medium
4.3
CWE
20
Advisory Published
CVE Published
Updated

CVE-2020-15192: Memory leak in Tensorflow

First published: Fri Sep 25 2020(Updated: )

### Impact If a user passes a list of strings to `dlpack.to_dlpack` there is a memory leak following an expected validation failure: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L100-L104 The allocated memory is from https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L256 The issue occurs because the `status` argument during validation failures is not properly checked: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/c/eager/dlpack.cc#L265-L267 Since each of the above methods can return an error status, the `status` value must be checked before continuing. ### 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 discovered during variant analysis of [GHSA-rjjg-hgv6-h69v](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rjjg-hgv6-h69v).

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 Linux=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-2020-15192?

    CVE-2020-15192 is classified as having a medium severity due to the potential for memory leaks.

  • How do I fix CVE-2020-15192?

    To mitigate CVE-2020-15192, upgrade TensorFlow to version 2.3.1 or later.

  • What versions of TensorFlow are affected by CVE-2020-15192?

    CVE-2020-15192 affects TensorFlow versions 2.3.0 and 2.2.0.

  • Can CVE-2020-15192 be exploited remotely?

    CVE-2020-15192 is not a remotely exploitable vulnerability as it requires the user to pass malicious input.

  • What software packages are impacted by CVE-2020-15192?

    CVE-2020-15192 impacts the tensorflow, tensorflow-gpu, and tensorflow-cpu packages.

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