CVE-2021-29514: Heap out of bounds write in tensorflow
Summary
CVE-2021-29514: Heap out of bounds write in RaggedBinCount. Affects tensorflow across 2 version ranges.
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
Fixed in 2.3.3 or 2.4.2, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-29514 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 machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building 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 heap buffer overflow.
Vulnerability Detail
The vulnerability, identified as CVE-2021-29514, exists in the RaggedBincount function of TensorFlow. If the splits argument of RaggedBincount does not specify a valid SparseTensor, an attacker can trigger a heap buffer overflow. By exploiting this vulnerability, an attacker can cause a read from outside the bounds of the splits tensor buffer, leading to potential code execution or information disclosure.
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.
-
>= 2.3.0, < 2.3.32.3.3pip install --upgrade "tensorflow==2.3.3" -
>= 2.4.0, < 2.4.22.4.2pip install --upgrade "tensorflow==2.4.2"
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 known workaround for this vulnerability. It is recommended to apply the patches or update to the patched versions of TensorFlow as mentioned in the 'how_to_fix' section.
Conclusion
The vulnerability in TensorFlow can be mitigated by updating to the patched versions or applying the provided patches. It is crucial to keep software dependencies up to date to ensure the security of machine learning models and applications. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
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 vulnerability allows an attacker to trigger a heap buffer overflow, potentially leading to code execution or information disclosure.
To fix this vulnerability, it is recommended to update TensorFlow to version 2.5.0 or apply the provided patches.
There are no known workarounds for this vulnerability. It is recommended to update to the patched versions of TensorFlow.
For more information, please refer to the TensorFlow security guide and the GitHub commit mentioned in the 'how_to_fix' section.
References
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