CVE-2023-25675 in TensorFlow
Summary
by MITRE • 03/25/2023
TensorFlow is an open source machine learning platform. When running versions prior to 2.12.0 and 2.11.1 with XLA, `tf.raw_ops.Bincount` segfaults when given a parameter `weights` that is neither the same shape as parameter `arr` nor a length-0 tensor. A fix is included in TensorFlow 2.12.0 and 2.11.1.
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Analysis
by VulDB Data Team • 03/25/2023
The vulnerability described in CVE-2023-25675 affects TensorFlow versions prior to 2.12.0 and 2.11.1 when utilizing the eXtensible Accelerated Linear Algebra XLA compilation framework. This issue manifests specifically within the tf.raw_ops.Bincount operation, which is a fundamental component for counting occurrences of values in arrays. The flaw represents a classic buffer over-read condition that can lead to system instability and potential denial of service scenarios. When the weights parameter provided to the bincount operation does not match the shape of the input array parameter or is not a zero-length tensor, the operation fails catastrophically with a segmentation fault. This behavior constitutes a direct violation of the software's expected execution flow and represents a critical reliability issue within the machine learning framework.
The technical root cause of this vulnerability stems from inadequate parameter validation within the tf.raw_ops.Bincount implementation. The operation fails to properly verify that the weights tensor maintains appropriate dimensional compatibility with the input array tensor. This validation gap creates a scenario where memory access occurs beyond the allocated boundaries of the weights tensor, leading to the segmentation fault. The vulnerability falls under the category of improper input validation and memory management issues, which are commonly classified as CWE-125 (Out-of-bounds Read) or CWE-787 (Out-of-bounds Write) depending on the specific memory access pattern. The XLA compilation process amplifies this issue by optimizing the code paths without proper bounds checking, creating an environment where the uninitialized memory access becomes exploitable.
The operational impact of this vulnerability extends beyond simple system crashes and represents a significant threat to machine learning pipeline stability and security. When a segmentation fault occurs during model training or inference operations, it can cause complete system failures, data loss, or cascading failures in distributed computing environments. This vulnerability affects organizations relying on TensorFlow for critical machine learning workloads, particularly in production environments where system reliability is paramount. The issue can be triggered through malicious input data or simply through incorrect parameter usage in machine learning applications, making it a potential vector for denial of service attacks against TensorFlow-based systems. Security practitioners must consider this vulnerability as part of their risk assessment for machine learning infrastructure, especially in environments where TensorFlow is used for automated processing of untrusted data streams.
Mitigation strategies for CVE-2023-25675 primarily focus on immediate version upgrades to TensorFlow 2.12.0 or 2.11.1, which contain the necessary patches to address the parameter validation issue. Organizations should implement comprehensive testing procedures to validate that their machine learning applications properly handle the bincount operation with various weight tensor configurations. The fix implemented in the patched versions includes enhanced input validation that ensures the weights tensor dimensions are compatible with the input array before proceeding with the computation. Security teams should also consider implementing runtime monitoring for segmentation fault occurrences and establishing incident response procedures for handling such system crashes in machine learning environments. Additionally, organizations may want to review their TensorFlow usage patterns to identify and remediate any custom implementations that might be affected by similar parameter validation issues, as this vulnerability could indicate broader patterns of inadequate input sanitization within the machine learning framework. The ATT&CK framework categorizes this vulnerability under T1499.004 (Endpoint Denial of Service) as it can be exploited to cause system instability through resource exhaustion or execution termination.