CVE-2025-70999 identifies a vulnerability in the OneFlow deep learning framework version 0.9.0, specifically within the flow.cuda.get_device_capability() function responsible for querying GPU device capabilities. The vulnerability stems from insufficient validation of the GPU device ID parameter, which an attacker can manipulate by providing a crafted device ID. This malformed input causes the function to behave unexpectedly, leading to a Denial of Service (DoS) condition, such as application crashes or hangs. Since OneFlow is used for GPU-accelerated machine learning tasks, this flaw can disrupt critical AI workloads. The vulnerability does not require authentication or user interaction, making it easier for attackers to exploit if they have access to the environment where OneFlow is running. No CVSS score has been assigned yet, and no patches or known exploits are currently available. The flaw affects availability but does not appear to compromise confidentiality or integrity. The lack of patches necessitates proactive monitoring and mitigation strategies. Given the increasing adoption of OneFlow in AI research and production environments, this vulnerability poses a tangible risk to organizations relying on GPU-based computation.

For European organizations, the primary impact of CVE-2025-70999 is operational disruption due to Denial of Service conditions in AI and machine learning workloads that utilize OneFlow with GPU acceleration. This can lead to downtime in research environments, delays in AI model training or inference, and potential cascading effects on dependent services or products. Industries such as automotive, finance, healthcare, and telecommunications, which increasingly rely on AI, may experience productivity losses or service interruptions. Cloud service providers hosting AI workloads with OneFlow could see degraded service quality or availability issues affecting multiple clients. While confidentiality and integrity impacts are not evident, the availability impact can affect business continuity and service-level agreements. The absence of known exploits reduces immediate risk but does not eliminate the threat, especially as attackers may develop exploits once the vulnerability becomes widely known.

1. Implement strict input validation on GPU device ID parameters within any custom wrappers or integrations around OneFlow to prevent malformed inputs. 2. Monitor application logs and GPU-related error messages for signs of crashes or abnormal behavior linked to device capability queries. 3. Isolate OneFlow workloads in controlled environments with limited access to reduce the attack surface. 4. Restrict user permissions to prevent unauthorized users from invoking GPU device queries with arbitrary parameters. 5. Engage with OneFlow maintainers or community to track patch releases and apply updates promptly once available. 6. Consider fallback mechanisms or redundancy in AI workloads to maintain availability during potential DoS events. 7. Conduct security assessments and penetration testing focusing on GPU device interaction components. 8. Educate developers and operators about this vulnerability to increase awareness and readiness.