CVE-2021-29517: Division by zero in Conv3D of TensorFlow
Summary
CVE-2021-29517: Division by zero in Conv3D. 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-2021-29517 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 that provides a comprehensive ecosystem of tools, libraries, and community resources. It is widely used for developing and deploying machine learning models. The Conv3D operation in TensorFlow is used for three-dimensional convolutional operations, commonly employed in computer vision tasks. This vulnerability affects versions of TensorFlow prior to 2.1.4, 2.2.3, 2.3.3, and 2.4.2.
Vulnerability Detail
The vulnerability arises from a division by zero issue in the Conv3D implementation of TensorFlow. An attacker can exploit this vulnerability by providing specific inputs to the Conv3D operation, causing a division by zero error. This can lead to various consequences, including program crashes and potential denial-of-service attacks. The vulnerability can be triggered when the fifth element of the filter tensor is set to zero. Additionally, if the shape of the input and filter tensors does not adhere to the constraints specified in the operation's description, an Eigen assertion can be triggered, resulting in a program crash.
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
As a workaround, users can ensure that the filter tensor does not have a zero as its fifth element. Additionally, it is crucial to ensure that the shape of the input and filter tensors complies with the constraints specified in the Conv3D operation's description. Here is an example of valid tensor shapes:
import tensorflow as tf
input_tensor = tf.constant([], shape=[2, 2, 2, 2, 1], dtype=tf.float32)
filter_tensor = tf.constant([], shape=[1, 1, 2, 2, 1], dtype=tf.float32)
tf.raw_ops.Conv3D(input=input_tensor, filter=filter_tensor, strides=[1, 1, 1, 1, 1], padding='VALID', data_format='NDHWC', dilations=[1, 1, 1, 1, 1])
By ensuring valid tensor shapes, the vulnerability can be mitigated.
Conclusion
The division by zero vulnerability in the Conv3D implementation of TensorFlow can lead to program crashes and potential denial-of-service attacks. It is crucial to upgrade to the patched versions of TensorFlow to mitigate this vulnerability. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open-source machine learning framework used for developing and deploying machine learning models.
The Conv3D operation in TensorFlow is used for three-dimensional convolutional operations, commonly employed in computer vision tasks.
An attacker can exploit this vulnerability by providing specific inputs to the Conv3D operation, causing a division by zero error.
The consequences of this vulnerability include program crashes and potential denial-of-service attacks.
To mitigate this vulnerability, it is recommended to upgrade to the patched versions of TensorFlow: 2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
References
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