CVE-2021-29576: TensorFlow - Heap Buffer Overflow in tf.raw_ops.MaxPool3DGradGrad
Summary
CVE-2021-29576: 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
< 63c6a29d0f2d692b247f7bf81f8732d6442fad09
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-2021-29576 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 includes various operations and functions for performing tasks such as data preprocessing, model training, and inference. One of the operations, tf.raw_ops.MaxPool3DGradGrad, is responsible for computing the gradient of the gradient for a 3D max pooling operation. However, this specific implementation of MaxPool3DGradGrad is vulnerable to a heap buffer overflow.
Vulnerability Detail
The vulnerability in tf.raw_ops.MaxPool3DGradGrad arises due to the incomplete initialization of the Pool3dParameters structure. The constructor of Pool3dParameters uses OP_REQUIRES to validate conditions, and if any assertion fails during initialization, the process is interrupted, leaving the params structure with invalid data. This can lead to a heap buffer overflow, depending on the default initialized values. An attacker could potentially exploit this vulnerability to execute arbitrary code or cause a denial-of-service condition.
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 workaround, you can avoid using the vulnerable tf.raw_ops.MaxPool3DGradGrad operation until you have updated to a fixed version of TensorFlow. If possible, you can consider using alternative operations or approaches to achieve the desired functionality.
Conclusion
The vulnerability in tf.raw_ops.MaxPool3DGradGrad in TensorFlow 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 the fixed versions of TensorFlow to mitigate these risks. For further support on vulnerability remediation, you can reach out to DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open source platform for machine learning that provides tools and resources for developing and deploying machine learning models.
tf.raw_ops.MaxPool3DGradGrad is an operation in TensorFlow that computes the gradient of the gradient for a 3D max pooling operation.
A heap buffer overflow is a type of vulnerability where a program writes data beyond the bounds of a heap-allocated buffer. This can lead to memory corruption and potentially allow an attacker to execute arbitrary code or cause a crash.
The vulnerability in tf.raw_ops.MaxPool3DGradGrad could potentially allow an attacker to execute arbitrary code or cause a denial-of-service condition. It is important to update to a fixed version of TensorFlow to mitigate these risks.
To update TensorFlow, you can use the package manager or follow the installation instructions provided by the TensorFlow project. It is recommended to upgrade to TensorFlow 2.5.0 or the cherrypicked fixes for versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
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