CVE-2021-29601: TensorFlow GPU - Integer Overflow in TFLite Concatenation
Summary
CVE-2021-29601: TensorFlow is an end-to-end open source platform for machine learning. Affects tensorflow-gpu across 5 version ranges.
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
< 4253f96a58486ffe84b61c0415bb234a4632ee73
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-cpu · PyPI
The same CVE-2021-29601 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 community resources that enable researchers and developers to build and deploy machine learning models. TensorFlow GPU is a variant of TensorFlow that leverages GPU acceleration for faster computation. The TFLite implementation of concatenation in TensorFlow GPU is vulnerable to an integer overflow issue. This vulnerability arises due to the use of int to represent tensor dimensions in TFLite, while TensorFlow uses int64. As a result, when converting valid TensorFlow models to TFLite format, an attacker can craft a malicious model that triggers an integer overflow by overflowing the dimensions of one of the concatenation inputs.
Vulnerability Detail
The integer overflow vulnerability in TensorFlow GPU's TFLite concatenation implementation can be exploited by an attacker to cause unexpected behavior or crashes in applications that use TensorFlow GPU. By carefully crafting a malicious model, an attacker can trigger the overflow of tensor dimensions, leading to memory corruption or other undefined behavior. This vulnerability affects TensorFlow GPU versions 2.1.4 and above, including versions 2.2.3, 2.3.3, and 2.4.2. The issue has been fixed in TensorFlow 2.5.0, and patches have been backported to versions 2.1.4, 2.2.3, 2.3.3, and 2.4.2 to address the vulnerability.
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.
-
< 2.1.42.1.4pip install --upgrade "tensorflow-gpu==2.1.4" -
>= 2.2.0, < 2.2.32.2.3pip install --upgrade "tensorflow-gpu==2.2.3" -
>= 2.3.0, < 2.3.32.3.3pip install --upgrade "tensorflow-gpu==2.3.3" -
>= 2.4.0, < 2.4.22.4.2pip install --upgrade "tensorflow-gpu==2.4.2"
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Workaround
There is no known workaround for this vulnerability. It is recommended to update TensorFlow GPU to the latest version or apply the provided patches to mitigate the risk.
Conclusion
The integer overflow vulnerability in TensorFlow GPU's TFLite concatenation implementation can lead to memory corruption or other unexpected behavior. It is crucial for users of TensorFlow GPU to update to version 2.5.0 or apply the provided patches to mitigate the risk. For further assistance or support 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 computation in machine learning tasks.
TFLite is the TensorFlow Lite library, which is designed for mobile and embedded devices to run TensorFlow models efficiently.
An attacker can exploit this vulnerability by crafting a malicious TensorFlow model that triggers an integer overflow in the TFLite concatenation implementation, leading to memory corruption or other unexpected behavior.
Versions 2.1.4 and above of TensorFlow GPU are affected, including versions 2.2.3, 2.3.3, and 2.4.2.
Yes, the vulnerability has been fixed in TensorFlow GPU version 2.5.0. Patches have also been backported to versions 2.1.4, 2.2.3, 2.3.3, and 2.4.2.
References
Related vulnerabilities
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