Executive Summary

This vulnerability is an integer overflow issue in the memory size calculation for tensors within the Samsung Open Source ONE framework. The original code used a 32-bit signed integer to represent the number of elements in a tensor, which could overflow when handling very large tensors. This overflow causes incorrect memory allocation sizes, potentially leading to insufficient memory allocation.

The problem was fixed by changing the data type to a 64-bit integer, adding explicit overflow checks before size calculations, and using safer functions and casting methods to handle large tensor sizes. The fix was applied to key components responsible for tensor operations and memory management.

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Impact Analysis

This vulnerability can lead to insufficient memory allocation for large tensors, which may cause memory corruption or security breaches. Specifically, because the memory size calculation can overflow, the system might allocate less memory than needed, potentially resulting in crashes, data corruption, or exploitable conditions that could compromise the integrity or availability of the application using Samsung ONE.

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Detection Guidance

This vulnerability relates to an integer overflow in tensor allocation size calculation within the Samsung Open Source ONE framework. Detection would primarily involve inspecting the version of Samsung ONE in use to determine if it is prior to commit 1.30.0, which is affected.

Since the issue is internal to tensor memory allocation and not network-exposed, direct network detection commands are not applicable.

To detect if your system is vulnerable, you can check the installed Samsung ONE version or source code commit. For example, if you have access to the source repository, you can run:

• git log -1 --oneline
• git show 1.30.0

If the version or commit is older than 1.30.0, your system is vulnerable.

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Mitigation Strategies

The immediate mitigation step is to update Samsung Open Source ONE to version 1.30.0 or later, where the integer overflow vulnerability has been fixed.

The fix includes changing the data type for tensor element counts to 64-bit integers, adding explicit overflow checks, and using safer functions for large tensor size calculations.

If updating is not immediately possible, avoid processing very large tensors that could trigger the overflow condition.

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Compliance Impact

The provided information does not specify any direct impact of this vulnerability on compliance with common standards and regulations such as GDPR or HIPAA.

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