CVE-2021-29546: TensorFlow - Integer Division by Zero in QuantizedBiasAdd
Summary
CVE-2021-29546: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow across 5 version ranges.
>= 2.2.0, < 2.3.0rc0
< 2.2.0rc0
>= 2.3.0, < 2.3.4
>= 2.4.0, < 2.4.3
< 67784700869470d65d5f2ef20aeb5e97c31673cb
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-2021-29546 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 developing and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. It offers support for both CPU and GPU computations, making it suitable for a wide range of hardware configurations. However, a vulnerability has been discovered in TensorFlow that can lead to an integer division by zero undefined behavior in the tf.raw_ops.QuantizedBiasAdd function.
Vulnerability Detail
The vulnerability arises from the implementation of the Eigen kernel in TensorFlow. The kernel performs a division by the number of elements of the smaller input, based on shape, without checking if the divisor is zero. An attacker can exploit this vulnerability by providing inputs that result in a zero divisor, triggering the integer division by zero undefined behavior. The impact of this vulnerability can vary depending on the context in which TensorFlow is used. In certain scenarios, it may lead to crashes, unexpected behavior, or even potential security risks.
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.
-
< 2.2.0rc02.2.0rc0pip install --upgrade "tensorflow==2.2.0rc0" -
>= 2.2.0, < 2.3.0rc02.3.0rc0pip install --upgrade "tensorflow==2.3.0rc0" -
>= 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 of now, there is no specific workaround available for this vulnerability. However, users can mitigate the risk by ensuring they are using the latest patched version of TensorFlow once it is released. It is also recommended to follow best practices for secure coding and input validation to minimize the potential impact of vulnerabilities.
Conclusion
The identified vulnerability in TensorFlow can result in an integer division by zero undefined behavior in the tf.raw_ops.QuantizedBiasAdd function. This can have various consequences, including crashes, unexpected behavior, and potential security risks. To address this vulnerability, it is crucial to update TensorFlow to the latest patched version once it becomes available. Following secure coding practices and input validation can also help mitigate the risk. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open source platform for machine learning that provides a comprehensive ecosystem of tools and resources.
The impact can vary depending on the context, but it may lead to crashes, unexpected behavior, or potential security risks.
Update your TensorFlow installation to the latest patched version once it becomes available.
There are no specific workarounds at the moment, but following secure coding practices and input validation can help mitigate the risk.
The vulnerability affects TensorFlow versions 2.1.4 and above, including 2.2.x, 2.3.x, and 2.4.x.
References
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