GHSA-xmq7-7fxm-rr79 Denial of Service 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 By controlling the `fill` argument of `tf.strings.as_string`, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/as_string_op.cc#L68-L74 This can result in unexpected output: ```python In [1]: tf.strings.as_string(input=[1234], width=6, fill='-') Out[1]: In [2]: tf.strings.as_string(input=[1234], width=6, fill='+') Out[2]: In [3]: tf.strings.as_string(input=[1234], width=6, fill="h") Out[3]: In [4]: tf.strings.as_string(input=[1234], width=6, fill="d") Out[4]: In [5]: tf.strings.as_string(input=[1234], width=6, fill="o") Out[5]: In [6]: tf.strings.as_string(input=[1234], width=6, fill="x") Out[6]: In [7]: tf.strings.as_string(input=[1234], width=6, fill="g") Out[7]: In [8]: tf.strings.as_string(input=[1234], width=6, fill="a") Out[8]: In [9]: tf.strings.as_string(input=[1234], width=6, fill="c") Out[9]: In [10]: tf.strings.as_string(input=[1234], width=6, fill="p") Out[10]: In [11]: tf.strings.as_string(input=[1234], width=6, fill='m') Out[11]: ``` However, passing in `n` or `s` results in segmentation fault. Patches We have patched the issue in 33be22c65d86256e6826666662e40dbdfe70ee83 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.