7.8
CWE
824
Advisory Published
Advisory Published
Updated

CVE-2021-37657: Reference binding to nullptr in `MatrixDiagV*` ops in TensorFlow

First published: Thu Aug 12 2021(Updated: )

### Impact An attacker can cause undefined behavior via binding a reference to null pointer in all operations of type `tf.raw_ops.MatrixDiagV*`: ```python import tensorflow as tf tf.raw_ops.MatrixDiagV3( diagonal=[1,0], k=[], num_rows=[1,2,3], num_cols=[4,5], padding_value=[], align='RIGHT_RIGHT') ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong: ```cc auto& diag_index = context->input(1); ... lower_diag_index = diag_index.flat<int32>()(0); ``` ### Patches We have patched the issue in GitHub commit [f2a673bd34f0d64b8e40a551ac78989d16daad09](https://github.com/tensorflow/tensorflow/commit/f2a673bd34f0d64b8e40a551ac78989d16daad09). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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.

Credit: security-advisories@github.com security-advisories@github.com

Affected SoftwareAffected VersionHow to fix
Google TensorFlow>=2.3.0<2.3.4
Google TensorFlow>=2.4.0<2.4.3
Google TensorFlow=2.5.0
Google TensorFlow=2.6.0-rc0
Google TensorFlow=2.6.0-rc1
Google TensorFlow=2.6.0-rc2
pip/tensorflow-gpu=2.5.0
2.5.1
pip/tensorflow-gpu>=2.4.0<2.4.3
2.4.3
pip/tensorflow-gpu<2.3.4
2.3.4
pip/tensorflow-cpu=2.5.0
2.5.1
pip/tensorflow-cpu>=2.4.0<2.4.3
2.4.3
pip/tensorflow-cpu<2.3.4
2.3.4
pip/tensorflow=2.5.0
2.5.1
pip/tensorflow>=2.4.0<2.4.3
2.4.3
pip/tensorflow<2.3.4
2.3.4
>=2.3.0<2.3.4
>=2.4.0<2.4.3
=2.5.0
=2.6.0-rc0
=2.6.0-rc1
=2.6.0-rc2

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