NULL Pointer Dereference and Access of Uninitialized Pointer in TensorFlow
Summary
GHSA-h6gw-r52c-724r: NULL Pointer Dereference and Access of Uninitialized Pointer in TensorFlow. Affects tensorflow across 3 version ranges.
< 2.5.3
>= 2.6.0, < 2.6.3
>= 2.7.0, < 2.7.1
Fixed in 2.5.3, 2.6.3 or 2.7.1, depending on the release line you run. How to fix ↓
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Background
TensorFlow is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. The boosted trees implementation in TensorFlow allows users to train and use gradient boosted decision trees for various tasks. However, this implementation has been found to have vulnerabilities that can lead to security issues.
Vulnerability Detail
The boosted trees implementation in TensorFlow lacks proper validation, which allows malicious users to read and write outside the bounds of heap allocated data. This can result in a NULL pointer dereference or access of uninitialized pointers, leading to denial of service attacks or triggering CHECK failures. These vulnerabilities have been reported by security researchers and have been assigned a CVSS score of 9.3, indicating a critical severity level. It is important to note that these vulnerabilities have been addressed in the latest versions of TensorFlow, and it is recommended to migrate to the TensorFlow Decision Forests project, which is actively maintained and supports more features.
How To Fix
Latest published version: 2.21.0 as of 22 Oct 2026
pip install --upgrade "tensorflow==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.
-
< 2.5.32.5.3pip install --upgrade "tensorflow==2.5.3" -
>= 2.6.0, < 2.6.32.6.3pip install --upgrade "tensorflow==2.6.3" -
>= 2.7.0, < 2.7.12.7.1pip install --upgrade "tensorflow==2.7.1"
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Workaround
There is no specific workaround provided for these vulnerabilities. The recommended approach is to update TensorFlow to the latest version or migrate to TensorFlow Decision Forests.
Conclusion
The vulnerabilities in TensorFlow's boosted trees implementation pose a critical security risk. It is crucial to update to the latest version of TensorFlow or migrate to TensorFlow Decision Forests to ensure the security and integrity of machine learning models. For further support and assistance in vulnerability remediation, please contact DevNack.com.
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FAQ
TensorFlow is an open-source machine learning framework developed by Google. It provides tools and resources for building and deploying machine learning models.
Boosted trees in TensorFlow are a machine learning technique that combines multiple decision trees to make predictions. It is used for tasks such as classification and regression.
The vulnerabilities in TensorFlow's boosted trees implementation can allow malicious users to read and write outside the bounds of allocated data, leading to denial of service attacks or CHECK failures.
To fix the vulnerabilities, it is recommended to update TensorFlow to version 2.8.0 or later. Alternatively, you can migrate to the TensorFlow Decision Forests project.
There is no specific workaround provided. It is recommended to update TensorFlow or migrate to TensorFlow Decision Forests.
References
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