CVE-2023-25676: TensorFlow has null dereference on ParallelConcat with XLA

Published Mar 24, 2023
·
Updated

TensorFlow is an open source machine learning platform. When running versions prior to 2.12.0 and 2.11.1 with XLA, tf.rawops.ParallelConcat segfaults with a nullptr dereference when given a parameter shape with rank that is not greater than zero. A fix is available in TensorFlow 2.12.0 and 2.11.1.

Affected Software

1 affected component
Google TensorFlow<2.12.0

Event History

Mar 24, 2023
CVE Published
via MITRE·11:10 PM
Data Sourced
via MITRE·11:10 PM
DescriptionSeverityWeakness
Free Weekly Intel

Don't miss critical vulnerabilities

Join thousands of security professionals who receive our weekly digest of trending CVEs, zero-days, and exploited vulnerabilities.

No spam. Unsubscribe anytime.

Frequently Asked Questions

1

What is the vulnerability ID of this TensorFlow vulnerability?

The vulnerability ID of this TensorFlow vulnerability is CVE-2023-25676.

2

What is TensorFlow?

TensorFlow is an open source machine learning platform.

3

What is affected by this vulnerability?

Versions of TensorFlow prior to 2.12.0 and 2.11.1 with XLA are affected by this vulnerability.

4

What is the severity of this vulnerability?

The severity of this vulnerability is high with a CVSS score of 7.5.

5

How can I fix this TensorFlow vulnerability?

To fix this TensorFlow vulnerability, update to version 2.12.0 or 2.11.1.

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