BREW-PDFALYZER-CVE-2026-102999 (GHSA-V247-6F48-MGCJ)

Vulnerability from osv_homebrew – Published: 2026-10-02 11:00 – Updated: 2026-10-02 11:00 – Source website
VLAI
Summary
pypdf: Possible long runtimes with large amount of embedded files
Details

Impact

An attacker who uses this vulnerability can craft a PDF which leads to long runtimes. This requires accessing the embedded files through the dictionary-based API.

Patches

This has been fixed in pypdf==6.19.0.

Workarounds

If you cannot upgrade yet, consider applying the changes from PR #4081.

Severity
8.7 (High)
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N
References
To clipboard 

{
  "affected": [
    {
      "ecosystem_specific": {
        "fix": null,
        "range_state": "affected",
        "resource": "pypdf",
        "resource_purl": "pkg:pypi/pypdf@6.6.0",
        "upstream_fixed_in": "6.19.0"
      },
      "package": {
        "ecosystem": "Homebrew",
        "name": "pdfalyzer",
        "purl": "pkg:brew/pdfalyzer"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "1.16.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "database_specific": {
    "confidence": "high",
    "source": "matched",
    "strategy": "registry",
    "upstream_evidence": [
      {
        "ecosystem": "PyPI",
        "key": "pkg:pypi/pypdf@6.6.0",
        "name": "pypdf",
        "resource": "pypdf",
        "strategy": "registry",
        "subject_version": "6.6.0"
      }
    ]
  },
  "details": "### Impact\n\nAn attacker who uses this vulnerability can craft a PDF which leads to long runtimes. This requires accessing the embedded files through the dictionary-based API.\n\n### Patches\n\nThis has been fixed in [pypdf==6.19.0](https://github.com/py-pdf/pypdf/releases/tag/6.19.0).\n\n### Workarounds\n\nIf you cannot upgrade yet, consider applying the changes from PR [#4081](https://github.com/py-pdf/pypdf/pull/4081).",
  "id": "BREW-pdfalyzer-CVE-2026-102999",
  "modified": "2026-10-02T11:00:21Z",
  "published": "2026-10-02T11:00:21Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/py-pdf/pypdf/security/advisories/GHSA-v247-6f48-mgcj"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-102999"
    },
    {
      "type": "WEB",
      "url": "https://github.com/py-pdf/pypdf/pull/4081"
    },
    {
      "type": "WEB",
      "url": "https://github.com/py-pdf/pypdf/commit/6b10556d13609a68f9ed18bb29fdd8bba88eb2c3"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/py-pdf/pypdf"
    },
    {
      "type": "WEB",
      "url": "https://github.com/py-pdf/pypdf/releases/tag/6.19.0"
    }
  ],
  "schema_version": "1.7.3",
  "severity": [
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "pypdf: Possible long runtimes with large amount of embedded files",
  "upstream": [
    "GHSA-v247-6f48-mgcj",
    "CVE-2026-102999",
    "PYSEC-2026-4159"
  ]
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.





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.



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.