Impact
The SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the indices tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix:
|
const auto indices_values = indices.matrix<int64>(); |
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 3cbb917 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
Impact
The
SparseCountSparseOutputimplementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that theindicestensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix:tensorflow/tensorflow/core/kernels/count_ops.cc
Line 185 in 0e68f4d
However, malicious users can pass in tensors of different rank, resulting in a
CHECKassertion 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 3cbb917 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