Memory exhaustion in Tensorflow
### Impact
The implementation of `StringNGrams`
https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/string_ngrams_op.cc#L29-L161 can be used to trigger a denial of service attack by causing an OOM condition after an integer overflow:
```python
import tensorflow as tf
tf.raw_ops.StringNGrams(
data=['123456'],
data_splits=[0,1],
separator='a'*15,
ngram_widths=[],
left_pad='',
right_pad='',
pad_width=-5,
preserve_short_sequences=True)
```
We are missing a validation on `pad_witdh` and that result in computing a negative value for `ngram_width` which is later used to allocate parts of the output.
### Patches
We have patched the issue in GitHub commit f68fdab93fb7f4ddb4eb438c8fe052753c9413e8
https://github.com/tensorflow/tensorflow/commit/f68fdab93fb7f4ddb4eb438c8fe052753c9413e8.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Yu Tian of Qihoo 360 AIVul Team.