CVE-2021-41223: TensorFlow Heap Out-of-Bounds Access Vulnerability
Summary
CVE-2021-41223: Heap OOB read in FusedBatchNorm kernels. Affects tensorflow across 3 version ranges.
< 2.4.4
>= 2.5.0, < 2.5.2
>= 2.6.0, < 2.6.1
Fixed in 2.4.4, 2.5.2 or 2.6.1, depending on the release line you run. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
The same CVE-2021-41223 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 platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow is widely used in various industries and research domains for tasks such as image recognition, natural language processing, and recommendation systems. However, a vulnerability has been discovered in the implementation of FusedBatchNorm kernels in TensorFlow, which can lead to a heap out-of-bounds access.
Vulnerability Detail
The vulnerability in TensorFlow arises from a heap out-of-bounds access in the FusedBatchNorm kernels implementation. This can be exploited by an attacker to read or modify memory outside the bounds of allocated buffers, potentially leading to information disclosure, data corruption, or even remote code execution. The vulnerability has been assigned CVE-2021-41223 and has a CVSSv3 severity rating of High.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
< 2.4.42.4.4 -
>= 2.5.0, < 2.5.22.5.2 -
>= 2.6.0, < 2.6.12.6.1
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Workaround
There is no known workaround for this vulnerability. It is essential to apply the provided patches or update to the latest version of TensorFlow to address the issue.
Conclusion
The vulnerability in TensorFlow's FusedBatchNorm kernels implementation can lead to a heap out-of-bounds access, posing a significant risk to the security and integrity of machine learning models and applications. It is crucial to update to the latest patched version of TensorFlow or apply the provided patches to mitigate this vulnerability. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open source platform for machine learning, widely used for developing and deploying machine learning models.
The vulnerability can allow an attacker to access or modify memory outside the allocated buffers, potentially leading to information disclosure, data corruption, or remote code execution.
The vulnerability affects TensorFlow versions 2.4.4, 2.5.2, 2.6.1, and earlier. Users of these versions should update to the latest patched version.
No, there are no known workarounds for this vulnerability. It is crucial to apply the provided patches or update to the latest version of TensorFlow.
You can use Vulert's playground at Vulert Scanner to check if your application is affected by this vulnerability. Vulert monitors and alerts in real-time for open-source vulnerabilities without requiring access to the code or installation.
References
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