CVE-2021-29550: TensorFlow FractionalAvgPool Division by Zero Denial of Service
Summary
CVE-2021-29550: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-cpu 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
< 548b5eaf23685d86f722233d8fbc21d0a4aecb96
Fixed in 4 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-29550 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, allowing users to leverage the power of hardware acceleration for faster model training and inference. However, a vulnerability has been discovered in the tf.raw_ops.FractionalAvgPool function of TensorFlow, which can lead to a runtime division by zero error and denial of service.
Vulnerability Detail
The vulnerability in TensorFlow's tf.raw_ops.FractionalAvgPool function arises from the computation of a divisor quantity by dividing two user-controlled values: input_size[i] and pooling_ratio_[i]. If the value in input_size[i] is smaller than the pooling_ratio_[i], the floor operation results in output_size[i] being 0. This issue is not triggered in released versions of TensorFlow due to a no-op line of code. However, in debug mode or certain unreleased versions, the division by zero occurs, leading to a runtime crash. The computed values are later used as arguments to GeneratePoolingSequence, where a division in a modulo operation can result in further crashes if output_length is 0. The impact of this vulnerability is a denial of service, potentially disrupting the availability of TensorFlow-based applications.
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.
-
< 2.2.0rc02.2.0rc0pip install --upgrade "tensorflow-cpu==2.2.0rc0" -
>= 2.2.0, < 2.3.0rc02.3.0rc0pip install --upgrade "tensorflow-cpu==2.3.0rc0" -
>= 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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Workaround
As a workaround, users can manually check and ensure that the values of input_size[i] and pooling_ratio_[i] are properly validated and handled in their TensorFlow-based applications. By ensuring that input_size[i] is always greater than or equal to pooling_ratio_[i], the division by zero error can be avoided. Here is an example code snippet demonstrating the validation:
if input_size[i] < pooling_ratio_[i]:
# Handle the error condition appropriately
...
Conclusion
The discovered vulnerability in TensorFlow's tf.raw_ops.FractionalAvgPool function can result in a runtime division by zero error and denial of service. It is crucial for users to update their TensorFlow installations to the patched versions once they are released. Additionally, users can implement the provided workaround to mitigate the risk of the division by zero error. For further support and assistance in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-cpu →
FAQ
TensorFlow is an open source platform for machine learning that provides a comprehensive ecosystem of tools and resources for developing and deploying machine learning models.
The vulnerability can lead to a runtime division by zero error and denial of service in TensorFlow-based applications, potentially disrupting their availability.
The vulnerability affects TensorFlow versions 2.1.4, 2.2.3, 2.3.3, 2.4.2, and earlier versions. The fix will be included in TensorFlow 2.5.0 and backported to the affected versions.
To fix this vulnerability, users should update their TensorFlow installations to the patched versions as soon as they become available. Follow the official TensorFlow documentation and release notes for guidance on updating.
Yes, a workaround is to manually validate and handle the values of input_size[i] and pooling_ratio_[i] in TensorFlow-based applications to avoid the division by zero error. See the provided code snippet for an example.
References
Related vulnerabilities
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