CVE-2026-41666: Samsung ONE Buffer Overflow Vulnerability

CVE-2026-41666 Overview

An integer overflow vulnerability exists in Samsung Open Source ONE (On-device Neural Engine) within the tensor copy size calculation component. This flaw can lead to out-of-bounds memory access during loop state propagation operations. The vulnerability affects versions prior to commit 1.30.0 of the ONE framework, which is Samsung's open-source neural network compiler and inference runtime.

Critical Impact

Successful exploitation of this integer overflow vulnerability could allow an attacker to cause denial of service conditions or potentially achieve arbitrary code execution through out-of-bounds memory access in machine learning workloads.

Affected Products

• Samsung Open Source ONE (On-device Neural Engine) prior to version 1.30.0
• Applications and systems utilizing vulnerable ONE library versions for neural network inference
• Devices running Samsung's neural network compiler with unpatched ONE dependencies

Discovery Timeline

• April 22, 2026 - CVE-2026-41666 published to NVD
• April 22, 2026 - Last updated in NVD database

Technical Details for CVE-2026-41666

Vulnerability Analysis

This vulnerability is classified as CWE-190 (Integer Overflow or Wraparound). The flaw resides in the tensor copy size calculation logic within Samsung's ONE neural network framework. When processing tensor operations during loop state propagation, the size calculation can overflow, resulting in a smaller-than-expected buffer allocation or incorrect bounds checking. This creates a window for out-of-bounds memory access when the system attempts to copy tensor data using the overflowed size value.

The attack requires local access and user interaction, such as loading a maliciously crafted neural network model. While there is no authentication required to trigger the vulnerability, successful exploitation depends on the attacker's ability to deliver a specially crafted model file to the target system.

Root Cause

The root cause is improper handling of integer arithmetic in tensor size calculations. When computing the total size for tensor copy operations during loop state propagation, the multiplication of tensor dimensions can exceed the maximum value representable by the integer type used. Without proper overflow checks, the calculation wraps around to a small or negative value, leading to subsequent out-of-bounds memory operations.

Attack Vector

An attacker can exploit this vulnerability by crafting a malicious neural network model with carefully constructed tensor dimensions designed to trigger the integer overflow. When a victim processes this model using an affected version of Samsung ONE, the overflow occurs during tensor copy size calculation, potentially leading to:

1. Out-of-bounds read - Leaking sensitive memory contents
2. Out-of-bounds write - Corrupting adjacent memory regions
3. Denial of service - Crashing the application or system

The attack requires local access (the attacker must be able to place a malicious model file on the target system) and user interaction (the victim must load and process the malicious model). For technical details on the vulnerability and its fix, refer to the GitHub Pull Request #16481.

Detection Methods for CVE-2026-41666

Indicators of Compromise

• Unexpected crashes or segmentation faults in applications using Samsung ONE for neural network inference
• Abnormal memory access patterns in processes loading neural network models
• Presence of unusually large or malformed .circle, .tflite, or other model files supported by ONE
• Error logs indicating tensor dimension calculation failures or memory allocation issues

Detection Strategies

• Monitor for integer overflow conditions in tensor processing applications using runtime sanitizers (e.g., AddressSanitizer, UndefinedBehaviorSanitizer)
• Implement file integrity monitoring for neural network model files to detect tampering
• Deploy endpoint detection solutions capable of identifying memory corruption exploitation attempts
• Review application logs for unexpected errors during model loading or inference operations

Monitoring Recommendations

• Enable verbose logging for Samsung ONE-based applications to capture tensor dimension calculations
• Configure system monitoring to alert on unexpected process terminations in machine learning workloads
• Implement model validation checks before processing to verify tensor dimensions are within expected bounds
• Monitor memory allocation patterns for anomalies that may indicate exploitation attempts

How to Mitigate CVE-2026-41666

Immediate Actions Required

• Update Samsung Open Source ONE to version 1.30.0 or later immediately
• Audit systems to identify all applications and deployments using vulnerable ONE versions
• Restrict access to model loading functionality to trusted users and processes
• Implement input validation for neural network models before processing

Patch Information

Samsung has addressed this vulnerability in the ONE framework. The fix is available in version 1.30.0 and later. Organizations should update their ONE installations by pulling the latest release from the official Samsung ONE GitHub repository. Details of the patch can be reviewed in the GitHub Pull Request #16481.

Workarounds

• Validate tensor dimensions in model files before loading to ensure they do not exceed safe integer bounds
• Implement application-level size checks for tensor operations as an additional defense layer
• Restrict model loading to only trusted, verified model files from known sources
• Run ONE-based applications in sandboxed environments to limit the impact of potential exploitation
• Consider using memory-safe compilation options when building ONE from source

To update Samsung ONE to a patched version, rebuild your installation from the official repository with version 1.30.0 or later:

# Clone or update Samsung ONE repository
git clone https://github.com/Samsung/ONE.git
cd ONE
git checkout v1.30.0

# Build with recommended security flags
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)