CVE-2026-119204 (GCVE-0-2026-119204)

Vulnerability from cvelistv5 – Published: 2026-10-29 20:34 – Updated: 2026-11-04 01:42
VLAI
Title
pypdf: Possible large memory usage when parsing page tree
Summary
pypdf is a free and open-source pure-python PDF library. Prior to 6.21.4, pypdf can consume excessive memory while parsing a crafted PDF page tree containing many shared resources. Page-tree flattening copies page dictionary and inherited resource entries without a document-wide entry budget, allowing the crafted resource structure to cause disproportionate allocations. This issue is fixed in version 6.21.4.
SSVC
Exploitation: none Automatable: no Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-11-04 01:42 UTC
CWE
  • CWE-400 - Uncontrolled Resource Consumption
Assigner
GitHub_M CNA
Impacted products
Vendor Product Version
py-pdf pypdf Affected: < 6.21.4
Create a notification for this product.
Show details on NVD website

{
  "containers": {
    "adp": [
      {
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2026-119204",
                "options": [
                  {
                    "Exploitation": "none"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2026-11-04T01:42:11.010579Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2026-11-04T01:42:21.589Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "pypdf",
          "vendor": "py-pdf",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 6.21.4"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "pypdf is a free and open-source pure-python PDF library. Prior to 6.21.4, pypdf can consume excessive memory while parsing a crafted PDF page tree containing many shared resources. Page-tree flattening copies page dictionary and inherited resource entries without a document-wide entry budget, allowing the crafted resource structure to cause disproportionate allocations. This issue is fixed in version 6.21.4."
        }
      ],
      "metrics": [
        {
          "cvssV4_0": {
            "attackComplexity": "LOW",
            "attackRequirements": "NONE",
            "attackVector": "NETWORK",
            "baseScore": 7.1,
            "baseSeverity": "HIGH",
            "privilegesRequired": "NONE",
            "subAvailabilityImpact": "NONE",
            "subConfidentialityImpact": "NONE",
            "subIntegrityImpact": "NONE",
            "userInteraction": "PASSIVE",
            "vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
            "version": "4.0",
            "vulnAvailabilityImpact": "HIGH",
            "vulnConfidentialityImpact": "NONE",
            "vulnIntegrityImpact": "NONE"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-400",
              "description": "CWE-400: Uncontrolled Resource Consumption",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2026-10-29T20:34:02.704Z",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "shortName": "GitHub_M"
      },
      "references": [
        {
          "name": "https://github.com/py-pdf/pypdf/security/advisories/GHSA-cggf-277g-3q54",
          "tags": [
            "x_refsource_CONFIRM"
          ],
          "url": "https://github.com/py-pdf/pypdf/security/advisories/GHSA-cggf-277g-3q54"
        },
        {
          "name": "https://github.com/py-pdf/pypdf/pull/4290",
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/py-pdf/pypdf/pull/4290"
        },
        {
          "name": "https://github.com/py-pdf/pypdf/commit/ea4ac663133f1b3de1d7a8f2e6fa239802fde79e",
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/py-pdf/pypdf/commit/ea4ac663133f1b3de1d7a8f2e6fa239802fde79e"
        },
        {
          "name": "https://github.com/py-pdf/pypdf/releases/tag/6.21.4",
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/py-pdf/pypdf/releases/tag/6.21.4"
        }
      ],
      "source": {
        "advisory": "GHSA-cggf-277g-3q54",
        "discovery": "UNKNOWN"
      },
      "title": "pypdf: Possible large memory usage when parsing page tree"
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2026-119204",
    "datePublished": "2026-10-29T20:34:02.704Z",
    "dateReserved": "2026-10-29T16:04:48.729Z",
    "dateUpdated": "2026-11-04T01:42:21.589Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.2",
  "vulnerability-lookup:meta": {
    "vulnrichment": {
      "containers": {
        "adp": [
          {
            "metrics": [
              {
                "other": {
                  "content": {
                    "id": "CVE-2026-119204",
                    "options": [
                      {
                        "Exploitation": "none"
                      },
                      {
                        "Automatable": "no"
