CVE-2022-35985 in TensorFlow
Summary
by MITRE • 09/17/2022
TensorFlow is an open source platform for machine learning. If `LRNGrad` is given an `output_image` input tensor that is not 4-D, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit bd90b3efab4ec958b228cd7cfe9125be1c0cf255. 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-2022-35985 affects TensorFlow, a widely-used open source machine learning platform that powers numerous artificial intelligence applications across various industries. This security flaw resides within the LRNGrad operation, which is part of TensorFlow's neural network processing capabilities. The issue manifests when the LRNGrad function receives an output_image input tensor that does not conform to the expected 4-dimensional structure, leading to a critical check failure that can be exploited by malicious actors. The vulnerability represents a significant concern for organizations relying on TensorFlow for their machine learning workloads, as it creates a potential vector for denial of service attacks that could disrupt critical AI-powered services and applications.
The technical implementation of this vulnerability stems from insufficient input validation within the LRNGrad function's tensor processing pipeline. When the function encounters a tensor that is not properly formatted as a 4-D structure, it triggers a CHECK fail condition that terminates the execution process rather than gracefully handling the malformed input. This type of error handling mechanism creates a predictable crash scenario that attackers can leverage to cause system unavailability. The vulnerability specifically affects TensorFlow versions prior to 2.10.0, with the affected versions including 2.9.1, 2.8.1, and 2.7.2, all of which remain within supported release cycles. The fix implemented in the GitHub commit bd90b3efab4ec958b228cd7cfe9125be1c0cf255 addresses the core validation issue by implementing proper tensor dimension checking before processing operations.
From an operational perspective, this vulnerability poses a substantial risk to machine learning infrastructure and AI-powered services that depend on TensorFlow's computational graph execution. Attackers could exploit this weakness to cause denial of service conditions by submitting malformed tensor inputs to systems running affected TensorFlow versions, potentially leading to complete service disruption. The impact extends beyond simple service interruption as it affects the reliability and availability of critical AI applications, including those in financial services, healthcare, autonomous vehicles, and other sectors where continuous operation is essential. Organizations using TensorFlow in production environments must urgently evaluate their current deployments and implement the necessary patches to prevent exploitation. The vulnerability's classification aligns with CWE-248, which addresses "Uncaught Exception" conditions, and could be mapped to ATT&CK technique T1499.004 for network denial of service attacks. The absence of known workarounds means that organizations cannot delay patching, as any attempt to circumvent the vulnerability would leave systems exposed to exploitation. The fix implementation requires immediate deployment across all affected TensorFlow versions to ensure comprehensive protection against this denial of service threat.
The broader implications of this vulnerability highlight the critical importance of robust input validation in machine learning frameworks, particularly those handling complex tensor operations. Given the widespread adoption of TensorFlow across enterprise and research environments, this flaw could potentially impact numerous organizations simultaneously. Security teams must prioritize this vulnerability in their assessment protocols, considering both the immediate risk of service disruption and the potential for more sophisticated attacks that could exploit similar validation weaknesses. The incident underscores the necessity for continuous security auditing of AI frameworks and emphasizes that even well-established platforms like TensorFlow require vigilant maintenance and prompt response to emerging threats. Organizations should also consider implementing monitoring solutions to detect unusual tensor input patterns that might indicate exploitation attempts, while ensuring all TensorFlow deployments are kept current with security patches to maintain operational resilience against such vulnerabilities.