CVE-2023-25676: TensorFlow - Segfault in tf.raw_ops.ParallelConcat
Summary
CVE-2023-25676: TensorFlow has null dereference on ParallelConcat with XLA. Affects tensorflow versions < 2.12.0. Fixed in 2.12.0. How to fix ↓
Also tracked for
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow-gpu · PyPI
- tensorflow · PyPI
The same CVE-2023-25676 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 open source machine learning platform developed by Google. It provides a comprehensive set of tools and libraries for building and deploying machine learning models. TensorFlow is widely used in various domains, including research, industry, and academia. This vulnerability affects versions prior to 2.12.0 and 2.11.1 with XLA enabled. XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra operations in TensorFlow. It aims to optimize the execution of TensorFlow models on various hardware platforms. The vulnerability occurs in the tf.raw_ops.ParallelConcat function when provided with a shape parameter that has a rank less than or equal to zero. This leads to a segmentation fault (segfault) due to a nullptr dereference. The issue has been addressed in TensorFlow 2.12.0 and 2.11.1.
Vulnerability Detail
The vulnerability in TensorFlow arises from a nullptr dereference in the tf.raw_ops.ParallelConcat function when an invalid shape parameter is provided. This can occur when the rank of the shape is not greater than zero. A nullptr dereference can lead to a segmentation fault, causing the application to crash. An attacker could potentially exploit this vulnerability to cause a denial of service (DoS) attack by providing a malicious input that triggers the segfault. It is important to note that this vulnerability only affects versions prior to 2.12.0 and 2.11.1 with XLA enabled.
How To Fix
Minimum fixed version
2.12.0
This is the first release that contains the fix.
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Workaround
There is no specific workaround available for this vulnerability. The recommended solution is to upgrade to the fixed versions of TensorFlow.
Conclusion
The vulnerability in TensorFlow versions prior to 2.12.0 and 2.11.1 with XLA enabled can lead to a segmentation fault and potential denial of service attacks. It is crucial to upgrade to the fixed versions to ensure the security and stability of the application. For further support on vulnerability remediation, please contact DevNack.com.
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FAQ
TensorFlow is an open source machine learning platform developed by Google. It provides tools and libraries for building and deploying machine learning models.
XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra operations in TensorFlow. It optimizes the execution of TensorFlow models on various hardware platforms.
The vulnerability occurs in the tf.raw_ops.ParallelConcat function when provided with an invalid shape parameter that has a rank less than or equal to zero.
A nullptr dereference occurs when a null pointer is accessed, leading to a segmentation fault and application crash.
The vulnerability can be mitigated by upgrading TensorFlow to version 2.12.0 or 2.11.1, which include a fix for the nullptr dereference issue.
References
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