CVE-2021-37690: Use after free and segfault in shape inference functions in TensorFlow

Published Aug 12, 2021
·
Updated

Impact When running shape functions, some functions (such as MutableHashTableShape) produce extra output information in the form of a ShapeAndType struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault.

ShapeRefiner is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types.

Patches We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Other sources

TensorFlow is an end-to-end open source platform for machine learning. In affected versions when running shape functions, some functions (such as MutableHashTableShape) produce extra output information in the form of a ShapeAndType struct. The shapes embedded in this struct are owned by an inference context that is cleaned up almost immediately; if the upstream code attempts to access this shape information, it can trigger a segfault. ShapeRefiner is mitigating this for normal output shapes by cloning them (and thus putting the newly created shape under ownership of an inference context that will not die), but we were not doing the same for shapes and types. This commit fixes that by doing similar logic on output shapes and types. We have patched the issue in GitHub commit ee119d4a498979525046fba1c3dd3f13a039fbb1. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Affected Software

15 affected componentsFixes available
pip/tensorflow-gpu=2.5.0
2.5.1
pip/tensorflow-gpu>=2.4.0<2.4.3
2.4.3
pip/tensorflow-gpu<2.3.4
2.3.4
pip/tensorflow-cpu=2.5.0
2.5.1
pip/tensorflow-cpu>=2.4.0<2.4.3
2.4.3
pip/tensorflow-cpu<2.3.4
2.3.4
pip/tensorflow=2.5.0
2.5.1
pip/tensorflow>=2.4.0<2.4.3
2.4.3
pip/tensorflow<2.3.4
2.3.4
Google TensorFlow>=2.3.0<2.3.4
Google TensorFlow>=2.4.0<2.4.3
Google TensorFlow=2.5.0
Google TensorFlow=2.6.0-rc0
Google TensorFlow=2.6.0-rc1
Google TensorFlow=2.6.0-rc2

Remediation

Recommended actions to resolve this vulnerability, in priority order.

  1. Upgrade

    Upgrade pip/tensorflow-gpu to a version that resolves this vulnerability.

    Fixed in 2.5.1
  2. Upgrade

    Upgrade pip/tensorflow-gpu to a version that resolves this vulnerability.

    Fixed in 2.4.3
  3. Upgrade

    Upgrade pip/tensorflow-gpu to a version that resolves this vulnerability.

    Fixed in 2.3.4
  4. Upgrade

    Upgrade pip/tensorflow-cpu to a version that resolves this vulnerability.

    Fixed in 2.5.1
  5. Upgrade

    Upgrade pip/tensorflow-cpu to a version that resolves this vulnerability.

    Fixed in 2.4.3
  6. Upgrade

    Upgrade pip/tensorflow-cpu to a version that resolves this vulnerability.

    Fixed in 2.3.4
  7. Upgrade

    Upgrade pip/tensorflow to a version that resolves this vulnerability.

    Fixed in 2.5.1
  8. Upgrade

    Upgrade pip/tensorflow to a version that resolves this vulnerability.

    Fixed in 2.4.3
  9. Upgrade

    Upgrade pip/tensorflow to a version that resolves this vulnerability.

    Fixed in 2.3.4
  10. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Fixed in 2.6.0
  11. Upgrade

    Upgrade tensorflow to a version that resolves this vulnerability.

    Patch ee119d4a498979525046fba1c3dd3f13a039fbb1
  12. Upgrade

    Upgrade tensorflow 2.5.1 to a version that resolves this vulnerability.

    Patch ee119d4a498979525046fba1c3dd3f13a039fbb1
  13. Upgrade

    Upgrade tensorflow 2.4.3 to a version that resolves this vulnerability.

    Patch ee119d4a498979525046fba1c3dd3f13a039fbb1
  14. Upgrade

    Upgrade tensorflow 2.3.4 to a version that resolves this vulnerability.

    Patch ee119d4a498979525046fba1c3dd3f13a039fbb1

Event History

Aug 12, 2021
CVE Published
via MITRE·11:10 PM
Data Sourced
via MITRE·11:10 PM
DescriptionSeverityWeakness
Aug 13, 2021
Data Sourced
via NVD·12:15 AM
RemedyDescriptionSeverityWeaknessAffected Software
Aug 25, 2021
Advisory Published
via GitHub·02:39 PM
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Frequently Asked Questions

1

What is the severity of CVE-2021-37690?

CVE-2021-37690 has been classified with a medium severity level due to its potential impact on data integrity.

2

How do I fix CVE-2021-37690?

To remediate CVE-2021-37690, upgrade TensorFlow to version 2.5.1 or later, or use versions 2.4.3 and 2.3.4 as appropriate.

3

What versions of TensorFlow are affected by CVE-2021-37690?

CVE-2021-37690 affects TensorFlow versions from 2.3.0 up to and including 2.6.0-rc2.

4

What is the nature of the vulnerability in CVE-2021-37690?

CVE-2021-37690 relates to improper handling of shapes in certain shape functions which can lead to memory issues.

5

Is CVE-2021-37690 a remote code execution vulnerability?

CVE-2021-37690 is not classified as a remote code execution vulnerability, but it may affect the stability of the application.

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