CVE-2021-37642: TensorFlow - Division by 0 Error in ResourceScatterDiv
Summary
CVE-2021-37642: Division by 0 in ResourceScatterDiv 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-2021-37642 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 set of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. However, certain versions of TensorFlow are vulnerable to a division by 0 error in the implementation of tf.raw_ops.ResourceScatterDiv.
Vulnerability Detail
The vulnerability arises from a flaw in the implementation of tf.raw_ops.ResourceScatterDiv in TensorFlow. The affected code fails to handle the division by 0 case separately, leading to a potential division by 0 error. An attacker could exploit this vulnerability to cause a denial of service (DoS) condition or potentially execute arbitrary code. The vulnerability has been assigned the identifier CVE-2021-37642.
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
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Workaround
There is no specific workaround mentioned for this vulnerability.
Conclusion
In conclusion, TensorFlow versions 2.3.0 to 2.6.0-rc2 are vulnerable to a division by 0 error in the implementation of tf.raw_ops.ResourceScatterDiv. This vulnerability can potentially lead to a denial of service (DoS) condition or arbitrary code execution. It is crucial to update to TensorFlow 2.6.0 or apply the necessary patches to mitigate the risk. For further support on vulnerability remediation, you can reach out to 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 vulnerability could potentially lead to a division by 0 error, causing a denial of service (DoS) condition or enabling arbitrary code execution.
You can use the Vulert Playground at Vulert Scanner to check if your application is affected by this vulnerability. Simply upload your manifest file, such as package-lock.json, and Vulert will provide real-time alerts for open-source vulnerabilities.
No, only certain versions of TensorFlow are affected. The vulnerability was introduced in TensorFlow 2.3.0 and fixed in TensorFlow 2.6.0. Versions 2.3.4, 2.4.3, 2.5.0, 2.6.0-rc0, 2.6.0-rc1, and 2.6.0-rc2 are affected.
To fix the vulnerability, you should update your TensorFlow installation to version 2.6.0 or apply the respective patches for versions 2.5.1, 2.4.3, and 2.3.4.
References
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