CVE-2020-26271: TensorFlow - Accessing Uninitialized Memory While Loading Saved Model
Summary
CVE-2020-26271: Heap out of bounds access in MakeEdge in TensorFlow. Affects tensorflow across 5 version ranges.
< 1.15.5
>= 2.0.0, < 2.0.4
>= 2.1.0, < 2.1.3
>= 2.2.0, < 2.2.2
>= 2.3.0, < 2.3.2
Fixed in 5 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
- tensorflow · PyPI
The same CVE-2020-26271 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 allows users to define and execute computational graphs, making it a popular choice for deep learning applications. However, certain versions of TensorFlow are vulnerable to accessing uninitialized memory while loading a saved model, which can lead to data leakage or uninitialized data access.
Vulnerability Detail
In affected versions of TensorFlow, there is a vulnerability that allows uninitialized memory access when building the computation graph while loading a saved model. The MakeEdge function, responsible for creating an edge between output and input tensors, does not perform proper bounds checking on the indices used to access arrays. This can result in accessing data outside the boundaries of the allocated arrays, leading to uninitialized data access or potential address leakage. The severity of this vulnerability is rated as Low.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
< 1.15.51.15.5 -
>= 2.0.0, < 2.0.42.0.4 -
>= 2.1.0, < 2.1.32.1.3 -
>= 2.2.0, < 2.2.22.2.2 -
>= 2.3.0, < 2.3.22.3.2
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 specific workaround provided for this vulnerability. It is recommended to update to the fixed versions of TensorFlow as mentioned in the 'how_to_fix' section.
Conclusion
In conclusion, the vulnerability in TensorFlow allows accessing uninitialized memory while loading a saved model. This can lead to unauthorized access or data leakage. It is crucial to update TensorFlow to the fixed versions mentioned to mitigate this vulnerability. For further support on vulnerability remediation, you can contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open-source machine learning framework developed by Google. It provides tools and libraries for building and deploying machine learning models.
This vulnerability can result in accessing uninitialized memory or leaking addresses from the library, potentially leading to unauthorized access or data leakage.
To fix this vulnerability, update TensorFlow to one of the fixed versions: 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, or 2.4.0.
No specific workarounds are provided for this vulnerability. Updating to the fixed versions is the recommended solution.
You can find more information about this vulnerability in the provided source link: https://github.com/bitnami/vulndb/tree/main/data/tensorflow/BIT-tensorflow-2020-26271.json
References
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