CVE-2021-29552: TensorFlow - Denial of Service Vulnerability
Summary
CVE-2021-29552: CHECK-failure in UnsortedSegmentJoin. 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-2021-29552 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 applications, including image recognition, natural language processing, and recommendation systems. However, a vulnerability has been discovered in TensorFlow that can lead to a denial of service attack.
Vulnerability Detail
The vulnerability in TensorFlow is caused by a flaw in the UnsortedSegmentJoin function. An attacker can exploit this vulnerability by controlling the values of the num_segments tensor argument. The implementation assumes that the num_segments tensor is a valid scalar, but if the tensor is empty, the validation check fails and the process terminates, resulting in a denial of service. This vulnerability can be exploited remotely by an attacker with the ability to control the input values of the num_segments tensor.
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
There is no specific workaround mentioned for this vulnerability.
Conclusion
The vulnerability in TensorFlow can lead to a denial of service attack if exploited by an attacker. It is important to upgrade to the latest version of TensorFlow or apply the provided patches to mitigate this vulnerability. Regularly updating your TensorFlow installation and following best practices for secure machine learning model deployment are also recommended. For further support on vulnerability remediation, you can 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.
This vulnerability can be exploited by an attacker to cause a denial of service, potentially disrupting the availability of TensorFlow-based applications.
Yes, this vulnerability can be exploited remotely by an attacker with the ability to control the input values of the num_segments tensor.
The recommended mitigation is to upgrade to the latest version of TensorFlow once the fix is available. In the meantime, you can apply the provided patches for the affected versions.
You can find more information about this vulnerability in the official TensorFlow documentation and the vulnerability database.
References
Related vulnerabilities
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