Описание
TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.FractionalMaxPoolGrad triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a CHECK condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Ссылки
- PatchThird Party Advisory
- ExploitPatchThird Party Advisory
- PatchThird Party Advisory
- ExploitPatchThird Party Advisory
Уязвимые конфигурации
Одно из
EPSS
2.5 Low
CVSS3
5.5 Medium
CVSS3
2.1 Low
CVSS2
Дефекты
Связанные уязвимости
TensorFlow is an end-to-end open source platform for machine learning. ...
Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad`
EPSS
2.5 Low
CVSS3
5.5 Medium
CVSS3
2.1 Low
CVSS2