FKIE_CVE-2026-34052

Vulnerability from fkie_nvd - Published: 2026-04-03 23:17 - Updated: 2026-07-24 22:10
Summary
LTI JupyterHub Authenticator is a JupyterHub authenticator for LTI. Prior to version 1.6.3, the LTI 1.1 validator stores OAuth nonces in a class-level dictionary that grows without bounds. Nonces are added before signature validation, so an attacker with knowledge of a valid consumer key can send repeated requests with unique nonces to gradually exhaust server memory, causing a denial of service. This issue has been patched in version 1.6.3.
Impacted products
Vendor Product Version
jupyter lti_jupyterhub_authenticator *

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "ltiauthenticator",
          "vendor": "jupyterhub",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 1.6.3"
            }
          ]
        }
      ],
      "source": "security-advisories@github.com"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:jupyter:lti_jupyterhub_authenticator:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "AD8F0C3C-6B43-4A7A-B3DE-8585DB1B5668",
              "versionEndExcluding": "1.6.3",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "LTI JupyterHub Authenticator is a JupyterHub authenticator for LTI. Prior to version 1.6.3, the LTI 1.1 validator stores OAuth nonces in a class-level dictionary that grows without bounds. Nonces are added before signature validation, so an attacker with knowledge of a valid consumer key can send repeated requests with unique nonces to gradually exhaust server memory, causing a denial of service. This issue has been patched in version 1.6.3."
    },
    {
      "lang": "es",
      "value": "Autenticador LTI JupyterHub es un autenticador de JupyterHub para LTI. Antes de la versi\u00f3n 1.6.3, el validador LTI 1.1 almacena nonces de OAuth en un diccionario a nivel de clase que crece sin l\u00edmites. Los nonces se a\u00f1aden antes de la validaci\u00f3n de la firma, por lo que un atacante con conocimiento de una clave de consumidor v\u00e1lida puede enviar solicitudes repetidas con nonces \u00fanicos para agotar gradualmente la memoria del servidor, causando una denegaci\u00f3n de servicio. Este problema ha sido parcheado en la versi\u00f3n 1.6.3."
    }
  ],
  "id": "CVE-2026-34052",
  "lastModified": "2026-07-24T22:10:00.140",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "HIGH",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 5.9,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 2.2,
        "impactScore": 3.6,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-34052",
          "options": [
            {
              "exploitation": "none"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "partial"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-04-06T18:58:43.478477Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-04-03T23:17:03.777",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Product"
      ],
      "url": "https://github.com/jupyterhub/ltiauthenticator/releases/tag/1.6.3"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Vendor Advisory"
      ],
      "url": "https://github.com/jupyterhub/ltiauthenticator/security/advisories/GHSA-8mxq-7xr7-2fxj"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Analyzed",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-401"
        },
        {
          "lang": "en",
          "value": "CWE-770"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    }
  ]
}



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.