CVE-2020-15208: TensorFlow Lite Common Dimension Size Mismatch
Summary
CVE-2020-15208: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a DCHECK which is no-op outside of debug compilation modes. Affects tensorflow across 6 version ranges.
< 1.15.4
>= 2.0.0, < 2.0.3
>= 2.1.0, < 2.1.2
>= 2.2.0, < 2.2.1
>= 2.3.0, < 2.3.1
< 8ee24e7949a203d234489f9da2c5bf45a7d5157d
Fixed in 5 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2020-15208 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 developed by Google. It provides a comprehensive ecosystem of tools, libraries, and community resources for building and deploying machine learning models. TensorFlow Lite is a lightweight version of TensorFlow designed for mobile and embedded devices. It allows developers to run machine learning models on resource-constrained platforms. However, versions of TensorFlow Lite prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 are vulnerable to a common dimension size mismatch issue.
Vulnerability Detail
The common dimension size mismatch vulnerability in TensorFlow Lite occurs when determining the common dimension size of two tensors. The function responsible for this task uses a DCHECK statement, which is a no-op outside of debug compilation modes. As a result, the function always returns the dimension of the first tensor, even if it is larger than the dimension of the second tensor. This discrepancy can lead to reads and writes outside of bounds, as the interpreter assumes there is enough data in both tensors. Malicious attackers can exploit this vulnerability to craft cases where the first tensor's dimension is larger than the second, causing potential data corruption or unauthorized access.
How To Fix
Latest published version: 2.21.0 as of 22 Oct 2026
pip install --upgrade "tensorflow==2.21.0"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.
-
< 1.15.41.15.4pip install --upgrade "tensorflow==1.15.4" -
>= 2.0.0, < 2.0.32.0.3pip install --upgrade "tensorflow==2.0.3" -
>= 2.1.0, < 2.1.22.1.2pip install --upgrade "tensorflow==2.1.2" -
>= 2.2.0, < 2.2.12.2.1pip install --upgrade "tensorflow==2.2.1" -
>= 2.3.0, < 2.3.12.3.1pip install --upgrade "tensorflow==2.3.1"
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Workaround
There is no known workaround for this vulnerability. It is essential to update TensorFlow Lite to the patched versions mentioned above.
Conclusion
The common dimension size mismatch vulnerability in TensorFlow Lite can have severe consequences, including data corruption and unauthorized access. It is crucial for developers to update their TensorFlow Lite installations to the patched versions to mitigate the risk. For further support on vulnerability remediation and securing your machine learning models, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow Lite is a lightweight version of TensorFlow designed for running machine learning models on resource-constrained platforms such as mobile and embedded devices.
The common dimension size mismatch vulnerability in TensorFlow Lite can lead to data corruption or unauthorized access. Attackers can exploit this vulnerability to manipulate tensor dimensions and potentially compromise the integrity of machine learning models.
You can use the Vulert Playground at Vulert Scanner to check if your application is affected by this vulnerability. Simply provide your Pipfile.lock, and Vulert will analyze it for open-source vulnerabilities, including this TensorFlow Lite vulnerability.
No, there are no known workarounds for this vulnerability. It is crucial to update TensorFlow Lite to the patched versions to mitigate the risk.
You can find more information about this vulnerability in the TensorFlow advisory database at https://github.com/pypa/advisory-database/blob/main/vulns/tensorflow/PYSEC-2020-131.yaml.
References
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