Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2025-65886 is a high-severity Uncontrolled Resource Consumption (CWE-400) vulnerability in Oneflow Oneflow. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique OS Exhaustion Flood (T1499.001); ranked at the 30th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to SC-5 (Denial-of-service Protection) and SC-6 (Resource Availability) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-65886 is a shape mismatch vulnerability affecting OneFlow version 0.9.0, an open-source framework handling tensor operations. The flaw enables attackers to trigger a Denial of Service (DoS) condition by supplying crafted tensor shapes, as documented under CWE-400 (Uncontrolled Resource Consumption). Published on 2026-01-28 with a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H), it poses a high-impact availability risk without compromising confidentiality or integrity.
The vulnerability is exploitable by unauthenticated attackers over the network with low attack complexity and no user interaction required. By delivering specially crafted tensor shapes to a vulnerable OneFlow instance, attackers can cause resource exhaustion or crashes, leading to service disruption. This scenario targets deployments exposing OneFlow endpoints, such as in distributed training or inference setups.
Mitigation details and patches are referenced in the OneFlow GitHub repository (https://github.com/Oneflow-Inc/oneflow), specific issue tracker (https://github.com/Oneflow-Inc/oneflow/issues/10666), and related sites including http://oneflow.com and https://github.com/Daisy2ang. Security practitioners should review these for upgrade guidance or workarounds to address the issue in v0.9.0.
As a tensor shape issue in an AI/ML framework, CVE-2025-65886 highlights risks in deep learning pipelines where malformed inputs can propagate to DoS, though no real-world exploitation has been reported in the provided data.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-206484
Vulnerability Data
A shape mismatch vulnerability in OneFlow v0.9.0 allows attackers to cause a Denial of Service (DoS) via supplying crafted tensor shapes.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
SC-5 directly limits the effects of resource-exhaustion events that constitute uncontrolled consumption.
SC-6 enforces explicit allocation limits on resources, structurally preventing the weakness from occurring.
Process isolation confines resource consumption to separate domains, reducing blast radius without stopping the root flaw.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Explicitly requires monitoring and maintaining resource capacity, directly addressing uncontrolled consumption to preserve availability.
Continuous monitoring of computing resources can detect resource exhaustion but does not itself enforce allocation limits.
Resilience mechanisms such as avoiding single points of failure indirectly reduce impact of resource exhaustion.
Hardened configuration baselines can include resource quotas and limits that constrain consumption.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Resource-utilization monitoring and alerting on bottlenecks or overloads limits the impact of denial-of-service or resource-exhaustion attacks.
By continuously monitoring utilization, stress-testing peak loads, and maintaining documented plans to scale or throttle resources, the control directly limits an attacker’s ability to drive a system into uncontrolled resource exhaustion.
Pre-agreed severity-based prioritization and resource allocation during incident triage reduce the likelihood that an attacker-induced resource exhaustion will overwhelm the organization before corrective action is taken.
Business-continuity plans that include resource-management controls reduce the likelihood that an attacker can trigger uncontrolled resource consumption by forcing the system into a degraded or fallback state.
Defining RTOs and capacity requirements for ICT services during business-impact analysis forces organizations to provision sufficient resources and throttling mechanisms, reducing the likelihood that an attacker can induce denial-of-service through uncontrolled resource consumption.
Early notification of anomalous resource consumption or system malfunctions enables throttling or isolation before availability is lost.