CVE-2021-37677: TensorFlow - Denial of Service Vulnerability
Summary
CVE-2021-37677: Missing validation in shape inference for Dequantize 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-2021-37677 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 could potentially lead to a denial of service attack.
Vulnerability Detail
The vulnerability exists in the shape inference code for tf.raw_ops.Dequantize in TensorFlow. The code is responsible for inferring the shape of tensors during the dequantization process. However, it fails to properly validate the axis argument, allowing an attacker to provide invalid values. This can trigger a segmentation fault (segfault) and result in a denial of service. The issue has been patched in the latest versions of TensorFlow.
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
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 to the patched versions of TensorFlow as mentioned in the 'how_to_fix' section.
Conclusion
The vulnerability in TensorFlow's shape inference code for tf.raw_ops.Dequantize can lead to a denial of service attack. It is crucial to update to the patched versions of TensorFlow to ensure the system is protected. For further assistance and support 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.
The vulnerability can lead to a denial of service attack by triggering a segmentation fault. This can result in the system becoming unresponsive or crashing.
The vulnerability affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, 2.5.0, and 2.6.0-rc0 to 2.6.0-rc2.
To fix this vulnerability, update TensorFlow to version 2.6.0 or later. The fix has also been backported to versions 2.5.1, 2.4.3, and 2.3.4.
No, there is no specific workaround available for this vulnerability. Updating to the patched versions is the recommended solution.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
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