First published: Thu Aug 12 2021(Updated: )
### Impact If a user does not provide a valid padding value to `tf.raw_ops.MatrixDiagPartOp`, then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first: ```python import tensorflow as tf tf.raw_ops.MatrixDiagPartV2( input=tf.ones(2,dtype=tf.int32), k=tf.ones(2,dtype=tf.int32), padding_value=[]) ``` Although this example is given for `MatrixDiagPartV2`, all versions of the operation are affected. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. ### Patches We have patched the issue in GitHub commit [482da92095c4d48f8784b1f00dda4f81c28d2988](https://github.com/tensorflow/tensorflow/commit/482da92095c4d48f8784b1f00dda4f81c28d2988). 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
Affected Software | Affected Version | How 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 |
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