CVE-2026-50144 (GCVE-0-2026-50144)

Vulnerability from cvelistv5 – Published: 2026-07-15 20:04 – Updated: 2026-07-15 20:04
VLAI
Title
ncnn: Out-of-bounds heap write in ParamDict::load_param via unchecked negative parameter id
Summary
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.
Severity
7.1 (High)
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:H/A:H
CWE
  • CWE-20 - Improper Input Validation
  • CWE-129 - Improper Validation of Array Index
  • CWE-787 - Out-of-bounds Write
Assigner
References
Impacted products
Vendor Product Version
Tencent ncnn Affected: < 5a0288f255daa6c3294f77109f67718e434ec020
Affected: <= 20260526
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        }
      ],
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            "confidentialityImpact": "NONE",
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            }
          ]
        },
        {
          "descriptions": [
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              "description": "CWE-129: Improper Validation of Array Index",
              "lang": "en",
              "type": "CWE"
            }
          ]
        },
        {
          "descriptions": [
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          "url": "https://github.com/Tencent/ncnn/commit/5a0288f255daa6c3294f77109f67718e434ec020"
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      "title": "ncnn: Out-of-bounds heap write in ParamDict::load_param via unchecked negative parameter id"
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    "redhat_vex": {
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      "product_status:known_not_affected": "1",
      "source": "Red Hat CSAF VEX",
      "status": "final",
      "title": "ncnn: ncnn: Out-of-bounds write allows arbitrary code execution",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-50144.json",
      "version": "3"
    }
  }
}



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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

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  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
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