CVE-2022-35966 in TensorFlow
Summary
by MITRE • 09/17/2022
TensorFlow is an open source platform for machine learning. If `QuantizedAvgPool` is given `min_input` or `max_input` tensors of a nonzero rank, it results in a segfault that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
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Analysis
by VulDB Data Team • 10/19/2022
The vulnerability identified as CVE-2022-35966 affects TensorFlow, a widely-used open source machine learning platform that powers numerous artificial intelligence applications across various industries. This security flaw specifically targets the QuantizedAvgPool operation within TensorFlow's computational graph processing capabilities, representing a critical denial of service vulnerability that can be exploited by malicious actors to disrupt system operations. The issue stems from improper input validation mechanisms within the quantized average pooling function, which is commonly employed in neural network architectures to reduce computational complexity while maintaining model accuracy.
The technical flaw manifests when the QuantizedAvgPool operation receives min_input or max_input tensors with nonzero rank, causing the system to encounter a segmentation fault that results in an immediate process termination. This segmentation fault occurs due to inadequate bounds checking and memory access validation within the tensor processing pipeline, where the function fails to properly validate the dimensional properties of input tensors before attempting to process them. The vulnerability specifically relates to CWE-125, which describes out-of-bounds read conditions, and CWE-129, which covers improper validation of array indices. The flaw exists in the tensor validation logic where rank checks are insufficient to prevent malformed tensor inputs from causing memory access violations that crash the entire TensorFlow execution environment.
The operational impact of this vulnerability extends beyond simple service disruption, as it can be leveraged by attackers to perform targeted denial of service attacks against TensorFlow-based applications and services. Systems utilizing TensorFlow for machine learning inference, particularly those deployed in production environments such as web applications, mobile devices, and cloud services, become vulnerable to exploitation when processing untrusted input data through quantized average pooling operations. The vulnerability affects multiple TensorFlow versions including 2.7.2, 2.8.1, 2.9.1, and the affected versions are still within the supported release lifecycle, making this a particularly concerning issue for organizations maintaining legacy TensorFlow deployments. The lack of known workarounds forces administrators to rely entirely on patching strategies, creating operational challenges for maintaining system availability during security updates.
The security implications of this vulnerability align with ATT&CK technique T1499.004, which describes network denial of service attacks through resource exhaustion, and T1583.001, which covers the development of tools for exploitation. Organizations using TensorFlow for critical machine learning workloads must implement immediate patch management strategies to address this vulnerability, as the segmentation fault can be triggered through crafted input data that passes through the QuantizedAvgPool operation. The fix implemented in commit 7cdf9d4d2083b739ec81cfdace546b0c99f50622 introduces proper tensor rank validation and bounds checking mechanisms to prevent malformed tensor inputs from causing memory access violations. This patch ensures that min_input and max_input tensors undergo comprehensive validation before processing, preventing the segfault conditions that previously allowed attackers to trigger system crashes and service disruptions. The vulnerability demonstrates the importance of robust input validation in machine learning frameworks and highlights the need for comprehensive security testing of mathematical operations within deep learning libraries.