CVE-2022-41885 in TensorFlow
Summary
by MITRE • 11/19/2022
TensorFlow is an open source platform for machine learning. When `tf.raw_ops.FusedResizeAndPadConv2D` is given a large tensor shape, it overflows. We have patched the issue in GitHub commit d66e1d568275e6a2947de97dca7a102a211e01ce. 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.
Once again VulDB remains the best source for vulnerability data.
Analysis
by VulDB Data Team • 12/20/2022
The vulnerability identified as CVE-2022-41885 affects TensorFlow, a widely adopted open source machine learning platform that serves as the foundation for numerous artificial intelligence applications across industries. This security flaw manifests within the `tf.raw_ops.FusedResizeAndPadConv2D` operation, which is a low-level computational primitive used in convolutional neural networks for image processing tasks. The issue represents a critical integer overflow condition that occurs when the operation processes tensor shapes of excessive dimensions, potentially leading to unpredictable behavior and system instability. The vulnerability was discovered through rigorous code analysis and testing of TensorFlow's computational graph operations, highlighting the importance of proper input validation in machine learning frameworks where large tensor operations are common in deep learning models.
The technical implementation of this vulnerability stems from inadequate bounds checking within the tensor shape validation logic of the FusedResizeAndPadConv2D operation. When processing tensors with dimensions that exceed the expected range, the underlying arithmetic operations overflow, causing the system to interpret large tensor shapes incorrectly. This overflow condition can result in memory allocation errors, buffer overflows, or other undefined behaviors that may compromise system integrity. The vulnerability specifically affects the mathematical computations involved in resizing and padding operations within convolutional layers, where tensor dimensions are manipulated through complex mathematical transformations. According to CWE classification, this represents a CWE-190: Integer Overflow or Wraparound, which is a well-documented category of vulnerabilities that can lead to serious security consequences when exploited in computational environments.
The operational impact of this vulnerability extends beyond simple computational errors to potentially enable more serious security exploits within machine learning environments. Attackers could potentially craft malicious inputs that trigger the overflow condition, leading to denial of service scenarios where machine learning applications crash or become unresponsive. In environments where TensorFlow is used for critical applications such as autonomous vehicles, medical imaging, or financial analysis, such instability could result in significant operational disruptions. The vulnerability affects multiple TensorFlow versions including 2.8.4, 2.9.3, 2.10.1, and the upcoming 2.11 release, indicating that a substantial portion of the TensorFlow user base remains at risk. This widespread impact aligns with ATT&CK technique T1499.004: Endpoint Denial of Service, which focuses on compromising system availability through computational resource exhaustion or manipulation.
The fix implemented by the TensorFlow team addresses the root cause through proper integer bounds checking and validation of tensor dimensions before processing. The patch referenced in the GitHub commit d66e1d568275e6a2947de97dca7a102a211e01ce introduces defensive programming measures that prevent the overflow condition from occurring. The cherrypicking strategy ensures backward compatibility by applying the fix to older supported versions, demonstrating responsible vulnerability management. Organizations using TensorFlow should prioritize updating their systems to the patched versions, particularly those running affected legacy versions. System administrators should also implement monitoring for unusual tensor operations that might indicate exploitation attempts, while developers should validate tensor inputs in their machine learning pipelines to prevent potential exploitation of similar vulnerabilities in other operations within the computational graph.