Heap buffer overflow in `MaxPool3DGradGrad`
### Impact
The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow:
```python
import tensorflow as tf
values = [0.01] * 11
orig_input = tf.constant(values, shape=[11, 1, 1, 1, 1], dtype=tf.float32)
orig_output = tf.constant([0.01], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
grad = tf.constant([0.01], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.MaxPool3DGradGrad(
orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,
strides=strides, padding=padding)
```
The implementation
https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696 does not check that the initialization of `Pool3dParameters` completes successfully:
```cc
Pool3dParameters params{context, ksize_, stride_,
padding_, data_format_, tensor_in.shape()};
```
Since the constructor
https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88 uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values.
### Patches
We have patched the issue in GitHub commit 63c6a29d0f2d692b247f7bf81f8732d6442fad09
https://github.com/tensorflow/tensorflow/commit/63c6a29d0f2d692b247f7bf81f8732d6442fad09.
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.