CVE-2025-71004 identifies a segmentation violation vulnerability within the oneflow.logical_or component of OneFlow version 0.9.0. OneFlow is an open-source deep learning framework used for AI model training and inference. The vulnerability arises when the component processes crafted inputs that trigger a segmentation fault, leading to a Denial of Service (DoS) condition. This means an attacker can cause the affected application or service to crash or become unresponsive, disrupting normal operations. The flaw does not require authentication or user interaction, making it easier for remote attackers to exploit if the vulnerable component is exposed. No specific affected versions beyond v0.9.0 are listed, and no patches or known exploits are currently documented. The lack of a CVSS score indicates the vulnerability is newly disclosed and not yet fully assessed. The segmentation violation impacts availability but does not appear to compromise confidentiality or integrity. The vulnerability's exploitation scope is limited to environments running the vulnerable OneFlow version and invoking the logical_or operation with malicious input. Given OneFlow's role in AI workloads, affected systems could include research institutions, enterprises, and cloud services utilizing this framework. The absence of patches necessitates interim mitigations such as input sanitization and monitoring. Overall, this vulnerability represents a denial of service risk that could interrupt AI processing pipelines and related services.

For European organizations, the primary impact of CVE-2025-71004 is service disruption due to Denial of Service conditions in AI and machine learning environments using OneFlow v0.9.0. This could affect research labs, universities, and enterprises relying on AI frameworks for data analysis, automation, or product development. Disruptions could delay critical AI workloads, impact productivity, and cause operational downtime. Although no data breach or code execution is indicated, availability loss can have cascading effects, especially in sectors like finance, healthcare, and manufacturing where AI-driven insights are time-sensitive. Cloud service providers hosting OneFlow-based services might also face customer impact and reputational damage. The lack of known exploits reduces immediate risk, but the ease of triggering a segmentation fault with crafted input means attackers could launch DoS attacks remotely if the vulnerable component is exposed. European organizations with limited patch management capabilities or those using older OneFlow versions are at higher risk. Overall, the impact is moderate but significant in AI-dependent environments.

To mitigate CVE-2025-71004, organizations should first identify any deployments of OneFlow v0.9.0 within their infrastructure, especially in AI and machine learning pipelines. Until an official patch is released, implement strict input validation and sanitization on data fed into the oneflow.logical_or component to prevent malformed inputs from triggering the segmentation fault. Employ runtime monitoring and anomaly detection to identify crashes or unusual behavior indicative of exploitation attempts. Isolate vulnerable systems from untrusted networks to reduce exposure to remote attacks. Engage with the OneFlow community or maintainers to track patch releases and apply updates promptly once available. Additionally, consider deploying fallback or redundancy mechanisms in AI workflows to maintain availability during potential DoS incidents. Document and review incident response plans to handle service disruptions caused by this vulnerability. Finally, restrict access to AI processing environments to trusted users and networks to minimize attack surface.