CVE-2021-37665: TensorFlow - Incomplete Validation in MKL Implementation of Requantization
Summary
CVE-2021-37665: Incomplete validation in MKL requantization in TensorFlow. Affects tensorflow across 3 version ranges.
>= 2.3.0, < 2.3.4
>= 2.4.0, < 2.4.3
>= 2.5.0, < 2.5.1
Fixed in 2.3.4, 2.4.3 or 2.5.1, 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-2021-37665 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 for building and deploying machine learning models. TensorFlow is widely used in various industries and research fields for tasks such as image recognition, natural language processing, and recommendation systems. However, TensorFlow is vulnerable to an incomplete validation issue in the MKL (Math Kernel Library) implementation of requantization.
Vulnerability Detail
The vulnerability arises due to incomplete validation in the MKL implementation of requantization in TensorFlow. This vulnerability allows an attacker to trigger undefined behavior by binding a reference to a null pointer or accessing data outside the bounds of heap allocated arrays. The issue is present in the MklRequantizationRangePerChannelOp and MklRequantizePerChannelOp implementations, where the dimensions of the input tensor and all input arguments are not fully validated. This can lead to potential memory corruption and arbitrary code execution.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
>= 2.3.0, < 2.3.42.3.4 -
>= 2.4.0, < 2.4.32.4.3 -
>= 2.5.0, < 2.5.12.5.1
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. It is advised to apply the patches and update to the fixed versions of TensorFlow as mentioned in the 'how_to_fix' section.
Conclusion
The incomplete validation vulnerability in the MKL implementation of requantization in TensorFlow can lead to memory corruption and arbitrary code execution. It is crucial to update to the patched versions of TensorFlow (2.6.0 or the cherrypicked versions) to mitigate this vulnerability. For further support on vulnerability remediation, you can contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open source platform for machine learning that provides a comprehensive ecosystem for building and deploying machine learning models.
This vulnerability in TensorFlow can allow an attacker to trigger undefined behavior and potentially execute arbitrary code, leading to memory corruption and other security risks.
The vulnerability affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, 2.5.0, 2.6.0-rc0, 2.6.0-rc1, and 2.6.0-rc2.
To fix this vulnerability, it is recommended to update to the patched versions of TensorFlow (2.6.0 or the cherrypicked versions for older releases) that include the necessary fixes.
No specific workaround is provided for this vulnerability. Applying the patches and updating to the fixed versions is the recommended solution.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
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