CVE-2022-23572: Crash when type cannot be specialized in Tensorflow

Published Feb 4, 2022
·
Updated

Impact Under certain scenarios, TensorFlow can fail to specialize a type during shape inference:

cc void InferenceContext::PreInputInit( const OpDef& opdef, const std::vector<const Tensor>& inputtensors, const std::vector<ShapeHandle>& inputtensorsasshapes) { const auto ret = fulltype::SpecializeType(attrs, opdef); DCHECK(ret.status().ok()) << "while instantiating types: " << ret.status(); rettypes = ret.ValueOrDie(); // ... }

However, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the ValueOrDie line. This results in an assertion failure as ret contains an error Status, not a value. In the second case we also get a crash due to the assertion failure. Patches We have patched the issue in GitHub commit cb164786dc891ea11d3a900e90367c339305dc7b.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, 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 Open Source Machine Learning Framework. Under certain scenarios, TensorFlow can fail to specialize a type during shape inference. This case is covered by the DCHECK function however, DCHECK is a no-op in production builds and an assertion failure in debug builds. In the first case execution proceeds to the ValueOrDie line. This results in an assertion failure as ret contains an error Status, not a value. In the second case we also get a crash due to the assertion failure. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, and TensorFlow 2.6.3, as these are also affected and still in supported range.

Affected Software

12 affected componentsFixes available
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
Google TensorFlow<=2.5.2
Google TensorFlow>=2.6.0<=2.6.2
Google TensorFlow=2.7.0

Event History

Feb 4, 2022
CVE Published
via MITRE·10:32 PM
Data Sourced
via MITRE·10:32 PM
DescriptionSeverityWeakness
Feb 9, 2022
Advisory Published
via GitHub·11:28 PM

Frequently Asked Questions

1

What is the severity of CVE-2022-23572?

The severity of CVE-2022-23572 is classified as medium.

2

How do I fix CVE-2022-23572?

To fix CVE-2022-23572, upgrade TensorFlow to version 2.7.1 or later.

3

What versions of TensorFlow are affected by CVE-2022-23572?

CVE-2022-23572 affects TensorFlow versions up to 2.5.2, and versions 2.6.0 to 2.6.2, as well as 2.7.0.

4

What are the implications of CVE-2022-23572 for TensorFlow users?

Users of TensorFlow may experience incorrect type specialization during shape inference, which could affect model performance.

5

Is CVE-2022-23572 exploitable in production environments?

Yes, CVE-2022-23572 can potentially be exploited in production environments where affected TensorFlow versions are used.

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