CVE-2020-15196: TensorFlow CPU - Heap Buffer Overflow in SparseCountSparseOutput and RaggedCountSparseOutput Implementations
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-cpu across 2 version ranges.
< 2.3.1
< 3cbb917b4714766030b28eba9fb41bb97ce9ee02
Fixed in 3cbb917b4714766030b28eba9fb41bb97ce9ee02. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow-gpu · 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. It provides a comprehensive ecosystem of tools, libraries, and community resources to facilitate the development of artificial intelligence applications. TensorFlow CPU is a specific package within TensorFlow that focuses on providing CPU support for TensorFlow operations. It allows users to run TensorFlow models efficiently on CPUs without the need for GPU acceleration.
Vulnerability Detail
The vulnerability (CVE-2020-15196) exists in TensorFlow version 2.3.0 and affects the SparseCountSparseOutput and RaggedCountSparseOutput implementations. These implementations fail to validate the shape of the weights tensor, which should match the shape of the data tensor. While the DenseCountSparseOutput implementation includes this validation, the sparse and ragged count implementations do not. As a result, if a user passes fewer weights than the values for the tensors, it can lead to a read from outside the bounds of the heap buffer allocated for the weights. This can potentially result in a heap buffer overflow, leading to memory corruption and possible exploitation by an attacker. The issue has been patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
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 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-cpu==3cbb917b4714766030b28eba9fb41bb97ce9ee02"Tip — Not sure which version your app runs? Vulert can match this fix to it. Scan free →
Workaround
There is no specific workaround mentioned for this vulnerability. Users are advised to apply the fix by upgrading to the patched version of TensorFlow CPU.
Conclusion
The vulnerability (CVE-2020-15196) in TensorFlow CPU's SparseCountSparseOutput and RaggedCountSparseOutput implementations can lead to a heap buffer overflow and potential memory corruption. Users should upgrade to TensorFlow CPU version 2.3.1 or later to mitigate this vulnerability. For further support on vulnerability remediation, you can reach out to DevNack.com.
See every vulnerability recorded for tensorflow-cpu →
FAQ
TensorFlow CPU is a package within TensorFlow that provides CPU support for TensorFlow operations, allowing users to run TensorFlow models efficiently on CPUs.
This vulnerability can lead to a heap buffer overflow in the SparseCountSparseOutput and RaggedCountSparseOutput implementations of TensorFlow CPU. It can result in memory corruption and potential exploitation by an attacker.
To fix this vulnerability, users should upgrade their TensorFlow CPU package to version 2.3.1 or later.
There is no specific workaround mentioned for this vulnerability. Users are advised to apply the fix by upgrading to the patched version of TensorFlow CPU.
For more information about this vulnerability, you can refer to the official TensorFlow documentation or the CVE entry (CVE-2020-15196).
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-cpu.
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