Vulnerability-Lookup

CVE-2026-80219 (GCVE-0-2026-80219)

Vulnerability from cvelistv5 – Published: 2026-09-08 11:27 – Updated: 2026-09-21 11:33

Red Hat Product Security has come to the conclusion that this CVE is not needed.

Show details on NVD website

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      "product_status:known_affected": "1",
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      "source": "Red Hat CSAF VEX",
      "status": "final",
      "title": "hawtio-operator: hawtio-operator: OAuthClient created with GrantMethod auto and no secret enables OAuth token theft",
      "url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-80219.json",
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FKIE_CVE-2026-80219

Vulnerability from fkie_nvd - Published: 2026-09-08 12:16 - Updated: 2026-09-21 12:17

Severity

Summary

Rejected reason: Red Hat Product Security has come to the conclusion that this CVE is not needed.

References

	URL	Tags

Impacted products

	Vendor	Product	Version

JSON

To clipboard

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  "lastModified": "2026-09-21T12:17:20.097",
  "metrics": {},
  "published": "2026-09-08T12:16:59.700",
  "references": [],
  "sourceIdentifier": "secalert@redhat.com",
  "vulnStatus": "Rejected"
}

GHSA-7XPG-HM9R-CHMF

Vulnerability from github – Published: 2026-09-08 12:31 – Updated: 2026-09-08 12:31

Details

A flaw was found in hawtio-operator. When deploying Hawtio in cluster mode, the operator creates a cluster-scoped OAuthClient with automatic grant approval (GrantMethod: auto) and no client secret (public client). The redirect URIs are derived from the operator-created Route, whose hostname is tenant-controlled via the Hawtio CR spec.routeHostName field. A malicious tenant can register an arbitrary hostname as a valid OAuth redirect target and, because grants are auto-approved, obtain OpenShift access tokens of any cluster user who visits the crafted authorization URL without any consent prompt.

Severity

8.7 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:N

Show details on source website

JSON

To clipboard

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  "aliases": [
    "CVE-2026-80219"
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    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-09-08T12:16:59Z",
    "severity": "HIGH"
  },
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  "id": "GHSA-7xpg-hm9r-chmf",
  "modified": "2026-09-08T12:31:30Z",
  "published": "2026-09-08T12:31:30Z",
  "references": [
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      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-80219"
    },
    {
      "type": "WEB",
      "url": "https://access.redhat.com/security/cve/CVE-2026-80219"
    },
    {
      "type": "WEB",
      "url": "https://bugzilla.redhat.com/show_bug.cgi?id=2524895"
    }
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      "type": "CVSS_V3"
    }
  ]
}

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

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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

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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.

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