CVE-2025-71007 in OneFlow
Summary
by MITRE • 01/28/2026
An input validation vulnerability in the oneflow.index_add component of OneFlow v0.9.0 allows attackers to cause a Denial of Service (DoS) via a crafted input.
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Analysis
by VulDB Data Team • 01/29/2026
The vulnerability identified as CVE-2025-71007 resides within the oneflow.index_add component of the OneFlow deep learning framework version 0.9.0, representing a critical input validation flaw that can be exploited to trigger a denial of service condition. This component is designed to perform indexed addition operations on tensors, a fundamental operation in machine learning workflows where specific elements of arrays are updated based on provided indices. The vulnerability emerges from insufficient validation of input parameters, particularly those related to index ranges and tensor dimensions, allowing malicious actors to craft inputs that cause unexpected behavior in the underlying computation engine.
The technical flaw manifests when the index_add function receives malformed or out-of-bounds index values that are not properly sanitized before processing. This weakness creates a path for attackers to manipulate the component's internal state through carefully constructed input data, potentially leading to memory corruption, stack overflow conditions, or resource exhaustion scenarios. The vulnerability operates at the intersection of software robustness and computational integrity, where the framework fails to adequately validate the bounds of index references against the actual tensor dimensions. According to CWE classification, this represents a variant of CWE-129: Improper Validation of Array Index, which specifically addresses the failure to validate array indices against legitimate bounds, making it susceptible to buffer overflows and memory access violations.
The operational impact of this vulnerability extends beyond simple service disruption, as it can compromise the entire machine learning pipeline execution environment. When exploited, the DoS condition can cause the application process to crash, hang indefinitely, or consume excessive system resources, effectively preventing legitimate users from performing their computational tasks. In production environments where OneFlow serves as a core component of AI infrastructure, such a vulnerability could result in significant downtime and loss of productivity for data scientists and machine learning engineers. The attack surface is particularly concerning given that index_add operations are frequently used in training and inference workflows, making this vulnerability potentially exploitable in various operational contexts.
Mitigation strategies for CVE-2025-71007 should prioritize immediate patching of the affected OneFlow version to address the input validation deficiencies in the index_add component. Organizations should implement comprehensive input sanitization measures that validate all index ranges against tensor dimensions before processing, ensuring that any out-of-bounds references are properly rejected. The implementation should follow ATT&CK framework principles for defensive measures, specifically focusing on input validation and sanitization techniques that prevent malformed data from reaching critical system components. Additionally, deployment environments should incorporate monitoring mechanisms to detect unusual resource consumption patterns that might indicate exploitation attempts, while maintaining proper logging of all index_add operations for forensic analysis purposes. System administrators should also consider implementing access controls and privilege separation to limit the potential impact of successful exploitation attempts, ensuring that even if the vulnerability is exploited, the attack scope remains contained within specific operational boundaries.