Integer division by 0 in sparse reshaping

Overview

Impact

The implementation of tf.raw_ops.SparseReshape can be made to trigger an integral division by 0 exception:

import tensorflow as tf

tf.raw_ops.SparseReshape(
  input_indices = np.ones((1,3)),
  input_shape = np.array([1,1,0]),
  new_shape = np.array([1,0]))

The implementation calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements.

The reshape functor blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0.

Patches

We have patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version.

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.

Affected packages

• tensorflow < 2.3.4
• tensorflow >= 2.4.0, < 2.4.3
• tensorflow >= 2.5.0, < 2.5.1
• tensorflow-cpu < 2.3.4
• tensorflow-cpu >= 2.4.0, < 2.4.3
• tensorflow-cpu >= 2.5.0, < 2.5.1
• tensorflow-gpu < 2.3.4
• tensorflow-gpu >= 2.4.0, < 2.4.3
• tensorflow-gpu >= 2.5.0, < 2.5.1

Remediation

Upgrade to a patched release:

• tensorflow 2.3.4
• tensorflow 2.4.3
• tensorflow 2.5.1
• tensorflow-cpu 2.3.4
• tensorflow-cpu 2.4.3
• tensorflow-cpu 2.5.1
• tensorflow-gpu 2.3.4
• tensorflow-gpu 2.4.3
• tensorflow-gpu 2.5.1