Denial of Service in Tensorflow
Moderate severity
GitHub Reviewed
Published
Sep 24, 2020
in
tensorflow/tensorflow
•
Updated Oct 28, 2024
Description
Reviewed
Sep 25, 2020
Published to the GitHub Advisory Database
Sep 25, 2020
Published by the National Vulnerability Database
Sep 25, 2020
Last updated
Oct 28, 2024
Impact
The
SparseCountSparseOutput
implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that theindices
tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix:https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L185
However, malicious users can pass in tensors of different rank, resulting in a
CHECK
assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor.Patches
We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.
We recommend users to upgrade to TensorFlow 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 is a variant of GHSA-p5f8-gfw5-33w4
References