CVE-2022-36002 in TensorFlow
Summary
by MITRE • 09/17/2022
TensorFlow is an open source platform for machine learning. When `Unbatch` receives a nonscalar input `id`, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit 4419d10d576adefa36b0e0a9425d2569f7c0189f. 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 • 10/20/2022
The vulnerability identified as CVE-2022-36002 affects TensorFlow, a widely-used open-source machine learning platform that serves as the foundation for numerous artificial intelligence applications across industries. This security flaw resides within the Unbatch operation functionality of TensorFlow's computational graph processing system, where the software fails to properly validate input parameters before processing them. The issue manifests specifically when the Unbatch operation receives a nonscalar input identifier, triggering a critical CHECK failure that results in immediate program termination. This behavior represents a significant security concern as it can be exploited to induce denial of service conditions in systems that rely on TensorFlow for machine learning workloads, potentially disrupting critical AI-powered services and applications.
The technical nature of this vulnerability stems from inadequate input validation within TensorFlow's internal processing pipeline for the Unbatch operation. When the system encounters a nonscalar input parameter, the CHECK mechanism that TensorFlow employs for internal consistency validation fails, causing an abrupt program termination. This failure mode creates a predictable crash condition that can be systematically triggered by malicious actors through crafted input data, leading to service disruption. The vulnerability operates at the level of TensorFlow's core computational graph execution engine, making it particularly dangerous as it can affect any application that utilizes the Unbatch operation regardless of the specific machine learning model or use case. The flaw demonstrates poor defensive programming practices that violate fundamental security principles, as the system should gracefully handle malformed inputs rather than crashing completely.
The operational impact of this vulnerability extends beyond simple service disruption to potentially compromise the reliability and availability of machine learning infrastructure across numerous organizations. Systems that depend on TensorFlow for processing large volumes of data or real-time inference may experience cascading failures when exploited, particularly in production environments where continuous operation is critical. The vulnerability affects multiple TensorFlow versions including 2.7.2, 2.8.1, 2.9.1, and the upcoming 2.10.0 release, indicating a broad attack surface that requires immediate attention from system administrators and security teams. The lack of known workarounds means that organizations cannot simply modify their configurations or input validation procedures to mitigate the risk, forcing them to rely entirely on official patches and version upgrades.
The security implications of CVE-2022-36002 align with CWE-248, which addresses "Uncaught Exception" conditions in software systems, and reflects patterns commonly associated with denial of service attacks in software systems. From an ATT&CK framework perspective, this vulnerability maps to the T1499.004 technique related to "Cloud Service Destruction" and T1566.001 "Phishing" as attackers could potentially exploit this weakness in cloud-based machine learning services or through malicious input delivery. The vulnerability demonstrates a classic example of how seemingly minor input validation failures can create significant security risks in complex software systems, particularly those handling sensitive data processing tasks. Organizations utilizing TensorFlow for production machine learning applications must prioritize immediate patching of affected versions to prevent exploitation and maintain the integrity of their AI infrastructure. The fix implemented through the GitHub commit 4419d10d576adefa36b0e0a9425d2569f7c0189f represents a proper resolution that addresses the root cause by implementing robust input validation and proper error handling mechanisms within the Unbatch operation to prevent the CHECK failure condition from occurring.