CVE-2022-41893 in TensorFlow
Summary
by MITRE • 11/19/2022
TensorFlow is an open source platform for machine learning. If `tf.raw_ops.TensorListResize` is given a nonscalar value for input `size`, it results `CHECK` fail which can be used to trigger a denial of service attack. We have patched the issue in GitHub commit 888e34b49009a4e734c27ab0c43b0b5102682c56. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 12/20/2022
The vulnerability identified as CVE-2022-41893 affects TensorFlow, a widely-used open source machine learning platform that powers numerous artificial intelligence applications across various industries. This issue resides within the raw operations API of TensorFlow, specifically in the `tf.raw_ops.TensorListResize` function that handles tensor list resizing operations. The flaw represents a classic input validation failure where the system does not properly validate the scalar nature of the size parameter, creating a potential pathway for malicious actors to disrupt service availability. The vulnerability is particularly concerning given TensorFlow's extensive deployment in production environments where system stability and reliability are paramount for machine learning workloads and AI-powered applications.
The technical implementation of this vulnerability stems from a missing validation check within the tensor list resize operation that should enforce scalar input validation for the size parameter. When a nonscalar value is provided to the size input, the function triggers a CHECK fail condition that terminates the process execution. This type of failure represents a denial of service condition that can be reliably exploited by attackers who craft malicious inputs containing nonscalar tensors. The vulnerability manifests as an assertion failure in the TensorFlow runtime environment, causing the application to crash and potentially leading to complete service unavailability. From a cybersecurity perspective, this vulnerability aligns with CWE-248, which addresses "Uncaught Exception" conditions, and demonstrates how improper error handling can lead to system instability. The flaw operates at the operational level of TensorFlow's internal execution engine, specifically within the raw operations layer that provides direct access to low-level TensorFlow operations for advanced users and developers.
The operational impact of this vulnerability extends beyond simple service disruption to potentially affect critical machine learning workflows and AI applications that depend on TensorFlow's stability. Attackers who can submit crafted inputs to systems using TensorFlow may trigger denial of service conditions that could impact production environments, model training pipelines, and inference services. This vulnerability particularly affects organizations running TensorFlow versions 2.8.4, 2.9.3, 2.10.1, and 2.11, where the patched code has been implemented to prevent the CHECK failure from occurring. The fix implemented in commit 888e34b49009a4e734c27ab0c43b0b5102682c56 introduces proper scalar validation for the size parameter, ensuring that nonscalar inputs are rejected before they can cause system crashes. Organizations using TensorFlow in production environments should prioritize upgrading to patched versions or applying the cherry-picked fixes to maintain system integrity and prevent potential exploitation that could disrupt machine learning operations and AI-driven services.
The remediation approach for this vulnerability follows standard cybersecurity practices for addressing software flaws in widely-deployed platforms. The TensorFlow team has provided specific patch versions that include the necessary validation logic to prevent nonscalar inputs from triggering the CHECK failure condition. This fix represents a defensive programming approach that validates input parameters before processing them, aligning with ATT&CK technique T1499.004 which covers "Toggle Service" and related denial of service methods. Organizations should implement a comprehensive patch management strategy that includes monitoring for vulnerable versions and applying the appropriate security updates to prevent exploitation. The vulnerability serves as a reminder of the importance of input validation in security-critical systems and demonstrates how seemingly minor implementation flaws can have significant operational impacts in production environments that rely on stable machine learning platforms.