CVE-2025-71003 identifies a vulnerability in the flow.arange() component of OneFlow version 0.9.0, a machine learning framework. The issue stems from insufficient input validation, allowing attackers to craft malicious inputs that trigger a Denial of Service (DoS) condition. Specifically, the flow.arange() function, which generates sequences of numbers for tensor operations, does not properly validate input parameters such as start, end, or step values. Malformed inputs can cause excessive resource consumption or unhandled exceptions, leading to application crashes or service unavailability. Although no exploits have been reported in the wild, the vulnerability is publicly disclosed and could be leveraged by attackers to disrupt AI workloads or data pipelines relying on OneFlow. The lack of a CVSS score indicates that the vulnerability has not yet been fully assessed, but the nature of the flaw suggests a focus on availability impact without direct compromise of confidentiality or integrity. The absence of patches or mitigation guidance increases risk for organizations deploying this version. Attackers do not require authentication or user interaction to exploit this flaw, making it potentially easy to trigger if the vulnerable function is exposed. This vulnerability highlights the importance of robust input validation in AI frameworks to maintain service reliability and prevent denial of service conditions.

The primary impact of CVE-2025-71003 is on the availability of services using OneFlow v0.9.0. European organizations leveraging OneFlow for machine learning tasks, data analytics, or AI model training could experience service interruptions or crashes if attackers exploit this vulnerability. Disruptions could delay critical AI-driven business processes, affecting sectors such as finance, healthcare, manufacturing, and research. Since OneFlow is used in cloud and on-premises environments, both public and private infrastructure could be targeted. The vulnerability does not appear to affect confidentiality or integrity directly, but denial of service can indirectly impact operational continuity and reliability. Organizations with automated pipelines relying on OneFlow may face cascading failures or degraded performance. The absence of known exploits reduces immediate risk, but the public disclosure increases the likelihood of future exploitation attempts. The impact is more pronounced in environments where OneFlow is exposed to untrusted inputs or external users. Overall, the threat could lead to downtime, increased operational costs, and reputational damage if not addressed promptly.

1. Monitor OneFlow deployments for abnormal behavior or crashes related to flow.arange() usage. 2. Implement input validation controls at the application or API layer to sanitize inputs before they reach the vulnerable function. 3. Restrict access to OneFlow services and components to trusted users and networks to reduce exposure. 4. Employ resource limits and sandboxing to contain potential DoS effects caused by malformed inputs. 5. Stay informed about official patches or updates from OneFlow developers and apply them promptly once available. 6. Consider upgrading to newer versions of OneFlow if they address this vulnerability or provide enhanced input validation. 7. Conduct security testing and fuzzing on AI frameworks to identify similar input validation issues proactively. 8. Use runtime monitoring tools to detect and mitigate anomalous resource consumption patterns. 9. Educate developers and data scientists on secure coding practices for AI workflows to prevent introduction of exploitable flaws. 10. Maintain incident response plans that include scenarios involving AI framework disruptions.