CVE-2026-41665: Samsung ONE Buffer Overflow Vulnerability

CVE-2026-41665 Overview

CVE-2026-41665 is an integer overflow vulnerability affecting Samsung Open Source ONE, an on-device neural engine framework. The flaw exists in the scratch buffer initialization size calculation, which can cause incorrect memory initialization when processing large intermediate tensors. This vulnerability falls under CWE-190 (Integer Overflow or Wraparound) and affects versions prior to commit 1.30.0.

Critical Impact
An integer overflow in memory allocation calculations can lead to undersized buffer allocation, potentially resulting in out-of-bounds memory access, application crashes, or denial of service when processing maliciously crafted neural network models.

Affected Products

Samsung Open Source ONE versions prior to 1.30.0

Discovery Timeline

2026-04-22 - CVE CVE-2026-41665 published to NVD
2026-04-22 - Last updated in NVD database

Technical Details for CVE-2026-41665

Vulnerability Analysis

This vulnerability stems from improper handling of integer arithmetic during scratch buffer size calculations in Samsung's ONE neural network runtime. When the neural network runtime processes models containing exceptionally large intermediate tensors, the size calculation for allocating scratch buffers can overflow, wrapping around to a smaller value than required. This results in insufficient memory being allocated for the actual tensor operations.

The local attack vector requires user interaction, indicating that exploitation likely involves convincing a user to load a maliciously crafted neural network model. While confidentiality impact is minimal, the vulnerability can result in significant availability impact through application crashes and moderate integrity impact through memory corruption.

Root Cause

The root cause is an integer overflow condition (CWE-190) in the scratch buffer size calculation logic. When calculating the required buffer size for intermediate tensor operations, the code performs arithmetic operations that can exceed the maximum value representable by the integer type being used. Without proper overflow checks, the result wraps around to a small positive value, leading to an undersized memory allocation.

Attack Vector

Exploitation requires local access with user interaction. An attacker would need to craft a malicious neural network model file with tensor dimensions specifically designed to trigger the integer overflow during buffer size calculation. When a victim loads this model using an application built on the vulnerable ONE framework, the following sequence occurs:

The ONE runtime parses the model and identifies intermediate tensor requirements
During scratch buffer initialization, the size calculation overflows
A smaller-than-required buffer is allocated
Subsequent tensor operations write beyond the allocated buffer boundaries
This can lead to memory corruption, crashes, or denial of service

Detection Methods for CVE-2026-41665

Indicators of Compromise

Unexpected crashes in applications using Samsung ONE neural network runtime
Memory corruption errors or segmentation faults during model inference operations
Application logs showing abnormal memory allocation patterns or unusually small buffer sizes
Presence of neural network model files with abnormally large tensor dimension specifications

Detection Strategies

Monitor for crash patterns in ONE-based applications, particularly during model loading or inference
Implement file integrity monitoring on neural network model files in sensitive environments
Use memory debugging tools (e.g., AddressSanitizer, Valgrind) during development to detect buffer overflows
Review application logs for memory allocation anomalies when processing external model files

Monitoring Recommendations

Enable crash reporting and analysis for applications utilizing the Samsung ONE framework
Implement input validation for neural network models before loading, checking for unreasonably large tensor dimensions
Deploy endpoint detection and response (EDR) solutions to identify anomalous application behavior
Monitor for attempts to introduce untrusted model files into systems running ONE-based applications

How to Mitigate CVE-2026-41665

Immediate Actions Required

Update Samsung Open Source ONE to version 1.30.0 or later immediately
Review and validate all neural network model files loaded by affected applications
Restrict access to model file directories to prevent introduction of malicious files
Consider temporarily disabling functionality that loads external model files until patches are applied

Patch Information

Samsung has addressed this vulnerability in the ONE framework. The fix is available through the GitHub Pull Request for ONE. Organizations using the ONE neural network runtime should update to version 1.30.0 or apply the referenced patch to resolve this integer overflow vulnerability.

Workarounds

Implement input validation to reject neural network models with tensor dimensions exceeding safe thresholds
Restrict model loading to trusted, verified sources only
Run ONE-based applications in sandboxed environments to limit the impact of potential exploitation
Add size overflow checks in wrapper code before calling ONE buffer initialization functions