CVE-2021-37672: TensorFlow GPU - Out-of-Bounds Read Vulnerability
Summary
CVE-2021-37672: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-gpu across 3 version ranges.
>= 2.3.0, < 2.3.4
>= 2.4.0, < 2.4.3
< a4e138660270e7599793fa438cd7b2fc2ce215a6
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-cpu · PyPI
The same CVE-2021-37672 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.
Check if this vulnerability affects your application
Upload a manifest or SBOM and we'll show whether you're affected, plus every other risky package in the same scan.
No signup required · Results in about 60 seconds
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 GPU is a variant of TensorFlow that leverages GPU acceleration for faster computation. It is commonly used in scenarios where large-scale machine learning tasks require significant computational power. However, a vulnerability has been discovered in TensorFlow GPU that could potentially allow an attacker to read data from outside the bounds of heap allocated memory.
Vulnerability Detail
The vulnerability exists in the tf.raw_ops.SdcaOptimizerV2 function of TensorFlow GPU. By sending specially crafted illegal arguments, an attacker can exploit the lack of bounds checking in the implementation. Specifically, the length of example_labels is not properly validated against the number of examples. This oversight allows an attacker to read data beyond the intended boundaries of heap allocated memory. The impact of this vulnerability could range from leaking sensitive information to potential remote code execution.
How To Fix
Latest published version: 2.12.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-gpu==2.12.0"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-gpu==2.3.4" -
>= 2.4.0, < 2.4.32.4.3pip install --upgrade "tensorflow-gpu==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 provided for this vulnerability. However, users can minimize the risk by ensuring they have the latest patched version of TensorFlow GPU installed. Additionally, it is recommended to follow best practices for secure coding and input validation to reduce the likelihood of successful exploitation.
Conclusion
In conclusion, TensorFlow GPU is vulnerable to an out-of-bounds read issue that could allow an attacker to read data from outside the intended boundaries of heap allocated memory. This vulnerability has been addressed in the latest versions of TensorFlow GPU, and users are strongly advised to update to the patched versions to mitigate the risk. For further support and assistance in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow GPU is a variant of TensorFlow that utilizes GPU acceleration for faster machine learning computations. It is commonly used in scenarios where large-scale machine learning tasks require significant computational power.
The vulnerability allows an attacker to read data from outside the bounds of heap allocated memory. This could potentially lead to the leakage of sensitive information or even remote code execution.
To fix this vulnerability, it is recommended to update TensorFlow GPU to version 2.6.0 or later. The issue has been patched in the latest versions of TensorFlow GPU.
Yes, older versions of TensorFlow GPU, specifically versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and 2.5.0 to 2.5.1, are affected by this vulnerability. It is important to update to the latest patched version to mitigate the risk.
While there is no specific workaround provided, users can minimize the risk by ensuring they have the latest patched version of TensorFlow GPU installed and following secure coding practices.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
