CVE-2021-29570: TensorFlow - Out-of-Bounds Read in MaxPoolGradWithArgmax
Summary
CVE-2021-29570: Heap out of bounds read in MaxPoolGradWithArgmax. Affects tensorflow across 4 version ranges.
< 2.1.4
>= 2.2.0, < 2.2.3
>= 2.3.0, < 2.3.3
>= 2.4.0, < 2.4.2
Fixed in 4 releases; the minimum fixed version depends 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-2021-29570 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 end-to-end open source platform for machine learning. It provides a comprehensive set of tools, libraries, and resources for building and deploying machine learning models. TensorFlow is widely used in various industries and research domains.
Vulnerability Detail
The vulnerability (CVE-2021-29570) exists in the implementation of tf.raw_ops.MaxPoolGradWithArgmax in TensorFlow. An attacker can exploit this vulnerability by supplying specially crafted inputs, which can cause reads outside the bounds of heap allocated data. The implementation uses the same value to index two different arrays, but there is no guarantee that the sizes are identical. This can lead to memory corruption and potentially allow an attacker to execute arbitrary code or cause a denial-of-service (DoS) condition.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
< 2.1.42.1.4 -
>= 2.2.0, < 2.2.32.2.3 -
>= 2.3.0, < 2.3.32.3.3 -
>= 2.4.0, < 2.4.22.4.2
Tip — Not sure which release line you're on? Vulert can find the fix for the version you are using. Scan free →
Workaround
There is no known workaround for this vulnerability at the moment. It is recommended to apply the official fix or upgrade to the patched versions of TensorFlow.
Conclusion
The vulnerability in TensorFlow's tf.raw_ops.MaxPoolGradWithArgmax can lead to memory corruption and potential code execution or denial-of-service. It is crucial to upgrade to the fixed versions or apply the official fix as soon as possible. For further support on vulnerability remediation, 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 building and deploying machine learning models.
This vulnerability can lead to memory corruption and potentially allow an attacker to execute arbitrary code or cause a denial-of-service (DoS) condition.
The vulnerability affects TensorFlow versions 0.x, 2.1.4, 2.2.0 - 2.2.3, 2.3.0 - 2.3.3, 2.4.0 - 2.4.2.
To fix this vulnerability, it is recommended to upgrade to TensorFlow 2.5.0 or later. Alternatively, you can update to the patched versions 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
There is no known workaround for this vulnerability. It is recommended to apply the official fix or upgrade to the patched versions.
References
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