CVE-2022-23583: Denial of Service in tensorflow-gpu
Summary
CVE-2022-23583: Tensorflow is an Open Source Machine Learning Framework. Affects tensorflow-gpu across 3 version ranges.
< 2.5.3
>= 2.6.0, < 2.6.3
< a7c02f1a9bbc35473969618a09ee5f9f5d3e52d9
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-2022-23583 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 that provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. It is widely used in various domains for tasks such as image recognition, natural language processing, and deep learning. The tensorflow-gpu package is a PyPI distribution of TensorFlow specifically optimized for GPU acceleration. It allows users to leverage the power of GPUs to accelerate their machine learning workflows.
Vulnerability Detail
A malicious user can exploit a vulnerability in tensorflow-gpu to cause a denial of service (DoS) attack. The vulnerability arises from a type confusion issue in the handling of SavedModel objects. By modifying the protobuf part corresponding to the tensor arguments in a SavedModel, an attacker can cause a mismatch between the expected data type (dtype) and the actual data type of the tensors. This can lead to corrupted data being passed to binary operators, resulting in silent failures or, in some cases, triggering a CHECK crash. The impact of this vulnerability can range from disrupting machine learning workflows to potentially crashing the application or system.
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
As a workaround, users can consider validating the SavedModel objects before usage to ensure that the dtype of the tensors matches the expected data type. This can be done by inspecting the protobuf structure of the SavedModel and verifying the dtype field of the tensor arguments. If any inconsistencies are found, the SavedModel should be treated as potentially malicious and not used.
Conclusion
The vulnerability in tensorflow-gpu can be exploited by a malicious user to cause a denial of service attack. It is crucial for users to upgrade to the latest version of tensorflow-gpu and monitor their applications for vulnerabilities. Tools like Vulert can help in identifying and addressing open-source vulnerabilities in real-time. 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 PyPI distribution of TensorFlow that is optimized for GPU acceleration. It allows users to leverage the power of GPUs for faster machine learning computations.
A SavedModel is a serialization format in TensorFlow that allows users to save and restore trained models. It encapsulates the model's architecture, variables, and computation graph.
An attacker can exploit this vulnerability by modifying the protobuf part of a SavedModel to introduce a type confusion between the expected and actual data types of tensors. This can lead to corrupted data being passed to binary operators, potentially causing a denial of service or crashing the application.
The impact of this vulnerability can range from disrupting machine learning workflows to potentially crashing the application or system. It can lead to silent failures or trigger a CHECK crash in certain scenarios.
To protect your application, it is recommended to upgrade to the latest available version of tensorflow-gpu, which includes the fix for this vulnerability. Additionally, you can monitor your application for vulnerabilities using tools like Vulert, which provides real-time alerts for open-source vulnerabilities.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
