CVE-2026-40448: Samsung ONE Integer Overflow Vulnerability

CVE-2026-40448 Overview

CVE-2026-40448 is an integer overflow vulnerability affecting Samsung Open Source ONE, an on-device neural network framework. The vulnerability occurs in the tensor allocation size calculation, which could lead to insufficient memory allocation when processing large tensors. This flaw allows attackers to potentially cause denial of service conditions or corrupt memory through undersized buffer allocations.

Critical Impact

Integer overflow in tensor allocation could result in undersized memory buffers, potentially leading to denial of service or memory corruption when processing maliciously crafted neural network models.

Affected Products

• Samsung Open Source ONE (versions prior to commit 1.30.0)

Discovery Timeline

• April 22, 2026 - CVE-2026-40448 published to NVD
• April 22, 2026 - Last updated in NVD database

Technical Details for CVE-2026-40448

Vulnerability Analysis

This vulnerability is classified as CWE-190 (Integer Overflow or Wraparound). The flaw exists in the tensor allocation size calculation logic within Samsung's ONE neural network framework. When processing tensor dimensions for large neural network models, the size calculation can overflow, resulting in a smaller-than-expected memory allocation. Subsequent operations that write to this undersized buffer can cause memory corruption or crashes.

The vulnerability requires local access and user interaction to exploit, meaning an attacker would need to convince a user to load a maliciously crafted neural network model. While the attack complexity is high, successful exploitation could impact the integrity and availability of the affected system.

Root Cause

The root cause is improper handling of integer arithmetic when calculating memory requirements for tensor allocations. When tensor dimensions are sufficiently large, multiplying them together without overflow checks causes the result to wrap around to a smaller value. The framework then allocates an insufficient buffer based on this incorrect size calculation.

Attack Vector

The attack vector is local, requiring an attacker to supply a malicious neural network model file to the target system. The exploitation scenario involves:

1. An attacker crafts a neural network model with tensor dimensions designed to trigger the integer overflow
2. The victim loads the malicious model using the ONE framework
3. During tensor allocation, the size calculation overflows, allocating an undersized buffer
4. Subsequent tensor operations write beyond the allocated buffer, causing memory corruption or denial of service

The vulnerability mechanism centers on insufficient bounds checking during tensor size computation. When the framework calculates the total memory required for a tensor by multiplying its dimensions, extremely large dimension values can cause the product to exceed the maximum representable integer value, wrapping to a smaller number. Technical details are available in the GitHub Pull Request by Samsung.

Detection Methods for CVE-2026-40448

Indicators of Compromise

• Unexpected application crashes or segmentation faults when loading neural network models
• Memory allocation errors or out-of-memory conditions despite available system memory
• Abnormal process termination in applications using the ONE framework
• Core dumps or crash logs indicating buffer overflows in tensor allocation routines

Detection Strategies

• Monitor for crashes in applications utilizing the Samsung ONE framework, particularly during model loading operations
• Implement file integrity monitoring for neural network model files to detect potentially malicious modifications
• Deploy runtime application self-protection (RASP) solutions to detect memory corruption attempts
• Enable address sanitizer (ASan) during development and testing to identify integer overflow conditions

Monitoring Recommendations

• Log and alert on repeated application crashes associated with the ONE framework
• Monitor system logs for memory-related errors in processes using neural network inference
• Track file access patterns to detect loading of untrusted model files
• Implement anomaly detection for unusual tensor dimension values in model metadata

How to Mitigate CVE-2026-40448

Immediate Actions Required

• Update Samsung Open Source ONE to version 1.30.0 or later which contains the fix
• Restrict access to neural network model files and validate their source before loading
• Implement input validation to reject models with suspiciously large tensor dimensions
• Consider sandboxing applications that process untrusted neural network models

Patch Information

Samsung has addressed this vulnerability in commit 1.30.0 of the ONE framework. The fix is available through the GitHub Pull Request by Samsung. Users should update to this version or later to remediate the vulnerability.

Workarounds

• Only load neural network models from trusted sources until the patch can be applied
• Implement application-level validation of tensor dimensions before passing models to the framework
• Run ONE-based applications in sandboxed environments with limited privileges
• Consider using static analysis tools to identify integer overflow patterns in custom implementations

To update to the patched version, clone or pull the latest repository changes and rebuild the framework:

# Update Samsung ONE framework to patched version
git clone https://github.com/Samsung/ONE.git
cd ONE
git checkout 1.30.0
# Follow build instructions in the repository documentation