misaligned
+    assert len(detections) == 2
+    assert detections.mask is None
+    assert detections.xyxy.shape == (2, 4)
+
+
 def test_from_inference_empty_class_name_dtype_matches_non_empty() -> None:
     """Empty and non-empty results should produce string-kind class_name arrays."""
     empty_result = {"predictions": [], "image": {"width": 100, "height": 100}}
diff --git a/tests/detection/utils/test_internal.py b/tests/detection/utils/test_internal.py
index 293ce332..a2be07d3 100644
--- a/tests/detection/utils/test_internal.py
+++ b/tests/detection/utils/test_internal.py
@@ -436,17 +436,14 @@ TEST_RLE_NONCONTIGUOUS_MASK[0, 3, 2:4] = True
                 np.array([[0.5, 0.5, 2.5, 2.5], [2.0, 2.0, 4.0, 4.0]]),
                 np.array([0.9, 0.8]),
                 np.array([0, 1]),
-                # NOTE: known misalignment — masks has 1 entry, xyxy has 2.
-                # Mixed RLE + box-only batches produce mask arrays shorter than
-                # xyxy; constructing Detections from this result would raise
-                # ValueError. This is a pre-existing limitation shared by the
-                # polygon + box-only path.
-                TEST_RLE_MASK,
+                # When only some predictions carry a mask, all masks are dropped
+                # so the result stays aligned with xyxy (mirrors tracker_id).
+                None,
                 None,
                 {CLASS_NAME_DATA_FIELD: np.array(["person", "car"])},
             ),
             DoesNotRaise(),
-        ),  # mixed RLE + box-only batch — masks misaligned with xyxy (known limitation)
+        ),  # mixed RLE + box-only batch — masks dropped to preserve alignment
         pytest.param(
             {
                 "predictions": [