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:

1. The ONE runtime parses the model and identifies intermediate tensor requirements
2. During scratch buffer initialization, the size calculation overflows
3. A smaller-than-required buffer is allocated
4. Subsequent tensor operations write beyond the allocated buffer boundaries
5. 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