Описание
Out of bounds read in Tensorflow
Impact
The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect.
If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible.
These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
Patches
We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are affected.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Пакеты
tensorflow
= 2.7.0
2.7.1
tensorflow-cpu
= 2.7.0
2.7.1
tensorflow-gpu
= 2.7.0
2.7.1
Связанные уязвимости
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
Tensorflow is an Open Source Machine Learning Framework. The TFG diale ...