FKIE_CVE-2020-19497

Vulnerability from fkie_nvd - Published: 2021-07-21 18:15 - Updated: 2026-06-17 02:59
Summary
Integer overflow vulnerability in Mat_VarReadNextInfo5 in mat5.c in tbeu matio (aka MAT File I/O Library) 1.5.17, allows attackers to cause a Denial of Service or possibly other unspecified impacts.
Impacted products
Vendor Product Version
matio_project matio 1.5.17

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "n/a",
          "vendor": "n/a",
          "versions": [
            {
              "status": "affected",
              "version": "n/a"
            }
          ]
        }
      ],
      "source": "cve@mitre.org"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:matio_project:matio:1.5.17:*:*:*:*:*:*:*",
              "matchCriteriaId": "BA202CA9-93FA-4D2E-8968-0A2748AC055D",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "Integer overflow vulnerability in Mat_VarReadNextInfo5 in mat5.c in tbeu matio (aka MAT File I/O Library) 1.5.17, allows attackers to cause a Denial of Service or possibly other unspecified impacts."
    },
    {
      "lang": "es",
      "value": "Una vulnerabilidad de desbordamiento de enteros en la funci\u00f3n Mat_VarReadNextInfo5 en el archivo mat5.c en tbeu matio (tambi\u00e9n se conoce como MAT File I/O Library) versi\u00f3n 1.5.17, permite a atacantes causar una Denegaci\u00f3n de Servicio o posiblemente otros impactos no especificados"
    }
  ],
  "id": "CVE-2020-19497",
  "lastModified": "2026-06-17T02:59:48.877",
  "metrics": {
    "cvssMetricV2": [
      {
        "acInsufInfo": false,
        "baseSeverity": "MEDIUM",
        "cvssData": {
          "accessComplexity": "MEDIUM",
          "accessVector": "NETWORK",
          "authentication": "NONE",
          "availabilityImpact": "PARTIAL",
          "baseScore": 6.8,
          "confidentialityImpact": "PARTIAL",
          "integrityImpact": "PARTIAL",
          "vectorString": "AV:N/AC:M/Au:N/C:P/I:P/A:P",
          "version": "2.0"
        },
        "exploitabilityScore": 8.6,
        "impactScore": 6.4,
        "obtainAllPrivilege": false,
        "obtainOtherPrivilege": false,
        "obtainUserPrivilege": false,
        "source": "nvd@nist.gov",
        "type": "Primary",
        "userInteractionRequired": true
      }
    ],
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 8.8,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "REQUIRED",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 2.8,
        "impactScore": 5.9,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ]
  },
  "published": "2021-07-21T18:15:09.097",
  "references": [
    {
      "source": "cve@mitre.org",
      "tags": [
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tbeu/matio/commit/5fa49ef9fc4368fe3d19b5fdaa36d8fa5e7f4606"
    },
    {
      "source": "cve@mitre.org",
      "tags": [
        "Exploit",
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tbeu/matio/issues/121"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tbeu/matio/commit/5fa49ef9fc4368fe3d19b5fdaa36d8fa5e7f4606"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Exploit",
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tbeu/matio/issues/121"
    }
  ],
  "sourceIdentifier": "cve@mitre.org",
  "vulnStatus": "Modified",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-190"
        }
      ],
      "source": "nvd@nist.gov",
      "type": "Primary"
    }
  ]
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.

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.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

JSON
Events included in the timeline

Loading timeline…


    Loading…

    Loading…

    Loading…

    Related by attack behaviour

    Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.