CVE-2020-15265: TensorFlow - Out-of-Bounds Access Vulnerability
Summary
CVE-2020-15265: Segfault in Tensorflow. Affects tensorflow versions < 2.4.0. Fixed in 2.4.0. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2020-15265 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 resources for building and deploying machine learning models. TensorFlow allows developers to create and train neural networks, perform numerical computations, and process large datasets efficiently. However, an out-of-bounds access vulnerability has been identified in TensorFlow versions prior to 2.4.0.
Vulnerability Detail
The vulnerability exists in the tf.quantization.quantize_and_dequantize function of TensorFlow. By passing an invalid axis value, an attacker can trigger a segmentation fault and access elements outside the rank of the input tensor. The C++ kernel implementation of the function does not properly validate the axis argument, leading to an out-of-bounds access. This vulnerability can potentially be exploited to execute arbitrary code or cause a denial-of-service condition.
How To Fix
Minimum fixed version
2.4.0
This is the first release that contains the fix.
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Workaround
There is no known workaround for this vulnerability. It is essential to update TensorFlow to the patched version to mitigate the risk.
Conclusion
The out-of-bounds access vulnerability in TensorFlow versions prior to 2.4.0 can be mitigated by updating to the patched version. It is crucial to keep software dependencies up to date to ensure the security and stability of applications. For further support on vulnerability remediation, you can reach out to DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open-source machine learning framework developed by Google. It provides tools and resources for building and deploying machine learning models.
This vulnerability can allow an attacker to execute arbitrary code or cause a denial-of-service condition.
To fix this vulnerability, update TensorFlow to version 2.4.0 or later.
No, there is no known workaround for this vulnerability. Updating to the patched version is necessary.
You can find more information about this vulnerability in the TensorFlow vulnerability database.
References
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