CVE-2021-29522: TensorFlow - Division by Zero Denial of Service
Summary
CVE-2021-29522: Division by 0 in Conv3DBackprop*. 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-2021-29522 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 industries and research domains due to its flexibility and scalability. However, a vulnerability has been discovered in TensorFlow that can lead to a denial of service attack.
Vulnerability Detail
The vulnerability, identified as CVE-2021-29522, resides in the tf.raw_ops.Conv3DBackprop* operations of TensorFlow. These operations fail to validate the input tensors, allowing an attacker to trigger a division by zero error. The issue arises from the implementation of the operations, which does not check if the divisor used in computing the shard size is zero. By controlling the input sizes, an attacker can exploit this vulnerability to cause a denial of service attack.
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
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Workaround
There is no known workaround for this vulnerability. It is crucial to apply the provided fix or update to the patched versions of TensorFlow to ensure the security and stability of the machine learning environment.
Conclusion
The identified vulnerability in TensorFlow can be exploited by an attacker to cause a denial of service attack. To ensure the security and stability of TensorFlow-based machine learning systems, it is crucial to update to the fixed versions of TensorFlow or apply the provided patches. 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 a comprehensive set of tools and resources for developing and deploying machine learning models.
This vulnerability allows an attacker to trigger a division by zero error, leading to a denial of service attack. It can disrupt the availability and functionality of TensorFlow-based machine learning systems.
The vulnerability affects TensorFlow versions 0 and 2.1.4 to 2.4.2. It has been fixed in TensorFlow 2.5.0 and will also be patched in TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3, and TensorFlow 2.1.4.
No, there is no known workaround for this vulnerability. It is essential to update to the fixed versions of TensorFlow to mitigate the risk.
You can find more information about this vulnerability in the official TensorFlow security advisory or by contacting the TensorFlow support team.
References
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