CVE-2021-41208: TensorFlow Boosted Trees API Denial of Service and Heap Buffer Overflow
Summary
CVE-2021-41208: TensorFlow is an open source platform for machine learning. Affects tensorflow-cpu across 5 version ranges.
< 2.4.4
>= 2.5.0, < 2.5.2
>= 2.6.0, < 2.6.1
>= 2.7.0rc0, < 2.7.0
< 5c8c9a8bfe750f9743d0c859bae112060b216f5c
Fixed in 4 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-41208 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 open source platform for machine learning. It provides a wide range of tools and libraries for building and deploying machine learning models. The Boosted Trees API in TensorFlow allows users to train and use boosted tree models for various tasks. Boosted trees are an ensemble learning method that combines multiple decision trees to make predictions. They are widely used in machine learning for tasks such as classification and regression. However, in certain versions of TensorFlow, the Boosted Trees API is vulnerable to denial of service attacks and heap buffer overflow. These vulnerabilities can be exploited by attackers to cause the application to crash, read and write from heap buffers, and abuse undefined behavior. It is important to note that the Boosted Trees implementation in TensorFlow is unmaintained, and it is recommended to no longer use these APIs.
Vulnerability Detail
The vulnerability in the Boosted Trees API in TensorFlow arises from missing validation in the code. This allows attackers to trigger denial of service attacks by dereferencing null pointers or causing CHECK failures. It also enables them to abuse undefined behavior by binding references to null pointers. Additionally, depending on the API and arguments used, attackers can read and write from heap buffers. These vulnerabilities can have serious consequences, including application crashes, data corruption, and potential remote code execution. The affected versions of TensorFlow include 2.4.4, 2.5.2, 2.6.1, and 2.7.0rc0. The fix for this vulnerability will be included in TensorFlow 2.7.0, and patches will be released for the affected versions.
How To Fix
Latest published version: 2.21.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-cpu==2.21.0"Upgrading to it may include breaking changes; the versions below are the minimum fixed release for this vulnerability.
If you can't move to the latest
The minimum fixed version depends on the release line you run.
-
< 2.4.42.4.4pip install --upgrade "tensorflow-cpu==2.4.4" -
>= 2.5.0, < 2.5.22.5.2pip install --upgrade "tensorflow-cpu==2.5.2" -
>= 2.6.0, < 2.6.12.6.1pip install --upgrade "tensorflow-cpu==2.6.1" -
>= 2.7.0rc0, < 2.7.02.7.0pip install --upgrade "tensorflow-cpu==2.7.0"
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Workaround
There is no specific workaround mentioned for this vulnerability. However, as a general best practice, it is recommended to follow secure coding practices and keep software dependencies up to date. Regularly monitoring for security updates and applying them promptly can help mitigate the risk of vulnerabilities.
Conclusion
The Boosted Trees API in TensorFlow is vulnerable to denial of service attacks and heap buffer overflow. It is recommended to upgrade to the latest version of TensorFlow (2.7.0) or apply the available patches for the affected versions (2.4.4, 2.5.2, and 2.6.1) to mitigate these vulnerabilities. Additionally, it is advised to no longer use the Boosted Trees APIs in TensorFlow, as they are unmaintained and pose a security risk. For further support on vulnerability remediation, you can contact DevNack.com.
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FAQ
TensorFlow is an open source platform for machine learning that provides tools and libraries for building and deploying machine learning models.
Boosted trees are an ensemble learning method that combines multiple decision trees to make predictions. They are widely used in machine learning for tasks such as classification and regression.
The consequences of this vulnerability include denial of service attacks, heap buffer overflow, application crashes, data corruption, and potential remote code execution.
The affected versions of TensorFlow include 2.4.4, 2.5.2, 2.6.1, and 2.7.0rc0.
To fix this vulnerability, it is recommended to upgrade to the latest version of TensorFlow (2.7.0) or apply the available patches for the affected versions (2.4.4, 2.5.2, and 2.6.1). It is also advised to no longer use the Boosted Trees APIs in TensorFlow.
References
Related vulnerabilities
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