CVE-2021-37651: TensorFlow FractionalAvgPoolGrad Heap Buffer Overflow
Summary
CVE-2021-37651: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-cpu across 3 version ranges.
< 0f931751fb20f565c4e94aa6df58d54a003cdb30
>= 2.3.0, < 2.3.4
>= 2.4.0, < 2.4.3
Fixed in 2.3.4 or 2.4.3, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-37651 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 computer vision, natural language processing, and deep learning. However, a vulnerability has been discovered in TensorFlow that affects the tf.raw_ops.FractionalAvgPoolGrad implementation.
Vulnerability Detail
The vulnerability in TensorFlow's tf.raw_ops.FractionalAvgPoolGrad implementation allows an attacker to access data outside the bounds of heap allocated buffers. The issue arises due to the lack of validation for non-empty input tensors. By constructing an empty EigenDoubleMatrixMap and accessing it with out-of-bounds indices, an attacker can trigger a heap buffer overflow. This vulnerability can potentially lead to arbitrary code execution or denial of service attacks.
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.
-
>= 2.3.0, < 2.3.42.3.4pip install --upgrade "tensorflow-cpu==2.3.4" -
>= 2.4.0, < 2.4.32.4.3pip install --upgrade "tensorflow-cpu==2.4.3"
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. However, users can mitigate the risk by ensuring they are using the patched versions of TensorFlow.
Conclusion
The vulnerability in TensorFlow's tf.raw_ops.FractionalAvgPoolGrad implementation can lead to heap buffer overflow and potential code execution or denial of service attacks. It is crucial for users to update their TensorFlow installations to the patched versions to mitigate the risk. For further support on vulnerability remediation, you can reach out to DevNack.com.
See every vulnerability recorded for tensorflow-cpu →
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 allow an attacker to access data outside the bounds of heap allocated buffers, potentially leading to arbitrary code execution or denial of service attacks.
The vulnerability affects TensorFlow versions 2.3.0 and above. Specifically, TensorFlow 2.3.4, TensorFlow 2.4.3, and TensorFlow 2.5.1 are known to be vulnerable.
To fix this vulnerability, update your TensorFlow installation to the patched versions: TensorFlow 2.6.0, TensorFlow 2.5.1, TensorFlow 2.4.3, or TensorFlow 2.3.4.
There is no specific workaround mentioned for this vulnerability. However, using the patched versions of TensorFlow can mitigate the risk.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-cpu.
