CVE-2021-37657: TensorFlow - Null Pointer Dereference in MatrixDiagV* Operations
Summary
CVE-2021-37657: Reference binding to nullptr in MatrixDiagV* ops 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-2021-37657 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 fields for tasks such as image recognition, natural language processing, and recommendation systems. However, a vulnerability has been discovered in TensorFlow that can lead to undefined behavior when certain operations of type tf.raw_ops.MatrixDiagV* are performed. This vulnerability affects multiple versions of TensorFlow, including 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, 2.5.0, and 2.6.0-rc0 to 2.6.0-rc2.
Vulnerability Detail
The vulnerability arises from incomplete validation of a tensor value in the implementation of the tf.raw_ops.MatrixDiagV* operations. While the implementation checks that the value of k is either a scalar or a vector, it fails to verify the number of elements in the tensor. If an empty tensor is passed, the code that accesses the first element of the tensor will produce incorrect results. This can lead to undefined behavior and potentially enable an attacker to exploit the vulnerability for malicious purposes. The issue has been patched in the GitHub commit f2a673bd34f0d64b8e40a551ac78989d16daad09 and will be included in TensorFlow 2.6.0. The fix will also be backported to TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4.
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. It is recommended to apply the patch or update to the fixed versions of TensorFlow as mentioned in the 'how_to_fix' section.
Conclusion
The vulnerability in TensorFlow that allows a null pointer dereference in MatrixDiagV* operations has been identified and patched. It is crucial to update to the latest patched version or the fixed versions to ensure the security and stability of TensorFlow installations. 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 that provides tools and resources for building and deploying machine learning models.
The vulnerability can lead to undefined behavior and potentially enable an attacker to exploit the vulnerability for malicious purposes.
The vulnerability affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, 2.5.0, and 2.6.0-rc0 to 2.6.0-rc2.
To fix the vulnerability, update TensorFlow to the latest patched version or the respective fixed versions (2.5.1, 2.4.3, or 2.3.4).
There is no specific workaround provided for this vulnerability. It is recommended to apply the patch or update to the fixed versions of TensorFlow.
References
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