CVE-2021-37659: TensorFlow GPU - Null Pointer Dereference in Binary Cwise Operations
Summary
CVE-2021-37659: 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
< 93f428fd1768df147171ed674fee1fc5ab8309ec
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-2021-37659 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 for building and deploying machine learning models. TensorFlow GPU is a specific package that enables GPU acceleration for TensorFlow. It allows users to leverage the power of GPUs to accelerate their machine learning computations. However, the TensorFlow GPU package is vulnerable to a null pointer dereference in binary cwise operations.
Vulnerability Detail
The vulnerability in TensorFlow GPU arises from a flaw in the implementation of binary cwise operations. These operations are used in various computations in machine learning models. The implementation assumes that the two inputs to the binary cwise operations have the same number of elements, but it does not check for this condition. As a result, when the eigen functor executes, it triggers heap out-of-bounds reads and undefined behavior by binding to a null pointer. This vulnerability can be exploited by an attacker to cause undefined behavior and potentially lead to a denial of service or arbitrary 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 known workaround for this vulnerability. It is recommended to apply the patch or update to the fixed versions of TensorFlow GPU.
Conclusion
The vulnerability in TensorFlow GPU allows for a null pointer dereference in binary cwise operations, potentially leading to undefined behavior and security risks. Users are strongly advised to update their TensorFlow GPU package to the patched versions or apply the provided fix. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow GPU is a package that enables GPU acceleration for TensorFlow, allowing users to leverage the power of GPUs for faster machine learning computations.
A null pointer dereference occurs when a program attempts to access or manipulate memory through a null pointer, resulting in undefined behavior and potential crashes or security vulnerabilities.
Binary cwise operations in TensorFlow are element-wise operations performed on two tensors of the same shape, such as addition, subtraction, multiplication, and division.
This vulnerability can lead to undefined behavior, which may result in crashes, denial of service, or even arbitrary code execution. Attackers could potentially exploit this vulnerability to compromise the integrity and availability of machine learning models.
You can use Vulert's playground at Vulert Scanner to check if your application is affected by this vulnerability. Vulert monitors and alerts in real-time for open-source vulnerabilities, using only the Pipfile.lock file.
References
Related vulnerabilities
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