CVE-2022-35970: TensorFlow - Denial of Service Vulnerability
Summary
CVE-2022-35970: Segfault in QuantizedInstanceNorm in TensorFlow. Affects tensorflow across 3 version ranges.
>= 2.7.0, < 2.7.2
>= 2.8.0, < 2.8.1
>= 2.9.0, < 2.9.1
Fixed in 2.7.2, 2.8.1 or 2.9.1, depending 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-2022-35970 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 fields for tasks such as image recognition, natural language processing, and recommendation systems. However, a vulnerability has been discovered in TensorFlow that can be exploited to trigger a denial of service attack.
Vulnerability Detail
The vulnerability, identified as CVE-2022-35970, affects the QuantizedInstanceNorm function in TensorFlow. When this function is provided with x_min or x_max tensors of a nonzero rank, it results in a segmentation fault, leading to a denial of service condition. An attacker can exploit this vulnerability to crash the TensorFlow application, causing disruption of service. The issue has been patched in the GitHub commit 785d67a78a1d533759fcd2f5e8d6ef778de849e0 and will be included in TensorFlow 2.10.0. The fix will also be backported to TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these versions are still within the supported range.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
>= 2.7.0, < 2.7.22.7.2 -
>= 2.8.0, < 2.8.12.8.1 -
>= 2.9.0, < 2.9.12.9.1
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Workaround
There are no known workarounds for this vulnerability. It is necessary to update TensorFlow or apply the provided patches to mitigate the risk.
Conclusion
In conclusion, TensorFlow is vulnerable to a denial of service attack due to a flaw in the QuantizedInstanceNorm function. It is crucial to update TensorFlow to version 2.10.0 or apply the provided patches to mitigate the risk. No workarounds are available for this vulnerability. For further assistance or support in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow →
FAQ
TensorFlow is an open source platform for machine learning that provides tools and resources for developing and deploying machine learning models.
This vulnerability allows an attacker to trigger a denial of service attack, causing the TensorFlow application to crash and disrupt the service.
To fix this vulnerability, update TensorFlow to version 2.10.0 or apply the provided patches for earlier versions.
No, there are no known workarounds for this vulnerability. Updating TensorFlow or applying the patches is necessary.
TensorFlow versions 2.7.0 to 2.7.2, 2.8.0 to 2.8.1, 2.9.0 to 2.9.1, and 2.10-rc0.0 to 2.10-rc3.0 are affected by this vulnerability.
References
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