CVE-2021-29548: TensorFlow - Division by Zero Denial of Service
Summary
CVE-2021-29548: Division by 0 in QuantizedBatchNormWithGlobalNormalization. Affects tensorflow across 4 version ranges.
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
Fixed in 4 releases; the minimum fixed version depends 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-2021-29548 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 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, a vulnerability has been discovered in TensorFlow that can lead to a division by zero error and denial of service.
Vulnerability Detail
The vulnerability exists in the tf.raw_ops.QuantizedBatchNormWithGlobalNormalization function of TensorFlow. An attacker can exploit this vulnerability by providing malicious input that triggers a runtime division by zero error. This can result in a denial of service, causing the affected TensorFlow application to crash or become unresponsive. The vulnerability arises due to the lack of proper validation of constraints specified in the op's contract. The impact of this vulnerability can be significant, as it can disrupt the availability and functionality of TensorFlow applications.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
< 2.1.42.1.4 -
>= 2.2.0, < 2.2.32.2.3 -
>= 2.3.0, < 2.3.32.3.3 -
>= 2.4.0, < 2.4.22.4.2
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
No specific workaround has been provided for this vulnerability. It is recommended to apply the fix by updating TensorFlow to the patched versions mentioned above.
Conclusion
The identified vulnerability in TensorFlow can result in a division by zero error and denial of service. To mitigate this vulnerability, it is crucial to update TensorFlow to the fixed versions. For further support and assistance in 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 libraries for developing and deploying machine learning models.
The vulnerability can lead to a division by zero error and denial of service in TensorFlow applications, causing them to crash or become unresponsive.
To fix this vulnerability, update your TensorFlow installation to one of the patched versions: TensorFlow 2.5.0, TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3, or TensorFlow 2.1.4.
No specific workarounds have been provided. It is recommended to update TensorFlow to the patched versions.
You can find more information about this vulnerability in the official TensorFlow documentation and the vulnerability database.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
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