Incomplete validation in `QuantizeV2`
### Impact
Due to incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays:
```python
import tensorflow as tf
tf.raw_ops.QuantizeV2(
input=[1,2,3],
min_range=[1,2],
max_range=[],
T=tf.qint32,
mode='SCALED',
round_mode='HALF_AWAY_FROM_ZERO',
narrow_range=False,
axis=1,
ensure_minimum_range=3)
```
The implementation
https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59 has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor.
### Patches
We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708
https://github.com/tensorflow/tensorflow/commit/6da6620efad397c85493b8f8667b821403516708.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.