CVE-2021-29610: Invalid validation in `QuantizeAndDequantizeV2` in TensorFlow
Summary
CVE-2021-29610: Invalid validation in QuantizeAndDequantizeV2. Affects tensorflow across 4 version ranges.
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
Fixed in 4 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-29610 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 open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and community resources that enable researchers and developers to build and deploy machine learning models. The QuantizeAndDequantizeV2 function in TensorFlow is used for quantization and dequantization of tensors, which is an important step in many machine learning applications.
Vulnerability Detail
The validation in tf.raw_ops.QuantizeAndDequantizeV2 allows invalid values for the axis argument. This vulnerability can be exploited by passing a value of axis that is less than -1, resulting in a heap underflow. Attackers can leverage this vulnerability to read or write to other data on the heap, potentially leading to unauthorized access or data corruption.
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.
-
< 2.1.42.1.4pip install --upgrade "tensorflow==2.1.4" -
>= 2.2.0, < 2.2.32.2.3pip install --upgrade "tensorflow==2.2.3" -
>= 2.3.0, < 2.3.32.3.3pip install --upgrade "tensorflow==2.3.3" -
>= 2.4.0, < 2.4.22.4.2pip install --upgrade "tensorflow==2.4.2"
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Workaround
There is no known workaround for this vulnerability. It is recommended to apply the patch or update to the latest version of TensorFlow as soon as possible.
Conclusion
The vulnerability in QuantizeAndDequantizeV2 in TensorFlow allows for invalid values of the axis argument, leading to a heap underflow. This can be exploited by attackers to read or write to other data on the heap. It is recommended to apply the patch or update to the latest version of TensorFlow to mitigate the risk. For further support on vulnerability remediation, please contact DevNack.com.
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FAQ
The vulnerability allows attackers to read or write to other data on the heap, potentially leading to unauthorized access or data corruption.
You can use the Vulert Playground (https://vulert.com/scanner) to check if your application is affected by this vulnerability. Simply upload your Pipfile.lock file and Vulert will analyze it for open-source vulnerabilities, including this one.
Yes, the CVSS score for this vulnerability is 3.6.
There are no known exploits for this vulnerability at the time of writing. However, it is important to apply the patch or update to the latest version of TensorFlow to mitigate the risk.
This vulnerability was reported by Yakun Zhang and Ying Wang of Baidu X-Team.
References
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