CVE-2026-6839: Samsung ONE Buffer Overflow Vulnerability

CVE-2026-6839 Overview

CVE-2026-6839 is an improper validation vulnerability affecting Samsung Open Source ONE (On-device Neural Engine), a neural network compiler and runtime framework. The vulnerability exists in the constant tensor import functionality where improper validation of STRING tensor offsets allows malformed string metadata to trigger out-of-bounds memory access during processing.

Critical Impact

Attackers can craft malicious neural network models with malformed string tensor metadata to trigger out-of-bounds memory access, potentially leading to information disclosure, memory corruption, or denial of service conditions.

Affected Products

• Samsung Open Source ONE versions prior to commit 1.30.0
• Neural network models processed by vulnerable ONE framework versions
• Applications leveraging ONE for on-device ML inference

Discovery Timeline

• April 22, 2026 - CVE-2026-6839 published to NVD
• April 22, 2026 - Last updated in NVD database

Technical Details for CVE-2026-6839

Vulnerability Analysis

This vulnerability is classified under CWE-1284 (Improper Validation of Specified Quantity in Input), indicating that the affected code fails to properly validate the quantity or bounds of input data before processing. In the context of Samsung ONE, the constant tensor import functionality processes STRING tensor data structures that contain offset information pointing to string data within the tensor.

When parsing a neural network model file, the framework reads offset values from the tensor metadata to locate string data. The vulnerability arises because these offset values are not properly validated against the actual bounds of the tensor data buffer. An attacker can craft a malicious model file containing STRING tensors with offset values that point outside the legitimate data boundaries.

The local attack vector requires user interaction—typically loading a malicious neural network model file. The impact includes potential read access to sensitive memory regions (low confidentiality impact), possible memory corruption (low integrity impact), and high availability impact through crashes or denial of service conditions.

Root Cause

The root cause is insufficient bounds checking during the parsing of STRING tensor offset metadata in the constant tensor import code path. The code trusts offset values provided in the input model file without verifying they fall within valid memory ranges, violating the principle of input validation for untrusted data.

Attack Vector

The attack requires local access and user interaction to execute. An attacker must convince a victim to load a crafted neural network model file containing malformed STRING tensor metadata. When the ONE framework processes this malicious model during import, the invalid offsets cause the code to access memory outside the intended buffer boundaries.

The attack could be delivered through:

• Malicious ML model files shared via email or file sharing platforms
• Compromised model repositories or distribution channels
• Embedded models in applications that process user-supplied neural networks

The vulnerability is exploited during the model loading phase before inference execution, making any application that allows loading external ONE-compatible models potentially vulnerable.

Detection Methods for CVE-2026-6839

Indicators of Compromise

• Unexpected crashes or segmentation faults in applications using Samsung ONE framework during model loading operations
• Memory access violations or exceptions originating from tensor import functions
• Abnormal memory read patterns in ONE framework processes, particularly during STRING tensor processing
• Application logs showing errors related to tensor offset validation or buffer boundary violations

Detection Strategies

• Implement file integrity monitoring for neural network model files in trusted deployment environments
• Deploy runtime application self-protection (RASP) to detect out-of-bounds memory access attempts
• Monitor for crashes with stack traces pointing to Samsung ONE tensor import functions
• Use static analysis tools to scan model files for anomalous offset values before loading

Monitoring Recommendations

• Enable crash reporting and analysis for applications utilizing the ONE framework to identify exploitation attempts
• Implement model file validation checks before importing into the ONE runtime environment
• Monitor system logs for memory access violations associated with ONE framework components
• Consider sandboxing model loading operations to contain potential exploitation impact

How to Mitigate CVE-2026-6839

Immediate Actions Required

• Update Samsung Open Source ONE to version 1.30.0 or later which includes the security fix
• Avoid loading neural network models from untrusted or unverified sources
• Implement application-level validation of model files before processing with the ONE framework
• Review and audit any custom model pipelines that accept external model inputs

Patch Information

Samsung has addressed this vulnerability in commit 1.30.0 of the ONE framework. The fix implements proper bounds validation for STRING tensor offsets during the constant tensor import process. Technical details of the patch are available in the GitHub Pull Request #16481.

Organizations should update to the patched version by pulling the latest code from the Samsung ONE repository and rebuilding their applications. For production deployments, ensure all instances using the ONE framework are updated to include this security fix.

Workarounds

• Restrict model loading to only trusted, internally-developed neural network models until patching is complete
• Implement input validation at the application layer to reject models with suspicious tensor configurations
• Run ONE framework model loading operations in sandboxed environments with restricted memory access
• Consider deploying application-level firewall rules to prevent loading of external model files in sensitive environments