CVE-2021-37637: Null pointer dereference in `CompressElement` in TensorFlow

Published Aug 12, 2021
·
Updated

Impact It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to tf.rawops.CompressElement:

python import tensorflow as tf

tf.rawops.CompressElement(components=[[]]) The implementation was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid.

Patches We have patched the issue in GitHub commit 5dc7f6981fdaf74c8c5be41f393df705841fb7c5.

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.

Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360. Concurrently, it was resolved in master branch as it was also discovered internally and fixed before the report was handled.

Other sources

TensorFlow is an end-to-end open source platform for machine learning. It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to tf.rawops.CompressElement. The implementation was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid. We have patched the issue in GitHub commit 5dc7f6981fdaf74c8c5be41f393df705841fb7c5. 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/tensorflow to a version that resolves this vulnerability.

    Patch 5dc7f6981fdaf74c8c5be41f393df705841fb7c5
  11. Upgrade

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

    Fixed in 2.6.0Patch 5dc7f6981fdaf74c8c5be41f393df705841fb7c5
  12. Upgrade

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

    Fixed in 2.5.1Patch 5dc7f6981fdaf74c8c5be41f393df705841fb7c5
  13. Upgrade

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

    Fixed in 2.4.3Patch 5dc7f6981fdaf74c8c5be41f393df705841fb7c5
  14. Upgrade

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

    Fixed in 2.3.4Patch 5dc7f6981fdaf74c8c5be41f393df705841fb7c5

Event History

Aug 12, 2021
CVE Published
via MITRE·06:15 PM
Data Sourced
via MITRE·06:15 PM
DescriptionSeverityWeakness
Data Sourced
via NVD·07:15 PM
RemedyDescriptionSeverityWeaknessAffected Software
Aug 25, 2021
Advisory Published
via GitHub·02:44 PM

Frequently Asked Questions

1

What is the severity of CVE-2021-37637?

CVE-2021-37637 has a severity rating that can lead to a denial of service due to null pointer dereference in TensorFlow.

2

How do I fix CVE-2021-37637?

To fix CVE-2021-37637, upgrade TensorFlow to version 2.5.1 or later.

3

Which versions of TensorFlow are affected by CVE-2021-37637?

CVE-2021-37637 affects TensorFlow versions from 2.3.0 to 2.6.0-rc2.

4

What are the potential impacts of CVE-2021-37637?

The potential impact of CVE-2021-37637 includes application crashes and service unavailability.

5

Is CVE-2021-37637 specific to TensorFlow GPU or CPU?

CVE-2021-37637 affects both TensorFlow GPU and CPU packages.

Company

Resources

Contact

SecAlerts Pty Ltd.
132 Wickham Terrace
Fortitude Valley,
QLD 4006, Australia
info@secalerts.co
By using SecAlerts services, you agree to our services end-user license agreement. This website is safeguarded by reCAPTCHA and governed by the Google Privacy Policy and Terms of Service. All names, logos, and brands of products are owned by their respective owners, and any usage of these names, logos, and brands for identification purposes only does not imply endorsement. If you possess any content that requires removal, please get in touch with us.
© 2026 SecAlerts Pty Ltd.
ABN: 70 645 966 203, ACN: 645 966 203