CVE-2021-37672: TensorFlow - Out-of-Bounds Read Vulnerability
Summary
CVE-2021-37672: Heap OOB in SdcaOptimizerV2 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-2021-37672 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 fields.
Vulnerability Detail
The vulnerability identified as CVE-2021-37672 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 a flaw in the tf.raw_ops.SdcaOptimizerV2 function. The implementation of this function fails to validate the length of example_labels, which can lead to out-of-bounds memory access. An attacker can exploit this vulnerability by sending specially crafted illegal arguments to the affected function. Successful exploitation of this vulnerability can result in unauthorized access to sensitive information or 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.
-
>= 2.3.0, < 2.3.42.3.4 -
>= 2.4.0, < 2.4.32.4.3 -
>= 2.5.0, < 2.5.12.5.1
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 available for this vulnerability. It is recommended to apply the official patches or update to the latest patched versions of TensorFlow.
Conclusion
The vulnerability identified as CVE-2021-37672 in TensorFlow can lead to unauthorized access to sensitive information or a denial of service (DoS) condition. It is crucial to update TensorFlow to the latest patched versions or apply the official 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.
This vulnerability allows an attacker to read from outside the bounds of heap allocated data, potentially leading to unauthorized access to sensitive information or a denial of service (DoS) condition.
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 respective patches. The fix is included in TensorFlow 2.6.0 and will be cherrypicked on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.
There is no known workaround available for this vulnerability. It is recommended to apply the official patches or update to the latest patched versions of TensorFlow.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow.
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