CVE-2025-70999 identifies a vulnerability in the OneFlow deep learning framework version 0.9.0, specifically within the flow.cuda.get_device_capability() component. This function is responsible for querying the capabilities of GPU devices by their device IDs. The vulnerability stems from insufficient validation of the device ID input, allowing an attacker to supply a specially crafted device ID that the system cannot properly handle. This leads to resource exhaustion or other failure modes that cause a Denial of Service (DoS), effectively disrupting the availability of the OneFlow service or application relying on it. The CVSS v3.1 base score is 7.5, indicating a high severity level, with an attack vector of network (AV:N), low attack complexity (AC:L), no privileges required (PR:N), no user interaction (UI:N), and an impact limited to availability (A:H) without affecting confidentiality or integrity. The vulnerability is categorized under CWE-400 (Uncontrolled Resource Consumption), highlighting that the flaw allows attackers to consume resources excessively, leading to service degradation or crash. No patches or exploits are currently publicly available, but the flaw poses a significant risk to environments using OneFlow for GPU-accelerated tasks, especially in AI and machine learning domains. The lack of authentication requirements and user interaction makes this vulnerability easier to exploit remotely, increasing its threat potential.

For European organizations, the primary impact of CVE-2025-70999 is the potential disruption of services relying on OneFlow for GPU-accelerated computing tasks. This can affect AI research institutions, data centers, and enterprises utilizing OneFlow in production environments, leading to downtime and loss of productivity. The denial of service could interrupt critical workflows, delay data processing, and impact dependent applications. Since the vulnerability does not affect confidentiality or integrity, data breaches or manipulation are not a direct concern. However, service unavailability can have cascading effects, such as missed deadlines, reduced operational efficiency, and reputational damage. Organizations in sectors heavily invested in AI and machine learning, including automotive, finance, healthcare, and academia, may experience operational setbacks. Additionally, cloud service providers hosting OneFlow workloads could face customer impact and SLA violations. 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.

To mitigate CVE-2025-70999, organizations should first monitor OneFlow vendor communications for official patches or updates addressing the device ID validation flaw and apply them promptly. In the absence of patches, implement strict input validation and sanitization at the application or middleware level to reject malformed or unexpected GPU device IDs before they reach the vulnerable function. Employ network-level protections such as firewalls and intrusion detection systems to limit access to OneFlow services, restricting exposure to trusted users and IP ranges. Conduct regular security assessments and penetration testing focused on GPU-accelerated components to identify similar weaknesses. Additionally, implement resource usage monitoring and automated alerts to detect abnormal consumption patterns indicative of exploitation attempts. For critical environments, consider isolating GPU workloads or deploying fallback mechanisms to maintain availability during an attack. Finally, maintain comprehensive logging to facilitate incident response and forensic analysis if exploitation occurs.