CVE-2026-40450: Samsung ONE Buffer Overflow Vulnerability

CVE-2026-40450 Overview

CVE-2026-40450 is an integer overflow vulnerability affecting Samsung Open Source ONE (On-device Neural Engine), a machine learning compiler and runtime framework. The vulnerability occurs in the output tensor copy size calculation, where an integer overflow can lead to incorrect copy lengths and subsequent memory corruption when processing oversized tensors.

Critical Impact
This integer overflow vulnerability can lead to memory corruption, potentially allowing attackers to crash applications or achieve unintended behavior through maliciously crafted tensor inputs.

Affected Products

Samsung Open Source ONE versions prior to commit 1.30.0

Discovery Timeline

2026-04-22 - CVE CVE-2026-40450 published to NVD
2026-04-22 - Last updated in NVD database

Technical Details for CVE-2026-40450

Vulnerability Analysis

This vulnerability is classified under CWE-190 (Integer Overflow or Wraparound). The flaw exists in how Samsung ONE calculates the copy size for output tensors. When processing tensors with extremely large dimensions, the multiplication operations used to compute the total copy size can exceed the maximum value representable by the integer type, causing the value to wrap around to a much smaller number.

This incorrect size calculation results in an undersized memory copy operation, which can lead to:

Buffer overflows when subsequent operations assume the full data was copied
Memory corruption affecting adjacent memory regions
Application crashes or denial of service conditions
Potential for exploitation if an attacker can control the tensor dimensions

The local attack vector requires user interaction, meaning an attacker would need to convince a user to load a maliciously crafted model or tensor data.

Root Cause

The root cause is improper validation and handling of arithmetic operations when calculating tensor copy sizes. When tensor dimensions are multiplied together to determine the total byte count for memory operations, the calculation does not check for or prevent integer overflow conditions. This is a common issue in deep learning frameworks where tensor shapes can vary dramatically and involve large numerical values.

Attack Vector

The attack requires local access and user interaction. An attacker could craft a malicious neural network model or tensor input file with oversized dimensions designed to trigger the integer overflow during output tensor copy operations. When a user loads this malicious input into an application using the vulnerable ONE framework, the overflow occurs, leading to memory corruption.

The vulnerability could be exploited through:

Maliciously crafted .tflite or other model files
Applications that process user-supplied tensor data
Model conversion utilities that handle untrusted input

Detection Methods for CVE-2026-40450

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 tensor dimensions in processed model files that appear unusually large
Log entries indicating memory allocation or copy failures in ONE-based applications

Detection Strategies

Monitor for crashes in applications that utilize the Samsung ONE framework
Implement input validation to detect suspiciously large tensor dimensions before processing
Use memory sanitizers (AddressSanitizer, MemorySanitizer) during development and testing to catch overflow-related corruption
Audit model files for abnormal dimension values that could trigger overflow conditions

Monitoring Recommendations

Enable crash reporting and analysis for applications using Samsung ONE
Log tensor dimension values during model loading to identify anomalous inputs
Implement runtime bounds checking for tensor operations in production environments

How to Mitigate CVE-2026-40450

Immediate Actions Required

Update Samsung Open Source ONE to version 1.30.0 or later
Review and validate all tensor inputs before processing, especially from untrusted sources
Implement input size limits for tensor dimensions in applications using ONE
Consider sandboxing applications that process untrusted model files

Patch Information

Samsung has addressed this vulnerability in GitHub Pull Request #16481. The fix is included in version 1.30.0 and later. Organizations using Samsung ONE should update to the patched version immediately.

To update, pull the latest changes from the Samsung ONE repository:

bash

git clone https://github.com/Samsung/ONE.git
cd ONE
git checkout 1.30.0

Workarounds

Implement pre-processing validation that checks tensor dimensions before passing to ONE
Add overflow detection logic to custom code that interfaces with ONE tensor operations
Restrict processing to only trusted model files from verified sources
Run ONE-based applications in sandboxed environments to limit the impact of potential exploitation