CVE-2020-26266: Uninitialized memory access in Eigen types in TensorFlow

Published Dec 10, 2020
·
Updated

Impact Under certain cases, a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen:

cc struct QUInt8 { QUInt8() {} // ... uint8t value; };

struct QInt16 { QInt16() {} // ... int16t value; };

struct QUInt16 { QUInt16() {} // ... uint16t value; };

struct QInt32 { QInt32() {} // ... int32t value; };

Patches We have patched the issue in GitHub commit ace0c15a22f7f054abcc1f53eabbcb0a1239a9e2 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.

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

In affected versions of TensorFlow under certain cases a saved model can trigger use of uninitialized values during code execution. This is caused by having tensor buffers be filled with the default value of the type but forgetting to default initialize the quantized floating point types in Eigen. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

Affected Software

20 affected componentsFixes available
pip/tensorflow-gpu>=2.3.0<2.3.2
2.3.2
pip/tensorflow-gpu>=2.2.0<2.2.2
2.2.2
pip/tensorflow-gpu>=2.1.0<2.1.3
2.1.3
pip/tensorflow-gpu>=2.0.0<2.0.4
2.0.4
pip/tensorflow-gpu<1.15.5
1.15.5
pip/tensorflow-cpu>=2.3.0<2.3.2
2.3.2
pip/tensorflow-cpu>=2.2.0<2.2.2
2.2.2
pip/tensorflow-cpu>=2.1.0<2.1.3
2.1.3
pip/tensorflow-cpu>=2.0.0<2.0.4
2.0.4
pip/tensorflow-cpu<1.15.5
1.15.5
pip/tensorflow>=2.3.0<2.3.2
2.3.2
pip/tensorflow>=2.2.0<2.2.2
2.2.2
pip/tensorflow>=2.1.0<2.1.3
2.1.3
pip/tensorflow>=2.0.0<2.0.4
2.0.4
pip/tensorflow<1.15.5
1.15.5
Google TensorFlow<1.15.5
Google TensorFlow>=2.0.0<2.0.4
Google TensorFlow>=2.1.0<2.1.3
Google TensorFlow>=2.2.0<2.2.2
Google TensorFlow>=2.3.0<2.3.2

Event History

Dec 10, 2020
Advisory Published
via GitHub·07:07 PM
CVE Published
via MITRE·10:10 PM
Data Sourced
via MITRE·10:10 PM
DescriptionSeverityWeakness
Data Sourced
via NVD·11:15 PM
RemedyDescriptionSeverityWeaknessAffected Software

Frequently Asked Questions

1

What is the severity of CVE-2020-26266?

CVE-2020-26266 has a medium severity due to the potential for uninitialized value usage in TensorFlow models.

2

How do I fix CVE-2020-26266?

To fix CVE-2020-26266, upgrade TensorFlow to version 2.3.2, 2.2.2, 2.1.3, 2.0.4, or 1.15.5.

3

Which software versions are affected by CVE-2020-26266?

CVE-2020-26266 affects TensorFlow versions earlier than 1.15.5 and versions from 2.0.0 to 2.3.0.

4

What causes CVE-2020-26266?

CVE-2020-26266 is caused by the use of uninitialized values during code execution in TensorFlow when handling quantized floating point types.

5

Is there a patch for CVE-2020-26266?

Yes, upgrading to the specified fixed versions provides a patch for CVE-2020-26266.

Contact

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