Vulnerability-Lookup

GHSA-Q2WP-4P9R-FG6G

Vulnerability from github – Published: 2022-05-24 19:19 – Updated: 2022-05-24 19:19

Details

Invalid JPEG XL images using libjxl can cause an out of bounds access on a std::vector> when rendering splines. The OOB read access can either lead to a segfault, or rendering splines based on other process memory. It is recommended to upgrade past 0.6.0 or patch with https://github.com/libjxl/libjxl/pull/757

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{
  "affected": [],
  "aliases": [
    "CVE-2021-22563"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2021-11-01T13:15:00Z",
    "severity": "MODERATE"
  },
  "details": "Invalid JPEG XL images using libjxl can cause an out of bounds access on a std::vector\u003cstd::vector\u003cT\u003e\u003e when rendering splines. The OOB read access can either lead to a segfault, or rendering splines based on other process memory. It is recommended to upgrade past 0.6.0 or patch with https://github.com/libjxl/libjxl/pull/757",
  "id": "GHSA-q2wp-4p9r-fg6g",
  "modified": "2022-05-24T19:19:16Z",
  "published": "2022-05-24T19:19:16Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-22563"
    },
    {
      "type": "WEB",
      "url": "https://github.com/libjxl/libjxl/issues/735"
    },
    {
      "type": "WEB",
      "url": "https://github.com/libjxl/libjxl/pull/757"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}

CVE-2021-22563 (GCVE-0-2021-22563)

Vulnerability from cvelistv5 – Published: 2021-11-01 13:10 – Updated: 2024-09-17 02:10

Title

Memory Overread in libjxl

Summary

Invalid JPEG XL images using libjxl can cause an out of bounds access on a std::vector<std::vector<T>> when rendering splines. The OOB read access can either lead to a segfault, or rendering splines based on other process memory. It is recommended to upgrade past 0.6.0 or patch with https://github.com/libjxl/libjxl/pull/757

Severity

4.5 (Medium)


                        
                          CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:L/I:L/A:L

CWE

CWE-126 - Buffer Over-read

Assigner

Google Google root

References

2 references

URL	Tags
https://github.com/libjxl/libjxl/issues/735	x_refsource_MISC
https://github.com/libjxl/libjxl/pull/757	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
Google LLC	libjxl	Affected: unspecified , ≤ 0.6.0 (custom)

Date Public

2021-10-14 00:00

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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.

Detection rules are retrieved from Rulezet.

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.