Samsung ONE EUVD-2026-24614

Exploitation requires the following specific, concrete conditions: (1) Processing of a neural network model containing tensors with specially crafted dimensions designed to trigger integer overflow in the size calculation-this is NOT a default-configuration scenario and requires attacker-controlled model input; (2) User interaction (UI:R per CVSS) meaning a developer, data scientist, or end-user must explicitly load the malicious model via Samsung ONE's model loading API or command-line tools; (3) Local system access (AV:L) to invoke the vulnerable code-the attacker cannot trigger this remotely over a network; (4) Absence of input validation or model size checks in the deployment that might reject oversized or suspicious tensor dimensions. The attack does NOT succeed against deployments that: enforce strict model size limits, use sandboxed execution of model loading, run ONE in read-only or restricted memory environments, or disable dynamic model loading entirely. These conditions mean the vulnerability is primarily a risk for development environments, research platforms, or edge deployments where users routinely load external or untrusted models.

Risk assessment reveals a moderate but context-dependent threat. CVSS 5.3 (AV:L/AC:H/PR:N/UI:R) indicates local-only access, high attack complexity, no privilege escalation required, and mandatory user interaction-all factors that significantly restrict real-world exploitability. The local attack vector eliminates remote exploitation scenarios common in higher-severity integer overflow bugs. High attack complexity (AC:H) further suggests the flaw requires specific conditions: processing unusually large tensors or particular model architectures. User interaction requirement (UI:R) means the victim must actively invoke the vulnerable code path, typically by opening a malicious neural network model. EPSS score is not provided, but the attack complexity and local-only vector suggest exploitation probability is substantially lower than the raw CVSS might imply. No KEV confirmation or public POC is reported, indicating no widespread or targeted active exploitation at time of analysis. The integrity impact is limited (I:L) while availability impact is high (A:H), suggesting the primary risk is denial of service rather than data exfiltration. For organizations deploying Samsung ONE on edge devices or mobile platforms, this represents a moderate-priority but not critical vulnerability-patch deployment should follow standard update cycles rather than emergency protocols.

An attacker crafts a malicious neural network model file (e.g., TensorFlow, ONNX, or ONE-format) containing tensors with dimensions calculated to trigger integer overflow in the allocation size calculation-for example, a tensor with shape [65536, 65536, 65536] on a 32-bit system would cause the size computation to wrap to a small value. When a user or developer loads this model using Samsung ONE for inference or compilation on an edge device or mobile platform, the integer overflow causes insufficient memory allocation. Subsequent writes to the tensor trigger a heap buffer overflow, corrupting adjacent heap metadata or objects. The attacker achieves denial of service by crashing the application, or potentially achieves limited code execution through heap exploitation if the corruption can be leveraged to overwrite function pointers or critical structures.