Vulnerability-Lookup

GHSA-79F7-6JWC-94QW

Vulnerability from github – Published: 2022-05-24 17:43 – Updated: 2022-05-24 17:43

Details

jpeg-xl v0.3.2 is affected by a heap buffer overflow in /lib/jxl/coeff_order.cc ReadPermutation. When decoding a malicous jxl file using djxl, an attacker can trigger arbitrary code execution or a denial of service.

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{
  "affected": [],
  "aliases": [
    "CVE-2021-28026"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-787"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2021-03-05T14:15:00Z",
    "severity": "HIGH"
  },
  "details": "jpeg-xl v0.3.2 is affected by a heap buffer overflow in /lib/jxl/coeff_order.cc ReadPermutation. When decoding a malicous jxl file using djxl, an attacker can trigger arbitrary code execution or a denial of service.",
  "id": "GHSA-79f7-6jwc-94qw",
  "modified": "2022-05-24T17:43:47Z",
  "published": "2022-05-24T17:43:47Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-28026"
    },
    {
      "type": "WEB",
      "url": "https://gitlab.com/wg1/jpeg-xl/-/issues/163"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}

CVE-2021-28026 (GCVE-0-2021-28026)

Vulnerability from cvelistv5 – Published: 2021-03-05 13:17 – Updated: 2024-08-03 21:33

Summary

Severity

No CVSS data available.

CWE

Assigner

mitre CNA under the mitre root

CNA scorecard E 29/100 over 2315 records in the last 180 days details

References

1 reference

URL	Tags
https://gitlab.com/wg1/jpeg-xl/-/issues/163	x_refsource_MISC

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            ],
            "url": "https://gitlab.com/wg1/jpeg-xl/-/issues/163"
          }
        ],
        "title": "CVE Program Container"
      }
    ],
    "cna": {
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          "product": "n/a",
          "vendor": "n/a",
          "versions": [
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        }
      ],
      "problemTypes": [
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          "descriptions": [
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      ],
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        "orgId": "8254265b-2729-46b6-b9e3-3dfca2d5bfca",
        "shortName": "mitre"
      },
      "references": [
        {
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://gitlab.com/wg1/jpeg-xl/-/issues/163"
        }
      ],
      "x_legacyV4Record": {
        "CVE_data_meta": {
          "ASSIGNER": "cve@mitre.org",
          "ID": "CVE-2021-28026",
          "STATE": "PUBLIC"
        },
        "affects": {
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                            "version_value": "n/a"
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  "cveMetadata": {
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    "dateReserved": "2021-03-05T00:00:00.000Z",
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    "state": "PUBLISHED"
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  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
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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

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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

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.

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