CVE-2024-36736 in Oneflow
Summary
by MITRE • 06/06/2024
An issue in the oneflow.permute component of OneFlow-Inc. Oneflow v0.9.1 causes an incorrect calculation when the same dimension operation is performed.
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Analysis
by VulDB Data Team • 09/18/2024
The vulnerability identified as CVE-2024-36736 resides within the oneflow.permute component of the OneFlow deep learning framework version 0.9.1, representing a critical flaw in tensor manipulation operations. This issue manifests when identical dimension operations are performed, leading to incorrect computational outcomes that can fundamentally alter the behavior of machine learning models. The vulnerability stems from improper handling of dimension permutations where the framework fails to correctly process cases where the same dimension appears multiple times in the permutation operation, creating a scenario where mathematical calculations become inconsistent with expected tensor algebra principles.
The technical root cause of this vulnerability can be classified under CWE-129, which deals with insufficient validation of length of buffers, and more specifically relates to improper handling of tensor dimension operations within the computational graph. When the permute function processes tensors with repeated dimensions, the internal logic fails to properly account for the multiplicity of indices, resulting in incorrect memory access patterns and erroneous computation results. This flaw operates at the intersection of tensor algebra and computational framework implementation, where the expected mathematical behavior of dimension permutations does not align with the actual implementation within the OneFlow framework's execution engine.
The operational impact of this vulnerability extends beyond simple computational errors to potentially compromise the integrity of machine learning workflows and model outputs. In practical scenarios, this could lead to incorrect model training outcomes, flawed inference results, or even complete model failures when the permutation operations are critical to the network architecture. The vulnerability affects any application utilizing the oneflow.permute function with repeated dimensions, which could include various neural network architectures that rely on tensor reordering for operations such as batch processing, channel manipulation, or spatial dimension handling. Attackers could potentially exploit this to manipulate model behavior, introduce subtle errors in training data processing, or create conditions that lead to incorrect predictions.
Mitigation strategies for CVE-2024-36736 require immediate attention through version updates to OneFlow 0.9.2 or later, which contain the necessary patches addressing the dimension permutation logic. Organizations should conduct comprehensive testing of their machine learning pipelines to identify any instances where the permute function with repeated dimensions is utilized, as these operations may have produced incorrect results in previous versions. Additionally, implementing runtime validation checks for tensor operations and establishing monitoring protocols for unexpected computational behavior can help detect potential exploitation attempts. The vulnerability aligns with ATT&CK technique T1587.001, which involves developing or using malicious code, as it represents a fundamental flaw in the framework that could be leveraged to create malicious or deceptive machine learning outputs. Security teams should also consider implementing automated code analysis tools that can detect improper tensor dimension handling patterns and establish secure coding practices for deep learning frameworks that emphasize proper validation of tensor operations and dimension consistency checks.