GHSA-g7p5-5759-qv46 Data leak in Tensorflow

• PyPI tensorflow: < 1.15.4 → fixed in 1.15.4 • PyPI tensorflow: >= 2.0.0, < 2.0.3 → fixed in 2.0.3 • PyPI tensorflow: >= 2.1.0, < 2.1.2 → fixed in 2.1.2 • PyPI tensorflow: >= 2.2.0, < 2.2.1 → fixed in 2.2.1 • PyPI tensorflow: >= 2.3.0, < 2.3.1 → fixed in 2.3.1 • PyPI tensorflow-cpu: < 1.15.4 → fixed in 1.15.4 • PyPI tensorflow-cpu: >= 2.0.0, < 2.0.3 → fixed in 2.0.3 • PyPI tensorflow-cpu: >= 2.1.0, < 2.1.2 → fixed in 2.1.2 • PyPI tensorflow-cpu: >= 2.2.0, < 2.2.1 → fixed in 2.2.1 • PyPI tensorflow-cpu: >= 2.3.0, < 2.3.1 → fixed in 2.3.1 • PyPI tensorflow-gpu: < 1.15.4 → fixed in 1.15.4 • PyPI tensorflow-gpu: >= 2.0.0, < 2.0.3 → fixed in 2.0.3 • PyPI tensorflow-gpu: >= 2.1.0, < 2.1.2 → fixed in 2.1.2 • PyPI tensorflow-gpu: >= 2.2.0, < 2.2.1 → fixed in 2.2.1 • PyPI tensorflow-gpu: >= 2.3.0, < 2.3.1 → fixed in 2.3.1

Impact The `data_splits` argument of `tf.raw_ops.StringNGrams` lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory ```python >>> tf.raw_ops.StringNGrams(data=["aa", "bb", "cc", "dd", "ee", "ff"], data_splits=[0,8], separator=" ", ngram_widths=[3], left_pad="", right_pad="", pad_width=0, preserve_short_sequences=False) StringNGrams(ngrams=<tf.Tensor: shape=(6,), dtype=string, numpy= array([b'aa bb cc', b'bb cc dd', b'cc dd ee', b'dd ee ff', b'ee ff \xf4j\xa7q\x7f\x00\x00q\x00\x00\x00\x00\x00\x00\x00\xd8\x9b~\xa8q\x7f\x00', b'ff \xf4j\xa7q\x7f\x00\x00q\x00\x00\x00\x00\x00\x00\x00\xd8\x9b~\xa8q\x7f\x00 \x9b~\xa8q\x7f\x00\x00p\xf5j\xa7q\x7f\x00\x00H\xf8j\xa7q\x7f\x00\x00\xf0\xf3\xf7\x85q\x7f\x00\x00`}\xa6\x00\x00\x00\x00\x00`~\xa6\x00\x00\x00\x00\x00\xb0~\xeb\x9bq\x7f\x00'],... ``` All the binary strings after `ee ff` are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. Patches We have patched the issue in 0462de5b544ed4731aa2fb23946ac22c01856b80 and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. 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 members of the Aivul Team from Qihoo 360.