diff --git a/tensorflow/core/kernels/image/crop_and_resize_op.cc b/tensorflow/core/kernels/image/crop_and_resize_op.cc
index 5c196df9cfe..4efc4ae8846 100644
--- a/tensorflow/core/kernels/image/crop_and_resize_op.cc
+++ b/tensorflow/core/kernels/image/crop_and_resize_op.cc
@@ -207,7 +207,7 @@ class CropAndResizeOp : public AsyncOpKernel {
 namespace functor {
 template <typename T>
 struct CropAndResize<CPUDevice, T> {
-  bool operator()(const OpKernelContext* context,
+  bool operator()(OpKernelContext* context,
                   typename TTypes<T, 4>::ConstTensor image,
                   typename TTypes<float, 2>::ConstTensor boxes,
                   typename TTypes<int32, 1>::ConstTensor box_index,
@@ -222,6 +222,17 @@ struct CropAndResize<CPUDevice, T> {
     const int crop_width = crops.dimension(2);
     const int depth = crops.dimension(3);
 
+    // Since `functor::CropAndResize` operates on float, we first validate
+    // that we don't overflow (since overflow causes undefined behavior which
+    // could result in segfault in this scenario).
+    const Eigen::Tensor<bool, 0, Eigen::RowMajor> only_finite_elements =
+        boxes.isfinite().all();
+    if (!only_finite_elements()) {
+      context->SetStatus(errors::InvalidArgument(
+          "Boxes contains at least one element that is not finite"));
+      return false;
+    }
+
     // Sharding across boxes.
     auto CropAndResizePerBox = [&](int64 start_box, int64 limit_box) {
       for (int b = start_box; b < limit_box; ++b) {
diff --git a/tensorflow/python/ops/image_ops_test.py b/tensorflow/python/ops/image_ops_test.py
index a7c964d8a30..c77fe1a300c 100644
--- a/tensorflow/python/ops/image_ops_test.py
+++ b/tensorflow/python/ops/image_ops_test.py
@@ -5710,6 +5710,25 @@ class DecodeImageTest(test_util.TensorFlowTestCase, parameterized.TestCase):
           self.assertAllEqual(list(image2.shape), [12, 40, 20, 3])
           self.assertAllEqual(image2, image3)
 
+  def testImageCropAndResize(self):
+    if test_util.is_gpu_available():
+      op = image_ops_impl.crop_and_resize_v2(
+          image=array_ops.zeros((2, 1, 1, 1)),
+          boxes=[[1.0e+40, 0, 0, 0]],
+          box_indices=[1],
+          crop_size=[1, 1])
+      self.evaluate(op)
+    else:
+      message = "Boxes contains at least one element that is not finite"
+      with self.assertRaisesRegex((errors.InvalidArgumentError, ValueError),
+                                  message):
+        op = image_ops_impl.crop_and_resize_v2(
+            image=array_ops.zeros((2, 1, 1, 1)),
+            boxes=[[1.0e+40, 0, 0, 0]],
+            box_indices=[1],
+            crop_size=[1, 1])
+        self.evaluate(op)
+
   @parameterized.named_parameters(
       ("_jpeg", "JPEG", "jpeg_merge_test1.jpg"),
       ("_png", "PNG", "lena_rgba.png"),