CVE-2021-29525: Division by 0 in TensorFlow Conv2DBackpropInput
Summary
CVE-2021-29525: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-gpu across 5 version ranges.
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
< 2be2cdf3a123e231b16f766aa0e27d56b4606535
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-cpu · PyPI
The same CVE-2021-29525 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 includes various operations for convolutional neural networks, including the tf.raw_ops.Conv2DBackpropInput operation.
Vulnerability Detail
The vulnerability (CVE-2021-29525) exists in the implementation of the tf.raw_ops.Conv2DBackpropInput operation in TensorFlow. An attacker can trigger a division by 0 by controlling a quantity used in the division. This can lead to a runtime error and potentially disrupt the execution of the machine learning model. The vulnerability affects versions of TensorFlow up to 2.4.2, 2.3.3, 2.2.3, and 2.1.4.
How To Fix
Latest published version: 2.12.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-gpu==2.12.0"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-gpu==2.1.4" -
>= 2.2.0, < 2.2.32.2.3pip install --upgrade "tensorflow-gpu==2.2.3" -
>= 2.3.0, < 2.3.32.3.3pip install --upgrade "tensorflow-gpu==2.3.3" -
>= 2.4.0, < 2.4.22.4.2pip install --upgrade "tensorflow-gpu==2.4.2"
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Workaround
As a temporary workaround, users can validate the input quantities to ensure they are non-zero before invoking the tf.raw_ops.Conv2DBackpropInput operation. By checking and handling the division by 0 condition explicitly, the vulnerability can be mitigated. Here's an example of how to implement the workaround in Python:
import tensorflow as tf
# Validate input quantities
if quantity != 0:
# Invoke Conv2DBackpropInput operation
output = tf.raw_ops.Conv2DBackpropInput(...)
else:
# Handle division by 0 condition
...
Conclusion
In conclusion, the vulnerability (CVE-2021-29525) in TensorFlow's tf.raw_ops.Conv2DBackpropInput operation allows an attacker to trigger a division by 0. The fix for this vulnerability will be included in TensorFlow 2.5.0 and backported to older supported versions. Users are advised to update their TensorFlow installations to the fixed versions to mitigate the risk. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow is an open source platform for machine learning that provides tools and libraries for building and deploying machine learning models.
tf.raw_ops.Conv2DBackpropInput is an operation in TensorFlow that performs the backpropagation of gradients for the input of a 2D convolution operation.
An attacker can exploit this vulnerability by controlling a quantity used in the division operation within tf.raw_ops.Conv2DBackpropInput, causing a division by 0 and potentially disrupting the execution of the machine learning model.
The vulnerability affects TensorFlow versions up to 2.4.2, 2.3.3, 2.2.3, and 2.1.4.
To protect your TensorFlow installation, it is recommended to update to the fixed versions (2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4) as soon as they become available.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
