CVE-2021-41225: TensorFlow - Use of Uninitialized Variable
Summary
CVE-2021-41225: A use of uninitialized value vulnerability in Tensorflow. Affects tensorflow across 3 version ranges.
>= 2.4.0, < 2.4.4
>= 2.5.0, < 2.5.2
>= 2.6.0, < 2.6.1
Fixed in 2.4.4, 2.5.2 or 2.6.1, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-41225 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 platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow's Grappler optimizer is responsible for optimizing the performance of machine learning models. However, a vulnerability has been discovered in TensorFlow's Grappler optimizer that involves the use of an uninitialized variable.
Vulnerability Detail
The vulnerability in TensorFlow's Grappler optimizer arises when the train_nodes vector, obtained from the saved model being optimized, does not contain a Dequeue node. In such cases, the dequeue_node variable is left uninitialized. This can lead to unpredictable behavior and potential security risks. The vulnerability has been assigned the identifier CVE-2021-41225 and has a severity rating of High. It affects multiple versions of TensorFlow, including versions 2.4.0 to 2.4.4, 2.5.0 to 2.5.2, 2.6.0 to 2.6.1, and 2.7.0-rc0 to 2.7.0-rc1.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
>= 2.4.0, < 2.4.42.4.4 -
>= 2.5.0, < 2.5.22.5.2 -
>= 2.6.0, < 2.6.12.6.1
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
There is no specific workaround provided for this vulnerability. However, users can follow general security best practices such as ensuring the integrity of the saved models and avoiding the use of uninitialized variables in their own code.
Conclusion
The vulnerability in TensorFlow's Grappler optimizer, identified as CVE-2021-41225, can lead to unpredictable behavior and potential security risks. It is crucial to update to the latest patched version of TensorFlow to mitigate this vulnerability. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open source platform for machine learning that provides tools and resources for developing and deploying machine learning models.
The Grappler optimizer in TensorFlow is responsible for optimizing the performance of machine learning models.
This vulnerability can lead to unpredictable behavior and potential security risks in TensorFlow when the dequeue_node variable is left uninitialized.
The vulnerability affects TensorFlow versions 2.4.0 to 2.4.4, 2.5.0 to 2.5.2, 2.6.0 to 2.6.1, and 2.7.0-rc0 to 2.7.0-rc1.
To fix this vulnerability, it is recommended to update to the latest patched version of TensorFlow, which includes the necessary fixes.
References
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