CVE-2022-21739: TensorFlow - QuantizedMaxPool Reference Binding to Null Pointer
Summary
CVE-2022-21739: Null pointer dereference 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-2022-21739 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 for building and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. It offers a range of functionalities and tools for training and deploying machine learning models efficiently. However, a vulnerability has been discovered in the implementation of QuantizedMaxPool in TensorFlow.
Vulnerability Detail
The vulnerability in TensorFlow's QuantizedMaxPool implementation allows user-controlled inputs to trigger a reference binding to a null pointer. This undefined behavior can lead to unexpected program execution and potential security risks. Attackers may exploit this vulnerability to cause denial of service (DoS) attacks or execute arbitrary code. The issue has been identified and will be fixed in TensorFlow 2.8.0. Additionally, the fix will be backported to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these versions are also affected and within the supported range.
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
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 available for this vulnerability. It is recommended to update TensorFlow to the fixed versions mentioned above.
Conclusion
The vulnerability in TensorFlow's QuantizedMaxPool implementation can have serious security implications. It is crucial to update TensorFlow to the fixed versions mentioned above to ensure the security and stability of machine learning models. For further support on vulnerability remediation, you can contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem for building and deploying machine learning models.
The vulnerability in TensorFlow's QuantizedMaxPool implementation can lead to unexpected program execution and potential security risks. Attackers may exploit this vulnerability to cause denial of service (DoS) attacks or execute arbitrary code.
The vulnerability affects TensorFlow versions 2.5.2, 2.6.0, and 2.7.0. The fix will be included in TensorFlow 2.8.0 and backported to TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3.
To mitigate this vulnerability, it is recommended to update TensorFlow to the fixed versions: TensorFlow 2.8.0, TensorFlow 2.7.1, TensorFlow 2.6.3, or TensorFlow 2.5.3.
For more information about this vulnerability, you can refer to the official GitHub repository of TensorFlow or the vulnerability database provided by Bitnami.
References
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