CVE-2021-37670: TensorFlow - Out-of-Bounds Read Vulnerability
Summary
CVE-2021-37670: Heap OOB in UpperBound and LowerBound 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-2021-37670 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 ecosystem of tools, libraries, and resources for developing and deploying machine learning models. TensorFlow is widely used in various industries and research domains.
Vulnerability Detail
The vulnerability identified as CVE-2021-37670 affects TensorFlow versions 2.3.0 to 2.6.0-rc2. It allows an attacker to read from outside the bounds of heap allocated data by exploiting the tf.raw_ops.UpperBound and tf.raw_ops.LowerBound functions. The issue arises due to the lack of validation for the rank of the sorted_input argument. An attacker can exploit this vulnerability by sending specially crafted illegal arguments to these functions. The impact of this vulnerability can lead to information disclosure and potential exploitation of sensitive data.
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
No specific workaround has been provided for this vulnerability. It is recommended to apply the official patch or update to the fixed versions of TensorFlow.
Conclusion
The identified vulnerability in TensorFlow can be mitigated by updating to the patched versions or applying the official patch. It is crucial for users to keep their TensorFlow installations up to date to ensure the security and integrity of their machine learning workflows. For further support and assistance 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 a comprehensive set of tools and resources for developing and deploying machine learning models.
The vulnerability allows an attacker to read from outside the bounds of heap allocated data, potentially leading to information disclosure and exploitation of sensitive data.
The vulnerability affects TensorFlow versions 2.3.0 to 2.6.0-rc2.
To fix this vulnerability, update your TensorFlow installation to the patched versions. The fix will be included in TensorFlow 2.6.0 and backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.
No specific workaround has been provided. It is recommended to apply the official patch or update to the fixed versions of TensorFlow.
References
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