CVE-2021-29547: TensorFlow - Out-of-Bounds Access in QuantizedBatchNormWithGlobalNormalization
Summary
CVE-2021-29547: Heap out of bounds 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-2021-29547 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, a vulnerability has been discovered in TensorFlow that can lead to a denial of service attack.
Vulnerability Detail
The vulnerability (CVE-2021-29547) in TensorFlow arises from an out-of-bounds access issue in the tf.raw_ops.QuantizedBatchNormWithGlobalNormalization function. The implementation assumes that the inputs are not empty, but if any of the inputs is empty, accessing the element at index 0 results in accessing data outside of bounds. This can cause a segmentation fault and lead to a denial of service. The vulnerability affects versions 0 and 2.1.4 to 2.4.2 of the TensorFlow package.
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 recommended to update to the fixed versions of TensorFlow as soon as they are available.
Conclusion
The vulnerability in TensorFlow (CVE-2021-29547) can lead to a denial of service attack. Users are advised to update their TensorFlow installations to the fixed versions as soon as they are available. Regularly checking for updates and applying them promptly is recommended to ensure the security of TensorFlow deployments. For further support on 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 resources for developing and deploying machine learning models.
The vulnerability can lead to a denial of service attack by causing a segmentation fault in TensorFlow.
Versions 0 and 2.1.4 to 2.4.2 of TensorFlow are affected by this vulnerability.
No, there is no known workaround for this vulnerability. It is recommended to update to the fixed versions of TensorFlow.
Once the fixed versions are available, you can update your TensorFlow installation by following the official documentation and guidelines provided by the TensorFlow project.
References
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