GHSA-W5P8-4JCX-2J6R

Vulnerability from github – Published: 2026-05-07 03:13 – Updated: 2026-05-07 03:13
VLAI
Summary
imageproc: integer overflow in kernel size check leads to out-of-bounds read
Details

A bounds verification of a slice storage of a 2-dimensional matrix's coefficients (a kernel) would compare the total size against the product of individual dimensions. This would erroneously cast after the multiplication and consequently fail to detect possible violations when overflow occurs.

Afterwards, the individual sizes were trusted to properly constrain coordinates within the matrix to indices valid for the underlying storage. With a crafted Kernel object, certain combinations of coordinates could then cause an out-of-bounds access in an unsafe function while fulfilling its documented preconditions. The kernel value could be passed to library functions that trusted the preconditions and then performed such reads.

Severity
6.9 (Medium)
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N
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{
  "affected": [
    {
      "package": {
        "ecosystem": "crates.io",
        "name": "imageproc"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "0.23.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "crates.io",
        "name": "imageproc"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.24.0"
            },
            {
              "fixed": "0.24.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.24.0"
      ]
    },
    {
      "package": {
        "ecosystem": "crates.io",
        "name": "imageproc"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.25.0"
            },
            {
              "fixed": "0.25.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.25.0"
      ]
    },
    {
      "package": {
        "ecosystem": "crates.io",
        "name": "imageproc"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.26.0"
            },
            {
              "fixed": "0.26.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [],
  "database_specific": {
    "cwe_ids": [
      "CWE-125",
      "CWE-190"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-05-07T03:13:57Z",
    "nvd_published_at": null,
    "severity": "MODERATE"
  },
  "details": "A bounds verification of a slice storage of a 2-dimensional matrix\u0027s coefficients (a kernel) would compare the total size against the product of individual dimensions. This would erroneously cast *after* the multiplication and consequently fail to detect possible violations when overflow occurs.\n\nAfterwards, the individual sizes were trusted to properly constrain coordinates within the matrix to indices valid for the underlying storage. With a crafted `Kernel` object, certain combinations of coordinates could then cause an out-of-bounds access in an `unsafe` function while fulfilling its documented preconditions. The kernel value could be passed to library functions that trusted the preconditions and then performed such reads.",
  "id": "GHSA-w5p8-4jcx-2j6r",
  "modified": "2026-05-07T03:13:57Z",
  "published": "2026-05-07T03:13:57Z",
  "references": [
    {
      "type": "PACKAGE",
      "url": "https://github.com/image-rs/imageproc"
    },
    {
      "type": "WEB",
      "url": "https://rustsec.org/advisories/RUSTSEC-2026-0116.html"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "imageproc: integer overflow in kernel size check leads to out-of-bounds read"
}



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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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