GHSA-98j8-c9q4-r38g Memory exhaustion in Tensorflow

• PyPI tensorflow: < 2.5.3 → fixed in 2.5.3 • PyPI tensorflow: >= 2.6.0, < 2.6.3 → fixed in 2.6.3 • PyPI tensorflow: >= 2.7.0, < 2.7.1 → fixed in 2.7.1 • PyPI tensorflow-cpu: < 2.5.3 → fixed in 2.5.3 • PyPI tensorflow-cpu: >= 2.6.0, < 2.6.3 → fixed in 2.6.3 • PyPI tensorflow-cpu: >= 2.7.0, < 2.7.1 → fixed in 2.7.1 • PyPI tensorflow-gpu: < 2.5.3 → fixed in 2.5.3 • PyPI tensorflow-gpu: >= 2.6.0, < 2.6.3 → fixed in 2.6.3 • PyPI tensorflow-gpu: >= 2.7.0, < 2.7.1 → fixed in 2.7.1

Impact The implementation of `StringNGrams` 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. 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 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.