                      },
                      {
                        "Technical Impact": "partial"
                      }
                    ],
                    "role": "CISA Coordinator",
                    "timestamp": "2026-11-04T01:42:11.010579Z",
                    "version": "2.0.3"
                  },
                  "type": "ssvc"
                }
              }
            ],
            "providerMetadata": {
              "dateUpdated": "2026-11-04T01:42:16.576Z",
              "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
              "shortName": "CISA-ADP"
            },
            "title": "CISA ADP Vulnrichment"
          }
        ],
        "cna": {
          "affected": [
            {
              "product": "pypdf",
              "vendor": "py-pdf",
              "versions": [
                {
                  "status": "affected",
                  "version": "\u003c 6.21.4"
                }
              ]
            }
          ],
          "descriptions": [
            {
              "lang": "en",
              "value": "pypdf is a free and open-source pure-python PDF library. Prior to 6.21.4, pypdf can consume excessive memory while parsing a crafted PDF page tree containing many shared resources. Page-tree flattening copies page dictionary and inherited resource entries without a document-wide entry budget, allowing the crafted resource structure to cause disproportionate allocations. This issue is fixed in version 6.21.4."
            }
          ],
          "metrics": [
            {
              "cvssV4_0": {
                "attackComplexity": "LOW",
                "attackRequirements": "NONE",
                "attackVector": "NETWORK",
                "baseScore": 7.1,
                "baseSeverity": "HIGH",
                "privilegesRequired": "NONE",
                "subAvailabilityImpact": "NONE",
                "subConfidentialityImpact": "NONE",
                "subIntegrityImpact": "NONE",
                "userInteraction": "PASSIVE",
                "vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
                "version": "4.0",
                "vulnAvailabilityImpact": "HIGH",
                "vulnConfidentialityImpact": "NONE",
                "vulnIntegrityImpact": "NONE"
              }
            }
          ],
          "problemTypes": [
            {
              "descriptions": [
                {
                  "cweId": "CWE-400",
                  "description": "CWE-400: Uncontrolled Resource Consumption",
                  "lang": "en",
                  "type": "CWE"
                }
              ]
            }
          ],
          "providerMetadata": {
            "dateUpdated": "2026-10-29T20:34:02.704Z",
            "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
            "shortName": "GitHub_M"
          },
          "references": [
            {
              "name": "https://github.com/py-pdf/pypdf/security/advisories/GHSA-cggf-277g-3q54",
              "tags": [
                "x_refsource_CONFIRM"
              ],
              "url": "https://github.com/py-pdf/pypdf/security/advisories/GHSA-cggf-277g-3q54"
            },
            {
              "name": "https://github.com/py-pdf/pypdf/pull/4290",
              "tags": [
                "x_refsource_MISC"
              ],
              "url": "https://github.com/py-pdf/pypdf/pull/4290"
            },
            {
              "name": "https://github.com/py-pdf/pypdf/commit/ea4ac663133f1b3de1d7a8f2e6fa239802fde79e",
              "tags": [
                "x_refsource_MISC"
              ],
              "url": "https://github.com/py-pdf/pypdf/commit/ea4ac663133f1b3de1d7a8f2e6fa239802fde79e"
            },
            {
              "name": "https://github.com/py-pdf/pypdf/releases/tag/6.21.4",
              "tags": [
                "x_refsource_MISC"
              ],
              "url": "https://github.com/py-pdf/pypdf/releases/tag/6.21.4"
            }
          ],
          "source": {
            "advisory": "GHSA-cggf-277g-3q54",
            "discovery": "UNKNOWN"
          },
          "title": "pypdf: Possible large memory usage when parsing page tree"
        }
      },
      "cveMetadata": {
        "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "assignerShortName": "GitHub_M",
        "cveId": "CVE-2026-119204",
        "datePublished": "2026-10-29T20:34:02.704Z",
        "dateReserved": "2026-10-29T16:04:48.729Z",
        "dateUpdated": "2026-11-04T01:42:21.589Z",
        "state": "PUBLISHED"
      },
      "dataType": "CVE_RECORD",
      "dataVersion": "5.2"
    }
  }
}



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.