high
8.8
CWE
908
Advisory Published
Advisory Published
Updated

CVE-2022-23573: Uninitialized variable access in Tensorflow

First published: Fri Feb 04 2022(Updated: )

### Impact The [implementation of `AssignOp`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/assign_op.h#L30-L143) can result in copying unitialized data to a new tensor. This later results in undefined behavior. The implementation has a check that the left hand side of the assignment is initialized (to minimize number of allocations), but does not check that the right hand side is also initialized. ### Patches We have patched the issue in GitHub commit [ef1d027be116f25e25bb94a60da491c2cf55bd0b](https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b). The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range. ### 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.

Credit: security-advisories@github.com security-advisories@github.com

Affected SoftwareAffected VersionHow to fix
pip/tensorflow-gpu=2.7.0
2.7.1
pip/tensorflow-gpu>=2.6.0<2.6.3
2.6.3
pip/tensorflow-gpu<2.5.3
2.5.3
pip/tensorflow-cpu=2.7.0
2.7.1
pip/tensorflow-cpu>=2.6.0<2.6.3
2.6.3
pip/tensorflow-cpu<2.5.3
2.5.3
pip/tensorflow=2.7.0
2.7.1
pip/tensorflow>=2.6.0<2.6.3
2.6.3
pip/tensorflow<2.5.3
2.5.3
TensorFlow Keras<=2.5.2
TensorFlow Keras>=2.6.0<=2.6.2
TensorFlow Keras=2.7.0

Remedy

https://github.com/tensorflow/tensorflow/commit/ef1d027be116f25e25bb94a60da491c2cf55bd0b

Remedy

https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q85f-69q7-55h2

Never miss a vulnerability like this again

Sign up to SecAlerts for real-time vulnerability data matched to your software, aggregated from hundreds of sources.

Read MoreStart Free Trial

Reference Links

Frequently Asked Questions

  • What is the severity of CVE-2022-23573?

    CVE-2022-23573 has a high severity rating due to the potential for undefined behavior caused by copying uninitialized data.

  • How do I fix CVE-2022-23573?

    To fix CVE-2022-23573, upgrade to TensorFlow versions 2.5.3, 2.6.3, or 2.7.1 depending on the installed version.

  • What types of software are affected by CVE-2022-23573?

    CVE-2022-23573 affects certain versions of Google TensorFlow, including those up to 2.5.2 and versions between 2.6.0 and 2.6.2, as well as 2.7.0.

  • What are the consequences of CVE-2022-23573?

    CVE-2022-23573 can lead to unstable software behavior due to undefined actions from uninitialized data being used.

  • Are there workarounds for CVE-2022-23573?

    There are no official workarounds for CVE-2022-23573; the only recommended solution is to upgrade to a patched version.

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.
© 2025 SecAlerts Pty Ltd.
ABN: 70 645 966 203, ACN: 645 966 203