CVE-2023-25801 in TensorFlow
Summary
by MITRE • 03/25/2023
TensorFlow is an open source machine learning platform. Prior to versions 2.12.0 and 2.11.1, `nn_ops.fractional_avg_pool_v2` and `nn_ops.fractional_max_pool_v2` require the first and fourth elements of their parameter `pooling_ratio` to be equal to 1.0, as pooling on batch and channel dimensions is not supported. A fix is included in TensorFlow 2.12.0 and 2.11.1.
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Analysis
by VulDB Data Team • 04/14/2023
The vulnerability identified as CVE-2023-25801 affects TensorFlow versions prior to 2.12.0 and 2.11.1, specifically within the neural network operations related to fractional pooling. This issue resides in the `nn_ops.fractional_avg_pool_v2` and `nn_ops.fractional_max_pool_v2` functions which are designed to perform fractional pooling operations on tensor data. The flaw manifests in the validation logic that governs the `pooling_ratio` parameter, which controls the pooling dimensions across different tensor axes. The implementation incorrectly enforces that the first and fourth elements of the `pooling_ratio` parameter must equal 1.0, thereby preventing legitimate pooling operations on batch and channel dimensions that could be valid under certain machine learning workflows.
The technical nature of this vulnerability stems from inadequate input validation and parameter checking within the fractional pooling implementation. When developers or researchers attempt to configure fractional pooling operations with pooling ratios that do not conform to the strict requirement of having the first and fourth elements equal to 1.0, the functions fail to properly validate whether such configurations would actually be supported or meaningful. This constraint is particularly problematic because it artificially restricts the flexibility of the pooling operations and could lead to unexpected behavior when users attempt to apply pooling across different tensor dimensions. The issue represents a weakness in the parameter validation logic that should allow for more flexible tensor dimension handling while maintaining computational correctness.
From an operational impact perspective, this vulnerability creates a significant limitation for users of TensorFlow who rely on fractional pooling operations in their machine learning models. The restriction prevents proper pooling across batch and channel dimensions, which are fundamental aspects of many deep learning architectures. This limitation could force developers to implement workarounds or alternative approaches that may be less efficient or introduce additional complexity to their models. The vulnerability essentially creates a barrier to legitimate use cases where fractional pooling across all tensor dimensions should be supported, potentially affecting model performance and training efficiency. Security implications arise from the potential for developers to work around this limitation through unintended code paths or by using deprecated functions that may introduce other vulnerabilities.
The mitigation strategy for CVE-2023-25801 involves upgrading to TensorFlow versions 2.12.0 or 2.11.1, which contain the necessary fixes to properly handle the `pooling_ratio` parameter validation. Organizations should prioritize this upgrade across their machine learning environments, particularly in production systems where fractional pooling operations are actively used. Additionally, security teams should monitor for any custom implementations or workarounds that might have been developed to circumvent this limitation, as these may introduce additional attack surface or compatibility issues. The fix addresses the core validation logic to properly support fractional pooling operations across all tensor dimensions while maintaining the computational integrity of the pooling operations. This vulnerability aligns with CWE-20, which covers improper input validation, and represents a specific implementation weakness in the TensorFlow framework's neural network operations that could impact the security and functionality of machine learning applications.
This issue demonstrates how seemingly minor parameter validation restrictions can have significant impacts on machine learning workflow flexibility and system security. The fix implemented in TensorFlow 2.12.0 and 2.11.1 addresses the root cause by allowing proper validation of pooling ratio parameters while maintaining the underlying mathematical correctness of fractional pooling operations. Organizations utilizing TensorFlow should conduct thorough testing after applying the upgrade to ensure that existing models continue to function correctly and that the expanded parameter support does not introduce unexpected behavior in their machine learning pipelines. The vulnerability also highlights the importance of proper input validation in machine learning frameworks, where parameter constraints can significantly impact model flexibility and operational security.