Vulnerability: Division by zero in `Conv2DBackpropFilter` ### Impact
An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`:

```python
import tensorflow as tf

input_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
filter_sizes = tf.constant([0, 0, 0, 0], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropFilter(
  input=input_tensor,
  filter_sizes=filter_sizes,
  out_backprop=out_backprop,
  strides=[1, 1, 1, 1],
  use_cudnn_on_gpu=False,
  padding='SAME',
  explicit_paddings=[],
  data_format='NHWC',
  dilations=[1, 1, 1, 1]
)
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments):

```cc
const size_t size_A = output_image_size * filter_total_size; 
const size_t size_B = output_image_size * dims.out_depth;
const size_t size_C = filter_total_size * dims.out_depth;
const size_t work_unit_size = size_A + size_B + size_C;
const size_t shard_size = (target_working_set_size + work_unit_size - 1) / work_unit_size;
```

If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 

### Patches
We have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21f11c96](https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96).

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

### For more information
Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.
  
### Attribution
This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.
Vulnerability ID: GHSA-j8qc-5fqr-52fp
CWE: CWE-369
Severity score: 2.5
Project: https://github.com/tensorflow/tensorflow
Published: 2021-05-21
File: tensorflow/core/kernels/conv_grad_filter_ops.cc
Commit message: Fix issues in Conv2DBackpropFilter.

PiperOrigin-RevId: 369772454
Change-Id: I49b465f2ae2ce91def61b56cea8000197d5177d8
Commit author: Mihai Maruseac

Vulnerable version (before fix):
 class Conv2DCustomBackpropFilterOp : public OpKernel {     const int filter_total_size = dims.spatial_dims[0].filter_size *                                   dims.spatial_dims[1].filter_size *                                   dims.in_depth;     // The output image size is the spatial size of the output.     const int output_image_size =         dims.spatial_dims[0].output_size * dims.spatial_dims[1].output_size; class Conv2DCustomBackpropFilterOp : public OpKernel {      const size_t work_unit_size = size_A + size_B + size_C;      const size_t shard_size =         (target_working_set_size + work_unit_size - 1) / work_unit_size; 

Fixed version (after fix):
 class Conv2DCustomBackpropFilterOp : public OpKernel {     const int filter_total_size = dims.spatial_dims[0].filter_size *                                   dims.spatial_dims[1].filter_size *                                   dims.in_depth;     OP_REQUIRES(         context,         filter_total_size * dims.out_depth == filter_backprop->NumElements(),         errors::InvalidArgument(             "filter_size does not have enough elements, requested ",             filter_total_size * dims.out_depth, ", got ",             filter_backprop->NumElements()));      // The output image size is the spatial size of the output.     const int output_image_size =         dims.spatial_dims[0].output_size * dims.spatial_dims[1].output_size; class Conv2DCustomBackpropFilterOp : public OpKernel {      const size_t work_unit_size = size_A + size_B + size_C;      OP_REQUIRES(         context, work_unit_size != 0,         errors::InvalidArgument(             "Work size for convolution would be 0, which is not acceptable"));      const size_t shard_size =         (target_working_set_size + work_unit_size - 1) / work_unit_size; 

Patch:
patch: @@ -495,6 +495,14 @@ class Conv2DCustomBackpropFilterOp : public OpKernel {
     const int filter_total_size = dims.spatial_dims[0].filter_size *
                                   dims.spatial_dims[1].filter_size *
                                   dims.in_depth;
+    OP_REQUIRES(
+        context,
+        filter_total_size * dims.out_depth == filter_backprop->NumElements(),
+        errors::InvalidArgument(
+            "filter_size does not have enough elements, requested ",
+            filter_total_size * dims.out_depth, ", got ",
+            filter_backprop->NumElements()));
+
     // The output image size is the spatial size of the output.
     const int output_image_size =
         dims.spatial_dims[0].output_size * dims.spatial_dims[1].output_size;
@@ -518,6 +526,11 @@ class Conv2DCustomBackpropFilterOp : public OpKernel {
 
     const size_t work_unit_size = size_A + size_B + size_C;
 
+    OP_REQUIRES(
+        context, work_unit_size != 0,
+        errors::InvalidArgument(
+            "Work size for convolution would be 0, which is not acceptable"));
+
     const size_t shard_size =
         (target_working_set_size + work_unit_size - 1) / work_unit_size;
path: tensorflow/core/kernels/conv_grad_filter_ops.cc