GHSA-FP3F-MC75-235C

Vulnerability from github – Published: 2026-08-07 19:29 – Updated: 2026-08-07 19:29
VLAI
Summary
pypdf: Possible large memory usage for large /ToUnicode streams
Details

Impact

An attacker who uses this vulnerability can craft a PDF which leads to large memory consumption. This requires parsing the /ToUnicode entry of a font with unusually large values, for example during text extraction.

Patches

This has been fixed in pypdf==6.15.0.

Workarounds

If you cannot upgrade yet, consider applying the changes from PR #3944.

Severity
4.8 (Medium)
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N
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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "pypdf"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "6.15.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-71870"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-400"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-08-07T19:29:09Z",
    "nvd_published_at": null,
    "severity": "MODERATE"
  },
  "details": "### Impact\n\nAn attacker who uses this vulnerability can craft a PDF which leads to large memory consumption. This requires parsing the `/ToUnicode` entry of a font with unusually large values, for example during text extraction.\n\n### Patches\n\nThis has been fixed in [pypdf==6.15.0](https://github.com/py-pdf/pypdf/releases/tag/6.15.0).\n\n### Workarounds\n\nIf you cannot upgrade yet, consider applying the changes from PR [#3944](https://github.com/py-pdf/pypdf/pull/3944).",
  "id": "GHSA-fp3f-mc75-235c",
  "modified": "2026-08-07T19:29:09Z",
  "published": "2026-08-07T19:29:09Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/py-pdf/pypdf/security/advisories/GHSA-fp3f-mc75-235c"
    },
    {
      "type": "WEB",
      "url": "https://github.com/py-pdf/pypdf/pull/3944"
    },
    {
      "type": "WEB",
      "url": "https://github.com/py-pdf/pypdf/commit/afba8080e19d29a3c256a742b340995e695b35aa"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/py-pdf/pypdf"
    },
    {
      "type": "WEB",
      "url": "https://github.com/py-pdf/pypdf/releases/tag/6.15.0"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "pypdf: Possible large memory usage for large /ToUnicode streams"
}



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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.

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.


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