CVE-2021-29569: TensorFlow CPU - Out-of-Bounds Read in MaxPoolGradWithArgmax
Summary
CVE-2021-29569: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-cpu 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
< ef0c008ee84bad91ec6725ddc42091e19a30cf0e
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-2021-29569 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 allows users to build and train neural networks, perform data preprocessing and augmentation, and deploy models in various environments. The TensorFlow CPU package, available on PyPI, is a CPU-only version of TensorFlow that can be used on systems without GPU support.
Vulnerability Detail
The vulnerability (CVE-2021-29569) exists in the implementation of tf.raw_ops.MaxPoolGradWithArgmax in TensorFlow. This function is responsible for computing the gradients of the max pooling operation with argmax indices. By supplying specially crafted inputs, an attacker can trigger an out-of-bounds read in the function. The issue arises when the input_min and input_max tensors are empty, causing the function to access the first element in two arrays. As .flat<T>() returns an empty object backed by an empty array, accessing even the 0th element results in a read outside the bounds. This vulnerability can potentially lead to information disclosure or denial of service.
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.
-
< 2.1.42.1.4pip install --upgrade "tensorflow-cpu==2.1.4" -
>= 2.2.0, < 2.2.32.2.3pip install --upgrade "tensorflow-cpu==2.2.3" -
>= 2.3.0, < 2.3.32.3.3pip install --upgrade "tensorflow-cpu==2.3.3" -
>= 2.4.0, < 2.4.22.4.2pip install --upgrade "tensorflow-cpu==2.4.2"
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Workaround
There is no specific workaround mentioned for this vulnerability.
Conclusion
The vulnerability (CVE-2021-29569) in TensorFlow CPU's tf.raw_ops.MaxPoolGradWithArgmax function can result in out-of-bounds reads when supplied with specially crafted inputs. This can potentially lead to information disclosure or denial of service. It is recommended to update the TensorFlow CPU package to one of the fixed versions (2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4) to mitigate the vulnerability. For further support on 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 set of tools and libraries for developing and deploying machine learning models.
The TensorFlow CPU package is a version of TensorFlow that is optimized for running on systems without GPU support. It allows users to utilize TensorFlow's functionality on CPU-only machines.
This vulnerability can potentially lead to information disclosure or denial of service. An attacker can exploit the vulnerability by supplying specially crafted inputs to the affected function, causing it to read outside the bounds of heap allocated data.
To fix this vulnerability, users should update their TensorFlow CPU package to one of the fixed versions (2.5.0, 2.4.2, 2.3.3, 2.2.3, or 2.1.4). This can be done by running the command: pip install --upgrade tensorflow-cpu==<fixed_version>.
No specific workaround has been mentioned for this vulnerability.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-cpu.
