FKIE_CVE-2026-54499

Vulnerability from fkie_nvd - Published: 2026-07-08 23:16 - Updated: 2026-07-13 14:57
Severity
7.5 (High) - CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H
Summary
Stanza is a Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages. Prior to 1.12.2, Stanza model loaders such as stanza.models.common.pretrain.Pretrain.load() attempt torch.load(..., weights_only=True) but fall back to torch.load(..., weights_only=False) on attacker-controllable pickle.UnpicklingError, allowing a malicious .pt pretrain or model file to execute arbitrary pickle code when a Stanza NLP pipeline loads it. This issue is fixed in version 1.12.2.
References
security-advisories@github.comhttps://github.com/stanfordnlp/stanza/commit/b745008c68c9e50ccb5acd537cb6f2453f8b7ad4Patch
security-advisories@github.comhttps://github.com/stanfordnlp/stanza/pull/1587Issue Tracking, Patch
security-advisories@github.comhttps://github.com/stanfordnlp/stanza/releases/tag/v1.12.2Release Notes
security-advisories@github.comhttps://github.com/stanfordnlp/stanza/security/advisories/GHSA-v5jw-96jm-7h2cExploit, Vendor Advisory
134c704f-9b21-4f2e-91b3-4a467353bcc0https://github.com/stanfordnlp/stanza/security/advisories/GHSA-v5jw-96jm-7h2cExploit, Vendor Advisory
Impacted products
Vendor Product Version
stanford stanza *
To clipboard 

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "stanza",
          "vendor": "stanfordnlp",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 1.12.2"
            }
          ]
        }
      ],
      "source": "security-advisories@github.com"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:stanford:stanza:*:*:*:*:*:python:*:*",
              "matchCriteriaId": "E88A6152-9471-45B4-9E83-DC16C7FFBB6A",
              "versionEndExcluding": "1.12.2",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "Stanza is a Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages. Prior to 1.12.2, Stanza model loaders such as stanza.models.common.pretrain.Pretrain.load() attempt torch.load(..., weights_only=True) but fall back to torch.load(..., weights_only=False) on attacker-controllable pickle.UnpicklingError, allowing a malicious .pt pretrain or model file to execute arbitrary pickle code when a Stanza NLP pipeline loads it. This issue is fixed in version 1.12.2."
    }
  ],
  "id": "CVE-2026-54499",
  "lastModified": "2026-07-13T14:57:10.527",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "HIGH",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 7.5,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "REQUIRED",
          "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 1.6,
        "impactScore": 5.9,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-54499",
          "options": [
            {
              "exploitation": "poc"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-07-09T13:13:21.918884Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-07-08T23:16:54.690",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch"
      ],
      "url": "https://github.com/stanfordnlp/stanza/commit/b745008c68c9e50ccb5acd537cb6f2453f8b7ad4"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Issue Tracking",
        "Patch"
      ],
      "url": "https://github.com/stanfordnlp/stanza/pull/1587"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Release Notes"
      ],
      "url": "https://github.com/stanfordnlp/stanza/releases/tag/v1.12.2"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Exploit",
        "Vendor Advisory"
      ],
      "url": "https://github.com/stanfordnlp/stanza/security/advisories/GHSA-v5jw-96jm-7h2c"
    },
    {
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "tags": [
        "Exploit",
        "Vendor Advisory"
      ],
      "url": "https://github.com/stanfordnlp/stanza/security/advisories/GHSA-v5jw-96jm-7h2c"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Analyzed",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-502"
        },
        {
          "lang": "en",
          "value": "CWE-676"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    }
  ]
}



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.