CVE-2020-26269: Heap out of bounds read in filesystem glob matching in TensorFlow

Published Dec 10, 2020
·
Updated

Impact The general implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories:

cc if (!fs->Match(childpath, dirs[dirindex])) { ... }

Since dirindex is unconditionaly incremented outside of the lambda function where the vulnerable pattern occurs, this results in an access out of bounds issue under certain scenarios. For example, if /tmp/x is a directory that only contains a single file y, then the following scenario will cause a crash due to the out of bounds read:

python >> tf.io.gfile.glob('/tmp/x/') Segmentation fault

There are multiple invariants and preconditions that are assumed by the parallel implementation of GetMatchingPaths but are not verified by the PRs introducing it (#40861 and #44310). Thus, we are completely rewriting the implementation to fully specify and validate these.

Patches We have patched the issue in GitHub commit 8b5b9dc96666a3a5d27fad7179ff215e3b74b67c and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

This issue only impacts master branch and the release candidates for TF version 2.4. The final release of the 2.4 release will be patched.

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 release candidate versions 2.4.0rc, the general implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories. There are multiple invariants and preconditions that are assumed by the parallel implementation of GetMatchingPaths but are not verified by the PRs introducing it (#40861 and #44310). Thus, we are completely rewriting the implementation to fully specify and validate these. This is patched in version 2.4.0. This issue only impacts master branch and the release candidates for TF version 2.4. The final release of the 2.4 release will be patched.

Affected Software

8 affected componentsFixes available
pip/tensorflow-gpu>=2.4.0rc0<2.4.0
2.4.0
pip/tensorflow-cpu>=2.4.0rc0<2.4.0
2.4.0
pip/tensorflow>=2.4.0rc0<2.4.0
2.4.0
Google TensorFlow=2.4.0-rc0
Google TensorFlow=2.4.0-rc1
Google TensorFlow=2.4.0-rc2
Google TensorFlow=2.4.0-rc3
Google TensorFlow=2.4.0-rc4

Event History

Dec 10, 2020
CVE Published
via MITRE·10:10 PM
Data Sourced
via MITRE·10:10 PM
DescriptionWeakness
Oct 7, 2022
Advisory Published
via GitHub·07:22 AM

Frequently Asked Questions

1

What is the severity of CVE-2020-26269?

CVE-2020-26269 is classified as a medium severity vulnerability due to its potential access out of bounds risk.

2

How do I fix CVE-2020-26269?

To fix CVE-2020-26269, upgrade to TensorFlow version 2.4.0 or higher.

3

Which versions of TensorFlow are affected by CVE-2020-26269?

CVE-2020-26269 affects TensorFlow versions 2.4.0-rc0 through 2.4.0-rc4.

4

What types of attacks can exploit CVE-2020-26269?

CVE-2020-26269 can potentially be exploited through maliciously crafted filesystem paths that cause out of bounds access.

5

Is CVE-2020-26269 related to any specific software packages?

CVE-2020-26269 is related to TensorFlow packages including tensorflow-gpu, tensorflow-cpu, and tensorflow.

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