CVE-2020-15193: Memory corruption in Tensorflow

Published Sep 25, 2020
·
Updated

Impact The implementation of dlpack.todlpack can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/tfewrapper.cc#L1361

However, there is nothing stopping users from passing in a Python object instead of a tensor. python In [2]: tf.experimental.dlpack.todlpack([2]) ==1720623==WARNING: MemorySanitizer: use-of-uninitialized-value #0 0x55b0ba5c410a in tensorflow::(anonymous namespace)::GetTensorFromHandle(TFETensorHandle, TFStatus) thirdparty/tensorflow/c/eager/dlpack.cc:46:7 #1 0x55b0ba5c38f4 in tensorflow::TFEHandleToDLPack(TFETensorHandle, TFStatus) thirdparty/tensorflow/c/eager/dlpack.cc:252:26 ...

The uninitialized memory address is due to a reinterpretcast https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/eager/pywraptensor.cc#L848-L850

Since the PyObject is a Python object, not a TensorFlow Tensor, the cast to EagerTensor fails.

Patches We have patched the issue in 22e07fb204386768e5bcbea563641ea11f96ceb8 and will release a patch release for all affected versions.

We recommend users to upgrade to TensorFlow 2.2.1 or 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 has been reported by members of the Aivul Team from Qihoo 360.

Other sources

In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of dlpack.todlpack can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a reinterpretcast Since the PyObject is a Python object, not a TensorFlow Tensor, the cast to EagerTensor fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.

Affected Software

9 affected componentsFixes available
pip/tensorflow-gpu=2.3.0
2.3.1
pip/tensorflow-gpu=2.2.0
2.2.1
pip/tensorflow-cpu=2.3.0
2.3.1
pip/tensorflow-cpu=2.2.0
2.2.1
pip/tensorflow=2.3.0
2.3.1
pip/tensorflow=2.2.0
2.2.1
Google TensorFlow=2.2.0
Google TensorFlow=2.3.0
openSUSE Leap=15.2

Event History

Sep 25, 2020
Advisory Published
via GitHub·06:28 PM
CVE Published
via MITRE·06:40 PM
Data Sourced
via MITRE·06:40 PM
DescriptionSeverityWeakness
Data Sourced
via NVD·07:15 PM
RemedyDescriptionSeverityWeaknessAffected Software

Frequently Asked Questions

1

What is the severity of CVE-2020-15193?

CVE-2020-15193 has been classified as a high severity vulnerability due to the potential for memory corruption.

2

How do I fix CVE-2020-15193?

To fix CVE-2020-15193, upgrade the affected TensorFlow packages to version 2.3.1 or later.

3

Which versions of TensorFlow are affected by CVE-2020-15193?

CVE-2020-15193 affects TensorFlow versions 2.2.0 and 2.3.0.

4

What could happen if CVE-2020-15193 is exploited?

If exploited, CVE-2020-15193 may lead to memory corruption, potentially resulting in application crashes or arbitrary code execution.

5

Is CVE-2020-15193 relevant to both TensorFlow CPU and GPU?

Yes, CVE-2020-15193 affects both TensorFlow GPU and TensorFlow CPU implementations.

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