CVE-2020-15196: TensorFlow GPU - Heap Buffer Overflow
Summary
CVE-2020-15196: In Tensorflow version 2.3.0, the SparseCountSparseOutput and RaggedCountSparseOutput implementations don't validate that the weights tensor has the same shape as the data. Affects tensorflow-gpu across 2 version ranges.
< 2.3.1
< 3cbb917b4714766030b28eba9fb41bb97ce9ee02
Fixed in 3cbb917b4714766030b28eba9fb41bb97ce9ee02. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow · PyPI
The same CVE-2020-15196 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 widely used for developing and training deep learning models. The TensorFlow GPU package, available on PyPI, provides GPU support for TensorFlow. It allows users to leverage the power of GPUs to accelerate their machine learning workflows.
Vulnerability Detail
In TensorFlow GPU version 2.3.0, the implementations of SparseCountSparseOutput and RaggedCountSparseOutput do not validate the shape of the weights tensor, unlike the DenseCountSparseOutput implementation. This lack of validation allows a user to pass fewer weights than the data values, leading to a read from outside the bounds of the heap buffer allocated for the weights. This vulnerability can result in a heap buffer overflow, potentially leading to arbitrary code execution or a crash of the application. The issue has been patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow GPU version 2.3.1.
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 version below is the minimum fixed release for this vulnerability.
If you need the minimum change
3cbb917b4714766030b28eba9fb41bb97ce9ee02
This is the first release that contains the fix.
pip install --upgrade "tensorflow-gpu==3cbb917b4714766030b28eba9fb41bb97ce9ee02"Tip — Not sure which version your app runs? Vulert can match this fix to it. Scan free →
Conclusion
The vulnerability in TensorFlow GPU version 2.3.0 allows for a heap buffer overflow, which can have serious security implications. Users are advised to upgrade to version 2.3.1 or later to mitigate this vulnerability. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow GPU is a package that provides GPU support for TensorFlow, enabling users to accelerate their machine learning workflows using GPUs.
A heap buffer overflow occurs when a program writes data beyond the bounds of a dynamically allocated buffer in the heap. This can lead to memory corruption and potentially allow an attacker to execute arbitrary code or crash the application.
You can check the version of TensorFlow GPU installed by running the following command:
pip show tensorflow-gpu
No, only TensorFlow GPU version 2.3.0 is affected. The vulnerability has been fixed in version 2.3.1 and later.
No, there is no known workaround for this vulnerability. It is recommended to upgrade to TensorFlow GPU version 2.3.1 or later.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-gpu.
