CVE-2021-29611: TensorFlow GPU Incomplete Validation in SparseReshape
Summary
CVE-2021-29611: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-gpu across 5 version ranges.
< 1d04d7d93f4ed3854abf75d6b712d72c3f70d6b6
< 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 · PyPI
- tensorflow · PyPI
- tensorflow-cpu · PyPI
The same CVE-2021-29611 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 GPU is a variant of TensorFlow that leverages the power of GPUs for accelerated computation. It allows users to train and run machine learning models on GPUs, enabling faster and more efficient processing of large datasets. However, TensorFlow GPU is vulnerable to an incomplete validation issue in the SparseReshape functionality.
Vulnerability Detail
The vulnerability in TensorFlow GPU arises from incomplete validation in the SparseReshape functionality. This vulnerability can be exploited to trigger a denial of service (DoS) condition by causing a CHECK failure. The implementation of SparseReshape does not validate the input arguments to ensure that they specify a valid sparse tensor. As a result, an attacker can provide malicious input that triggers the CHECK failure, leading to a crash or termination of the TensorFlow GPU application. This vulnerability can be particularly problematic in scenarios where TensorFlow GPU is used in critical systems or production environments.
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 workaround, users can avoid the vulnerability by refraining from using the SparseReshape functionality in TensorFlow GPU until they have updated to a patched version. If the functionality is essential for their use case, users should prioritize updating to the latest patched version as soon as possible.
Conclusion
The vulnerability in TensorFlow GPU's SparseReshape functionality can lead to a denial of service condition in affected applications. To mitigate this vulnerability, users should update their TensorFlow GPU installations to one of the patched versions: 2.5.0, 2.4.2, or 2.3.3. It is recommended to prioritize the update process, especially in critical systems or production environments. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow GPU is a variant of TensorFlow that utilizes GPUs for accelerated computation, enabling faster and more efficient processing of machine learning models.
SparseReshape is a functionality in TensorFlow GPU that allows users to reshape sparse tensors, which are tensors with a significant number of zero values.
The vulnerability can be exploited to trigger a denial of service condition in TensorFlow GPU applications, potentially leading to crashes or termination of the application.
The vulnerability affects TensorFlow GPU versions 2.4.0, 2.4.2, 2.3.0, 2.3.3, 2.2.0, 2.2.3, 2.1.4, and all versions prior to 2.1.4.
Users can update TensorFlow GPU by using the pip package manager and running the command 'pip install --upgrade tensorflow-gpu'. Alternatively, users can follow the specific update procedures provided by their installation method or package manager.
References
Related vulnerabilities
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