CVE-2021-37645: TensorFlow Integer Overflow Vulnerability
Summary
CVE-2021-37645: Integer overflow due to conversion to unsigned in TensorFlow. Affects tensorflow across 3 version ranges.
>= 2.3.0, < 2.3.4
>= 2.4.0, < 2.4.3
>= 2.5.0, < 2.5.1
Fixed in 2.3.4, 2.4.3 or 2.5.1, 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-2021-37645 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 developing and deploying machine learning models. TensorFlow is widely used in various industries and has a large community of developers and contributors. However, it is susceptible to security vulnerabilities that can potentially impact the confidentiality, integrity, and availability of machine learning applications.
Vulnerability Detail
The vulnerability identified as CVE-2021-37645 affects TensorFlow versions 2.3.0 to 2.6.0-rc2. It stems from an integer overflow issue in the implementation of tf.raw_ops.QuantizeAndDequantizeV4Grad. The vulnerability arises when a signed integer value is converted to an unsigned one, leading to an allocation of memory based on this value. The issue lies in the usage of the axis value as the size argument to the absl::InlinedVector constructor. Since the constructor expects an unsigned type, the implicit conversion transforms the negative value to a large integer. This vulnerability can be exploited by an attacker to cause a denial-of-service (DoS) condition or potentially execute arbitrary code.
How To Fix
Fixed versions by release line
The minimum fixed version depends on the release line you run.
-
>= 2.3.0, < 2.3.42.3.4 -
>= 2.4.0, < 2.4.32.4.3 -
>= 2.5.0, < 2.5.12.5.1
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Workaround
There is no specific workaround provided for this vulnerability. However, users can mitigate the risk by ensuring they have the latest patched versions of TensorFlow installed.
Conclusion
The identified vulnerability in TensorFlow can have serious implications for machine learning applications. It is crucial to promptly update to the latest patched versions or apply the relevant fixes to mitigate the risk. For further assistance and 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 can potentially lead to a denial-of-service (DoS) condition or allow an attacker to execute arbitrary code.
The vulnerability affects TensorFlow versions 2.3.0 to 2.6.0-rc2.
To fix this vulnerability, update TensorFlow to version 2.6.0 or apply the relevant patches.
There is no specific workaround provided, but users can mitigate the risk by updating to the latest patched versions of TensorFlow.
References
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