CVE-2022-23583: TensorFlow - Denial of Service Vulnerability
Summary
CVE-2022-23583: CHECK-failures in binary ops in Tensorflow. Affects tensorflow across 3 version ranges.
< 2.5.3
>= 2.6.0, < 2.6.3
>= 2.7.0, < 2.7.1
Fixed in 2.5.3, 2.6.3 or 2.7.1, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-gpu · 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 developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. This vulnerability affects versions 2.5.2, 2.6.0, 2.6.1, and 2.7.0 of TensorFlow.
Vulnerability Detail
A malicious user can exploit this vulnerability by modifying a SavedModel in such a way that it triggers CHECK failures when any binary operation is performed. This occurs when the protobuf part corresponding to the tensor arguments is altered, causing the dtype to no longer match the expected dtype of the operation. As a result, the binary operator receives corrupted data due to type confusion, leading to a denial of service. While most cases result in silent failures, there have been instances where this vulnerability causes a CHECK crash, further exacerbating the denial of service.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
< 2.5.32.5.3 -
>= 2.6.0, < 2.6.32.6.3 -
>= 2.7.0, < 2.7.12.7.1
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Workaround
There is currently no known workaround for this vulnerability. It is recommended to apply the official fix as soon as it is released.
Conclusion
The vulnerability in TensorFlow can lead to a denial of service if exploited by a malicious user. It is crucial for users to update their TensorFlow installations to the latest patched versions once they become available. For further support and assistance in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open-source machine learning framework developed by Google. It provides a wide range of tools and resources for building and deploying machine learning models.
A SavedModel is a serialized format for storing TensorFlow models. It allows models to be easily loaded and used for inference or further training.
By modifying a SavedModel to trigger CHECK failures, a malicious user can cause a denial of service by corrupting the data passed to binary operators.
The primary consequence of this vulnerability is a denial of service, which can impact the availability and reliability of TensorFlow-based applications.
No, only versions 2.5.2, 2.6.0, 2.6.1, and 2.7.0 are affected. The vulnerability will be fixed in TensorFlow 2.8.0 and backported to earlier versions.
References
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