CVE-2022-23573: TensorFlow - Uninitialized Data Copying in AssignOp
Summary
CVE-2022-23573: Uninitialized variable access in Tensorflow. Affects tensorflow across 3 version ranges.
< 2.5.3
>= 2.6.0, < 2.6.3
>= 2.7.0, < 2.7.1
Fixed in 2.5.3, 2.6.3 or 2.7.1, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-gpu · PyPI
- tensorflow-cpu · PyPI
The same CVE-2022-23573 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 is widely used in various domains, including research, industry, and academia. However, a vulnerability has been discovered in TensorFlow that can lead to uninitialized data being copied to a new tensor, resulting in undefined behavior.
Vulnerability Detail
The vulnerability stems from the implementation of the AssignOp in TensorFlow. While the left-hand side of the assignment is checked for initialization to minimize allocations, the right-hand side is not. This oversight can lead to uninitialized data being copied, which can have severe consequences. Attackers may be able to exploit this vulnerability to manipulate or corrupt data, potentially leading to unauthorized access, data leakage, or system compromise. The impact of this vulnerability is classified as high severity.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
< 2.5.32.5.3 -
>= 2.6.0, < 2.6.32.6.3 -
>= 2.7.0, < 2.7.12.7.1
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Workaround
As a temporary workaround, users can consider avoiding the usage of the AssignOp operation in their TensorFlow code until the patched versions are available. By refraining from using this specific operation, the risk of encountering the vulnerability can be minimized. However, it is important to note that this workaround may not be feasible in all scenarios, and updating to the patched versions should be the ultimate goal.
Conclusion
The vulnerability in TensorFlow that allows uninitialized data copying in the AssignOp operation can have severe consequences, including unauthorized access, data leakage, or system compromise. It is crucial for users to update their TensorFlow installations to the latest patched version as soon as it becomes available. This will ensure that the vulnerability is mitigated and the system remains secure. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
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.
The impact of this vulnerability is classified as high severity. It can potentially lead to unauthorized access, data leakage, or system compromise.
The vulnerability affects TensorFlow versions 2.5.2, 2.6.0, 2.6.1, 2.6.2, and 2.7.0.
The TensorFlow development team has addressed this vulnerability in the upcoming release of TensorFlow 2.8.0. They have also backported the fix to earlier versions, including TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3. Users are advised to update their TensorFlow installations to the latest patched version as soon as it becomes available.
As a temporary workaround, users can consider avoiding the usage of the AssignOp operation in their TensorFlow code until the patched versions are available. However, this workaround may not be feasible in all scenarios, and updating to the patched versions is recommended.
References
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