CVE-2018-10055: Improper Restriction of Operations within the Bounds of a Memory Buffer in Google TensorFlow
Summary
CVE-2018-10055: Improper Restriction of Operations within the Bounds of a Memory Buffer in Google TensorFlow. Affects tensorflow versions >= 1.1.0, < 1.7.1. Fixed in 1.7.1. How to fix ↓
Also tracked for
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2018-10055 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
Google TensorFlow is an open-source machine learning framework used for developing and training various deep learning models. It provides a comprehensive ecosystem for building and deploying machine learning applications. TensorFlow is widely adopted and used in both research and production environments. However, versions before 1.7.1 are vulnerable to an improper restriction of operations within the bounds of a memory buffer.
Vulnerability Detail
The vulnerability in Google TensorFlow arises from an invalid memory access and/or a heap buffer overflow in the TensorFlow XLA compiler. This vulnerability can be exploited by an attacker through a crafted configuration file. If successfully exploited, it could lead to a crash or unauthorized read access to other parts of the process memory. The impact of this vulnerability can be severe, potentially allowing an attacker to gain sensitive information or execute arbitrary code.
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 version below is the minimum fixed release for this vulnerability.
If you need the minimum change
1.7.1
This is the first release that contains the fix.
pip install --upgrade "tensorflow==1.7.1"Tip — Not sure which version your app runs? Vulert can match this fix to it. Scan free →
Workaround
There is no specific workaround provided for this vulnerability. Updating to the latest version of TensorFlow is the recommended solution.
Conclusion
The vulnerability in Google TensorFlow (CVE-2018-10055) poses a significant risk to applications utilizing TensorFlow versions before 1.7.1. It is crucial to update to the latest version and regularly update dependencies to mitigate the potential impact of this vulnerability. For further support and assistance in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
Google TensorFlow is an open-source machine learning framework used for developing and training various deep learning models. It provides a comprehensive ecosystem for building and deploying machine learning applications.
The vulnerability in Google TensorFlow can lead to a crash or unauthorized read access to other parts of the process memory. This can potentially allow an attacker to gain sensitive information or execute arbitrary code.
To fix this vulnerability, it is recommended to update the TensorFlow package to version 1.7.1 or later. Regularly updating dependencies is also important to mitigate the risk of this vulnerability.
There is no specific workaround provided for this vulnerability. Updating to the latest version of TensorFlow is the recommended solution.
For more information about this vulnerability, you can refer to the GitHub advisory database and the provided source link: https://github.com/github/advisory-database/blob/main/advisories/github-reviewed/2019/04/GHSA-q492-f7gr-27rp/GHSA-q492-f7gr-27rp.json
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