CVE-2020-15204: Segfault in Tensorflow

Published Sep 25, 2020
·
Updated

Impact In eager mode, TensorFlow does not set the session state. Hence, calling tf.rawops.GetSessionHandle or tf.rawops.GetSessionHandleV2 results in a null pointer dereference: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/sessionops.cc#L45

In the above snippet, in eager mode, ctx->sessionstate() returns nullptr. Since code immediately dereferences this, we get a segmentation fault.

Patches We have patched the issue in 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1 and will release patch releases for all versions between 1.15 and 2.3.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

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 eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling tf.rawops.GetSessionHandle or tf.rawops.GetSessionHandleV2 results in a null pointer dereference In linked snippet, in eager mode, ctx->sessionstate() returns nullptr. Since code immediately dereferences this, we get a segmentation fault. The issue is patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Affected Software

21 affected componentsFixes available
pip/tensorflow-gpu=2.3.0
2.3.1
pip/tensorflow-gpu=2.2.0
2.2.1
pip/tensorflow-gpu>=2.1.0<2.1.2
2.1.2
pip/tensorflow-gpu>=2.0.0<2.0.3
2.0.3
pip/tensorflow-gpu<1.15.4
1.15.4
pip/tensorflow-cpu=2.3.0
2.3.1
pip/tensorflow-cpu=2.2.0
2.2.1
pip/tensorflow-cpu>=2.1.0<2.1.2
2.1.2
pip/tensorflow-cpu>=2.0.0<2.0.3
2.0.3
pip/tensorflow-cpu<1.15.4
1.15.4
pip/tensorflow=2.3.0
2.3.1
pip/tensorflow=2.2.0
2.2.1
pip/tensorflow>=2.1.0<2.1.2
2.1.2
pip/tensorflow>=2.0.0<2.0.3
2.0.3
pip/tensorflow<1.15.4
1.15.4
Google TensorFlow<1.15.4
Google TensorFlow>=2.0.0<2.0.3
Google TensorFlow>=2.1.0<2.1.2
Google TensorFlow>=2.2.0<2.2.1
Google TensorFlow>=2.3.0<2.3.1
openSUSE Leap=15.2

Event History

Sep 25, 2020
Advisory Published
via GitHub·06:28 PM
CVE Published
via MITRE·06:46 PM
Data Sourced
via MITRE·06:46 PM
DescriptionSeverityWeakness
Data Sourced
via NVD·07:15 PM
RemedyDescriptionSeverityWeaknessAffected Software

Frequently Asked Questions

1

What is the severity of CVE-2020-15204?

CVE-2020-15204 has a severity rating of medium due to the null pointer dereference that can occur in eager mode.

2

How do I fix CVE-2020-15204?

To fix CVE-2020-15204, upgrade TensorFlow to version 2.3.1 or higher.

3

Which versions of TensorFlow are affected by CVE-2020-15204?

CVE-2020-15204 affects TensorFlow versions 2.0.0 to 2.3.0, as well as 1.15.x.

4

What are the implications of CVE-2020-15204 in a production environment?

In a production environment, CVE-2020-15204 may lead to application crashes or unexpected behaviors due to null pointer dereferences.

5

Is CVE-2020-15204 only applicable to specific operating systems?

CVE-2020-15204 is primarily associated with TensorFlow and is not limited to a specific operating system.

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