CVE-2021-37665: TensorFlow CPU - Incomplete Validation in MKL Implementation of Requantization
Summary
CVE-2021-37665: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-cpu across 4 version ranges.
>= 2.3.0, < 2.3.4
>= 2.4.0, < 2.4.3
< 9e62869465573cb2d9b5053f1fa02a81fce21d69
< 203214568f5bc237603dbab6e1fd389f1572f5c9
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 · PyPI
- tensorflow · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-37665 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 community resources that enable researchers and developers to build and deploy machine learning models. TensorFlow is widely used in various domains, including computer vision, natural language processing, and reinforcement learning. The TensorFlow CPU package is a Python package that provides CPU support for TensorFlow. It allows users to run TensorFlow models on CPUs without the need for specialized hardware. However, the package is vulnerable to an incomplete validation issue in the MKL (Math Kernel Library) implementation of requantization.
Vulnerability Detail
The vulnerability in TensorFlow CPU arises from incomplete validation in the MKL implementation of requantization. This can lead to undefined behavior, such as triggering a null pointer reference or accessing data outside the bounds of heap allocated arrays. The issue is caused by the lack of dimension validation in the input tensor in the mkl_requantization_range_per_channel_op.cc and mkl_requantize_per_channel_op.cc files. An attacker can exploit this vulnerability to execute arbitrary code or cause a denial-of-service condition. The vulnerability affects versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and 2.5.1, and has been patched in commits 9e62869465573cb2d9b5053f1fa02a81fce21d69 and 203214568f5bc237603dbab6e1fd389f1572f5c9 on the TensorFlow GitHub repository.
How To Fix
Latest published version: 2.21.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-cpu==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-cpu==2.3.4" -
>= 2.4.0, < 2.4.32.4.3pip install --upgrade "tensorflow-cpu==2.4.3"
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Conclusion
The incomplete validation vulnerability in the MKL implementation of requantization in TensorFlow CPU can have severe consequences, including arbitrary code execution and denial-of-service attacks. It is crucial to update the TensorFlow CPU package to version 2.6.0 or later to address this vulnerability. For further support and assistance in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-cpu →
FAQ
TensorFlow CPU is a Python package that provides CPU support for TensorFlow, allowing users to run TensorFlow models on CPUs without specialized hardware.
MKL stands for Math Kernel Library. It is a highly optimized, threaded, and vectorized math library for Intel processors. TensorFlow uses MKL for efficient numerical computations on CPUs.
Requantization is a process in machine learning that involves converting the numerical values of tensors to a different scale or precision. It is commonly used in quantized neural networks to reduce memory usage and improve inference performance.
This vulnerability can be exploited by an attacker to execute arbitrary code or cause a denial-of-service condition. It may lead to the compromise of sensitive data, unauthorized access, or disruption of services.
No, only versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and 2.5.1 are affected. It is recommended to update to version 2.6.0 or later to mitigate the vulnerability.
References
Related vulnerabilities
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