CVE-2024-36732 in Oneflow
Summary
by MITRE • 06/06/2024
An issue in OneFlow-Inc. Oneflow v0.9.1 allows attackers to cause a Denial of Service (DoS) when an empty array is processed with oneflow.tensordot.
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Analysis
by VulDB Data Team • 06/08/2024
The vulnerability identified as CVE-2024-36732 represents a critical denial of service flaw within the OneFlow deep learning framework version 0.9.1. This issue specifically manifests when the oneflow.tensordot function encounters empty arrays during tensor operations, creating a condition that can be exploited by malicious actors to disrupt system availability and compromise operational continuity. The vulnerability stems from inadequate input validation mechanisms within the tensor dot product implementation, which fails to properly handle edge cases involving empty tensor structures.
The technical implementation flaw occurs at the core level of the oneflow.tensordot function where the system does not perform proper boundary checks or empty array validation before executing tensor multiplication operations. When an empty array is passed to this function, the underlying computational logic becomes unstable, leading to system crashes or indefinite hanging states that prevent further processing. This behavior aligns with CWE-400, which categorizes improper handling of exceptional conditions as a fundamental weakness in software security architecture. The vulnerability demonstrates a classic lack of defensive programming practices where the code assumes valid input parameters without proper validation, creating exploitable pathways for denial of service attacks.
From an operational impact perspective, this vulnerability poses significant risks to machine learning infrastructure deployments that rely on OneFlow for tensor computations. Attackers can exploit this weakness by crafting malicious inputs containing empty arrays and submitting them to systems running vulnerable versions of the framework. The resulting denial of service can affect training pipelines, inference services, and real-time processing systems that depend on stable tensor operations. Such attacks can lead to complete system unavailability, requiring manual intervention and system restarts to restore functionality, which directly impacts business continuity and operational efficiency. The vulnerability is particularly concerning in cloud-based machine learning environments where multiple users share computational resources and could potentially disrupt services for other legitimate users.
Mitigation strategies for CVE-2024-36732 should focus on immediate patching of the OneFlow framework to version 0.9.2 or later, which includes proper input validation for empty array handling. System administrators should implement comprehensive input sanitization measures at application boundaries to detect and reject malformed tensor operations before they reach the vulnerable function. Additionally, deploying monitoring solutions that can detect unusual system behavior patterns or resource consumption spikes can help identify potential exploitation attempts. Organizations should also consider implementing rate limiting and input validation layers within their machine learning pipelines to prevent malicious inputs from reaching the core framework components. The ATT&CK framework categorizes this type of vulnerability under T1499.004, which covers network denial of service attacks, making it a critical target for defensive security measures in AI infrastructure protection strategies.