high
7.1
CWE
908
Advisory Published
CVE Published
Updated

CVE-2020-15193: Memory corruption in Tensorflow

First published: Fri Sep 25 2020(Updated: )

### Impact The implementation of `dlpack.to_dlpack` 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/tfe_wrapper.cc#L1361 However, there is nothing stopping users from passing in a Python object instead of a tensor. ```python In [2]: tf.experimental.dlpack.to_dlpack([2]) ==1720623==WARNING: MemorySanitizer: use-of-uninitialized-value #0 0x55b0ba5c410a in tensorflow::(anonymous namespace)::GetTensorFromHandle(TFE_TensorHandle*, TF_Status*) third_party/tensorflow/c/eager/dlpack.cc:46:7 #1 0x55b0ba5c38f4 in tensorflow::TFE_HandleToDLPack(TFE_TensorHandle*, TF_Status*) third_party/tensorflow/c/eager/dlpack.cc:252:26 ... ``` The uninitialized memory address is due to a `reinterpret_cast` https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/python/eager/pywrap_tensor.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](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
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
TensorFlow Keras=2.2.0
TensorFlow Keras=2.3.0
SUSE openSUSE=15.2

Remedy

https://github.com/tensorflow/tensorflow/commit/22e07fb204386768e5bcbea563641ea11f96ceb8

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Reference Links

Frequently Asked Questions

  • 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.

  • How do I fix CVE-2020-15193?

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

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

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

  • 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.

  • 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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