CVE-2021-29579: TensorFlow - Heap Buffer Overflow in tf.raw_ops.MaxPoolGrad
Summary
CVE-2021-29579: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow 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
< a74768f8e4efbda4def9f16ee7e13cf3922ac5f7
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-2021-29579 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 is widely used in various domains, including computer vision, natural language processing, and deep learning. However, a vulnerability has been discovered in the implementation of tf.raw_ops.MaxPoolGrad function, which can lead to a heap buffer overflow.
Vulnerability Detail
The vulnerability exists in the tf.raw_ops.MaxPoolGrad function of TensorFlow. The implementation fails to validate the indices used to access elements of input/output arrays, resulting in potential out-of-bounds access in the out_backprop_flat array. This can lead to a heap buffer overflow, potentially allowing an attacker to execute arbitrary code or cause a denial-of-service condition. 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: 2.21.0 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"
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
As a temporary workaround, you can avoid using the tf.raw_ops.MaxPoolGrad function in your TensorFlow models or applications. Instead, you can use alternative pooling operations that are not affected by this vulnerability, such as tf.nn.max_pool2d or tf.keras.layers.MaxPooling2D.
Conclusion
The vulnerability in TensorFlow's tf.raw_ops.MaxPoolGrad function can lead to a heap buffer overflow, potentially enabling arbitrary code execution or causing a denial-of-service condition. It is crucial to update to TensorFlow 2.5.0 or later to mitigate this vulnerability. As a temporary workaround, you can avoid using the affected function and use alternative pooling operations. 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 tools, libraries, and resources for building and deploying machine learning models.
tf.raw_ops.MaxPoolGrad is a function in TensorFlow that computes the gradient of the max pooling operation.
A heap buffer overflow is a type of vulnerability where a program writes data beyond the bounds of a dynamically allocated buffer in the heap, potentially overwriting adjacent memory and leading to arbitrary code execution or a crash.
You can check the version of TensorFlow installed on your system by running the following command: pip show tensorflow. If the version is 2.4.2, 2.3.3, 2.2.3, or 2.1.4, your installation is affected by this vulnerability.
At the time of this advisory, there are no known public exploits for this vulnerability. However, it is important to patch your TensorFlow installation to prevent potential exploitation.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
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