CVE-2022-23569: Denial of Service Vulnerability in TensorFlow-GPU
Summary
CVE-2022-23569: Tensorflow is an Open Source Machine Learning Framework. Affects tensorflow-gpu across 2 version ranges.
< 2.5.3
>= 2.6.0, < 2.6.3
Fixed in 2.5.3 or 2.6.3, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow-cpu · PyPI
The same CVE-2022-23569 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 widely used for developing and deploying machine learning models. It provides a comprehensive ecosystem for building and training neural networks. TensorFlow-GPU is a variant of TensorFlow that utilizes GPU acceleration for faster computation. It is commonly used in scenarios where large-scale data processing and complex model training are required. However, TensorFlow-GPU is susceptible to a denial of service vulnerability that can be triggered by certain operations in TensorFlow.
Vulnerability Detail
The vulnerability in TensorFlow-GPU allows an attacker to cause a denial of service by exploiting CHECK-fails, which are assertion failures within the framework. By crafting specific inputs or executing certain operations, an attacker can trigger these assertion failures, leading to the termination of the TensorFlow-GPU process. This vulnerability is similar to a previously reported issue (TFSA-2021-198) and has been addressed in multiple GitHub commits. The fixes for this vulnerability have been included in TensorFlow versions 2.8.0, 2.7.1, 2.6.3, and 2.5.3.
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.
-
< 2.5.32.5.3pip install --upgrade "tensorflow-gpu==2.5.3" -
>= 2.6.0, < 2.6.32.6.3pip install --upgrade "tensorflow-gpu==2.6.3"
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Workaround
There is no specific workaround provided for this vulnerability. The recommended approach is to apply the available patches and update TensorFlow-GPU to a fixed version.
Conclusion
The denial of service vulnerability in TensorFlow-GPU can be mitigated by updating to the patched versions mentioned above. It is crucial to keep your machine learning frameworks and dependencies up to date to ensure the security and stability of your applications. For further support on vulnerability remediation, you can reach out to DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow-GPU is a variant of TensorFlow that utilizes GPU acceleration for faster computation. It is commonly used in scenarios where large-scale data processing and complex model training are required.
A denial of service vulnerability allows an attacker to disrupt the normal functioning of a system or application, rendering it unavailable to legitimate users.
The vulnerability can be exploited by triggering CHECK-fails, which are assertion failures within the TensorFlow-GPU framework. By crafting specific inputs or executing certain operations, an attacker can cause these assertion failures and terminate the TensorFlow-GPU process.
No, not all versions of TensorFlow-GPU are affected. The vulnerability exists in versions 2.5.2 and earlier. It has been fixed in versions 2.5.3, 2.6.3, 2.7.1, and 2.8.0.
You can use Vulert, a vulnerability monitoring tool, to check if your application is affected by this vulnerability. Vulert analyzes your Pipfile.lock file and provides real-time alerts for open-source vulnerabilities. Visit the Vulert playground at Vulert Scanner to check if your application is affected without any signup or installation.
References
Related vulnerabilities
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