CVE-2026-40449: Samsung ONE Buffer Overflow Vulnerability

CVE-2026-40449 Overview

An integer overflow vulnerability exists in Samsung Open Source ONE, a neural network inference framework. The flaw occurs in buffer size calculation when handling large tensors, which could result in out-of-bounds memory access. This vulnerability affects versions prior to commit 1.30.0.

Critical Impact
Integer overflow in buffer size calculation can lead to out-of-bounds memory access, potentially causing application crashes or allowing attackers to corrupt memory when processing maliciously crafted tensor data.

Affected Products

Samsung Open Source ONE (versions prior to 1.30.0)
Systems utilizing Samsung ONE framework for neural network inference
Applications implementing tensor processing with vulnerable ONE library versions

Discovery Timeline

April 22, 2026 - CVE CVE-2026-40449 published to NVD
April 22, 2026 - Last updated in NVD database

Technical Details for CVE-2026-40449

Vulnerability Analysis

This vulnerability is classified as CWE-190 (Integer Overflow or Wraparound). The flaw exists in the buffer size calculation logic within Samsung Open Source ONE when processing tensor data. When a tensor with excessively large dimensions is provided, the multiplication operation used to calculate the required buffer size can overflow, wrapping around to a smaller value. This results in insufficient memory allocation, and subsequent operations write beyond the allocated buffer boundaries.

The attack requires local access and user interaction (such as opening a malicious model file), but no privileges are required to exploit the vulnerability. The impact includes potential disclosure of sensitive memory contents, modification of adjacent memory regions, and high availability impact through application crashes or denial of service.

Root Cause

The root cause is insufficient validation of tensor dimension sizes before performing arithmetic operations for buffer allocation. When tensor dimensions are multiplied together to calculate total buffer size, the result can exceed the maximum value representable by the integer type used, causing the value to wrap around to a small positive number or even zero. This leads to allocation of an undersized buffer that cannot safely hold the tensor data.

Attack Vector

The attack vector is local, requiring an attacker to craft a malicious model file or input data containing tensors with carefully chosen large dimensions. When a victim application loads or processes this data using a vulnerable version of Samsung ONE, the integer overflow triggers during buffer allocation. The subsequent memory access operations then write or read outside the allocated bounds, potentially leading to information disclosure, memory corruption, or denial of service.

The vulnerability can be exploited through applications that process untrusted neural network models or tensor data without proper validation of input dimensions before passing them to the ONE framework.

Detection Methods for CVE-2026-40449

Indicators of Compromise

Unexpected application crashes when loading or processing neural network models
Memory corruption errors or segmentation faults in applications using Samsung ONE
Anomalous memory allocation patterns showing unusually small allocations for large tensor operations
Debug logs indicating buffer overflows or out-of-bounds access in tensor processing functions

Detection Strategies

Monitor for application crashes with stack traces pointing to Samsung ONE tensor handling functions
Implement runtime bounds checking for buffer operations in development and testing environments
Use address sanitizer (ASan) tools during testing to detect out-of-bounds memory access
Review logs for allocation failures followed by memory corruption indicators

Monitoring Recommendations

Enable memory debugging features when running applications that process untrusted model files
Implement file integrity monitoring for neural network model files in production environments
Monitor system logs for repeated crashes in applications utilizing Samsung ONE framework
Deploy endpoint detection solutions capable of identifying memory corruption exploitation attempts

How to Mitigate CVE-2026-40449

Immediate Actions Required

Update Samsung Open Source ONE to version 1.30.0 or later
Review applications using Samsung ONE and assess exposure to untrusted input data
Implement input validation for tensor dimensions before processing
Restrict processing of neural network models to trusted sources only
Consider deploying applications with memory protection features enabled (ASLR, stack canaries)

Patch Information

Samsung has addressed this vulnerability in commit 1.30.0 of the Open Source ONE project. The fix implements proper validation of tensor dimensions and adds overflow checks before buffer size calculations. Organizations should update to the patched version as soon as possible. For technical details on the fix, refer to GitHub Pull Request 16481.

Workarounds

Validate tensor dimensions at the application level before passing data to Samsung ONE
Limit the maximum allowable tensor dimensions to prevent overflow conditions
Compile applications with integer overflow detection flags where supported
Avoid processing untrusted or unverified neural network model files until patched

bash

# Update Samsung ONE to patched version
git clone https://github.com/Samsung/ONE.git
cd ONE
git checkout 1.30.0
# Follow build instructions for your platform