CVE-2021-37657: TensorFlow - Null Pointer Dereference in MatrixDiagV* Operations
Summary
CVE-2021-37657: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow across 3 version ranges.
>= 2.3.0, < 2.3.4
>= 2.4.0, < 2.4.3
< f2a673bd34f0d64b8e40a551ac78989d16daad09
Fixed in 2.3.4 or 2.4.3, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-37657 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.
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Background
TensorFlow is an end-to-end open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. However, a vulnerability has been discovered in TensorFlow that can lead to undefined behavior and potential security risks.
Vulnerability Detail
The vulnerability (CVE-2021-37657) in TensorFlow affects all operations of type tf.raw_ops.MatrixDiagV*. The issue arises from incomplete validation of the k tensor, which is expected to be either a scalar or a vector. However, there is no check for the number of elements in the tensor. If the k tensor is empty, code that accesses the first element of the tensor will result in a null pointer dereference. This can lead to crashes, memory corruption, or other undefined behavior, potentially enabling an attacker to execute arbitrary code or cause denial of service.
How To Fix
Latest published version: 2.21.0 as of 22 Oct 2026
pip install --upgrade "tensorflow==2.21.0"Upgrading to it may include breaking changes; the versions below are the minimum fixed release for this vulnerability.
If you can't move to the latest
The minimum fixed version depends on the release line you run.
-
>= 2.3.0, < 2.3.42.3.4pip install --upgrade "tensorflow==2.3.4" -
>= 2.4.0, < 2.4.32.4.3pip install --upgrade "tensorflow==2.4.3"
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Workaround
As a temporary workaround, users can avoid passing an empty tensor as the k parameter in operations of type tf.raw_ops.MatrixDiagV*. It is important to ensure that the k tensor contains valid values, such as a scalar or a vector with non-zero elements.
Conclusion
The vulnerability in TensorFlow (CVE-2021-37657) can lead to undefined behavior and potential security risks. It is crucial for users to upgrade to the patched versions of TensorFlow to mitigate these risks. Additionally, users can follow the provided workaround to avoid passing empty tensors as the k parameter. For further support and assistance in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open source platform for machine learning that provides tools and libraries for building and deploying machine learning models.
The vulnerability can lead to undefined behavior, including crashes, memory corruption, or other security risks. It may allow an attacker to execute arbitrary code or cause denial of service.
The vulnerability affects TensorFlow versions 2.3.0 and above. Specifically, TensorFlow 2.3.0, TensorFlow 2.3.4, TensorFlow 2.4.0, TensorFlow 2.4.3, TensorFlow 2.5.1, and TensorFlow 2.6.0.
Yes, the vulnerability has been patched in TensorFlow versions 2.6.0, 2.5.1, 2.4.3, and 2.3.4. Users are advised to upgrade to the latest available version.
Yes, as a temporary workaround, users can ensure that the k tensor passed to operations of type tf.raw_ops.MatrixDiagV* is not empty and contains valid values.
References
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