CVE-2026-41667: Samsung ONE Buffer Overflow Vulnerability

CVE-2026-41667 Overview

An integer overflow vulnerability exists in Samsung Open Source ONE (On-device Neural Engine) within the constant tensor data size calculation functionality. This vulnerability occurs when processing large constant nodes, causing incorrect buffer sizing that could lead to memory corruption or denial of service conditions.

Critical Impact

Integer overflow in tensor data size calculations can result in undersized buffer allocations, potentially causing heap corruption, out-of-bounds memory access, and application crashes when processing maliciously crafted neural network models.

Affected Products

• Samsung Open Source ONE versions prior to commit 1.30.0
• On-device Neural Engine implementations using affected ONE library versions
• Applications leveraging ONE framework for neural network inference

Discovery Timeline

• 2026-04-22 - CVE CVE-2026-41667 published to NVD
• 2026-04-22 - Last updated in NVD database

Technical Details for CVE-2026-41667

Vulnerability Analysis

This vulnerability is classified as CWE-190 (Integer Overflow or Wraparound). The flaw resides in the constant tensor data size calculation logic within Samsung's ONE neural network framework. When the framework processes constant nodes with exceptionally large dimensions, the multiplication operations used to calculate buffer sizes can overflow, wrapping around to a smaller value than actually required.

The resulting undersized buffer allocation creates a dangerous condition where subsequent operations attempting to populate the buffer with tensor data write beyond the allocated memory boundary. This memory corruption can manifest as application crashes, data corruption, or potentially be leveraged for more severe exploitation in specific deployment contexts.

The local attack vector requires user interaction, typically through loading a maliciously crafted neural network model file. An attacker could craft a model with intentionally large tensor dimensions designed to trigger the integer overflow condition during model loading or inference operations.

Root Cause

The root cause is improper handling of arithmetic operations when calculating memory requirements for constant tensor data. The calculation fails to validate that the product of tensor dimensions does not exceed the maximum value representable by the integer type used, leading to silent wraparound behavior. This is a classic integer overflow pattern where the code trusts that dimension values will always result in manageable buffer sizes.

Attack Vector

The attack vector requires local access with user interaction. An attacker must convince a user or application to load a maliciously crafted neural network model. The attack scenario involves:

1. Crafting a neural network model file containing constant nodes with dimensions calculated to trigger integer overflow when multiplied
2. Distributing the malicious model through social engineering or supply chain compromise
3. When the target application loads the model using the vulnerable ONE framework, the overflow occurs during buffer allocation
4. The undersized buffer leads to heap corruption when tensor data is written, potentially causing denial of service or enabling further exploitation

The vulnerability mechanism can be understood as follows: when tensor dimensions (e.g., width × height × channels × batch_size × element_size) are multiplied together to determine buffer size, values near the integer maximum can cause the result to overflow and wrap to a small positive number. This results in allocating far less memory than needed for the actual tensor data. Technical details and the fix can be found in the GitHub Pull Request #16481.

Detection Methods for CVE-2026-41667

Indicators of Compromise

• Unexpected application crashes when loading neural network models with unusually large constant tensors
• Memory corruption errors or heap-related crashes in applications using Samsung ONE framework
• Anomalous model files with suspiciously large tensor dimension values that exceed practical use cases
• Segmentation faults or access violations during model inference initialization

Detection Strategies

• Monitor applications using Samsung ONE framework for abnormal termination patterns during model loading
• Implement input validation to reject model files with tensor dimensions that would cause overflow when multiplied
• Use memory sanitizers (ASan, MSan) during development and testing to detect buffer overflows early
• Deploy runtime integrity monitoring to detect heap corruption indicative of exploitation attempts

Monitoring Recommendations

• Enable crash dump analysis for applications using the ONE neural network framework
• Implement logging for model loading operations, capturing tensor dimension metadata
• Monitor for patterns of repeated crashes that may indicate exploitation attempts
• Track file system access to detect loading of unusual or externally-sourced model files

How to Mitigate CVE-2026-41667

Immediate Actions Required

• Update Samsung Open Source ONE to version 1.30.0 or later
• Review and validate all neural network model files before loading in production environments
• Implement input validation at the application layer to reject models with unreasonably large tensor dimensions
• Consider sandboxing or isolation for model loading operations in security-sensitive deployments

Patch Information

The vulnerability has been addressed in Samsung Open Source ONE version 1.30.0. The fix is documented in GitHub Pull Request #16481. Organizations should update their ONE framework installations to the patched version as soon as possible.

Workarounds

• Implement application-level validation to check tensor dimension products before passing models to the ONE framework
• Use 64-bit integer types for size calculations in wrapper code to detect overflow conditions
• Restrict model loading to trusted, validated sources only until the patch can be applied
• Deploy application sandboxing to limit the impact of potential exploitation

For environments where immediate patching is not feasible, consider implementing bounds checking on tensor dimensions at the application layer before model loading. Validate that dimension products do not exceed reasonable thresholds for your use case.