CVE-2026-50144

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Out-of-Bounds Heap Write in ncnn Framework

Publication date: 2026-07-15

Last updated on: 2026-07-17

Assigner: GitHub, Inc.

Description

ncnn is a high-performance neural network inference framework optimized for the mobile platform. In commit e54f7b1f88434e1d844ea0551b880a1cfb079ce1 and earlier, ncnn allows an out-of-bounds heap write in ncnn::ParamDict::load_param() when Net::load_param() loads a malicious .param model file because the parsed parameter id is checked only against id >= NCNN_MAX_PARAM_COUNT, allowing a negative id to index before the params[NCNN_MAX_PARAM_COUNT] array. This vulnerability is fixed by commit 5a0288f255daa6c3294f77109f67718e434ec020.

CVSS Scores

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Meta Information

Published

2026-07-15

Last Modified

2026-07-17

Generated

2026-08-06

AI Q&A

2026-07-16

EPSS Evaluated

2026-08-04

NVD

CVE-2026-50144

Affected Vendors & Products

Vendor	Product	Version / Range
tencent	ncnn	to e54f7b1f88434e1d844ea0551b880a1cfb079ce1 (exc)

Helpful Resources

Exploitability

CWE

KEV

CWE ID	Description
CWE-787	The product writes data past the end, or before the beginning, of the intended buffer.
CWE-129	The product uses untrusted input when calculating or using an array index, but the product does not validate or incorrectly validates the index to ensure the index references a valid position within the array.
CWE-20	The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.

Attack-Flow Graph

Executive Summary

This vulnerability is an out-of-bounds heap write flaw in the ncnn library. It occurs in the ParamDict::load_param function when loading a malicious .param model file. The issue allows negative parameter IDs to bypass validation checks and be used as array indices, causing memory corruption by writing before the allocated memory block.

Detection Guidance

To detect this vulnerability, inspect applications using ncnn for processing .param model files. Check for crashes or memory corruption when loading untrusted files. Use static analysis tools to identify out-of-bounds writes in ParamDict::load_param(). Monitor system logs for heap corruption errors.

Impact Analysis

This vulnerability can lead to denial-of-service attacks or potential code execution. Attackers can exploit it by providing malicious model files, causing memory corruption. The impact depends on how the ncnn library is used in applications, but it poses a significant risk if untrusted model files are processed.

Compliance Impact

This vulnerability could impact compliance by enabling data breaches or system compromises. GDPR requires protecting personal data integrity, while HIPAA mandates safeguarding health information. Exploitation could lead to unauthorized access or data corruption, potentially violating these regulations' security requirements.

Mitigation Strategies

Update ncnn to a version containing commit 5a0288f255daa6c3294f77109f67718e434ec020 or later. If updating is not possible, apply the patch manually by adding a lower-bound check (id < 0) in ParamDict::load_param() for both text and binary loaders. Avoid processing untrusted .param files until patched.

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