GHSA-H2QV-VJC9-5G55

Vulnerability from github – Published: 2026-09-22 15:32 – Updated: 2026-09-22 15:32
VLAI
Details

Mattermost versions 11.9.x <= 11.9.1, 11.8.x <= 11.8.5, 11.7.x <= 11.7.10, 11.10.x <= 11.10.1 fail to limit the length of the post ID array accepted by the bulk reactions endpoint which allows an authenticated user to cause excessive database load via a crafted request to {{POST /api/v4/posts/ids/reactions}}.. Mattermost Advisory ID: MMSA-2026-00771

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2026-95666"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-770"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-09-22T14:17:21Z",
    "severity": "MODERATE"
  },
  "details": "Mattermost versions 11.9.x \u003c= 11.9.1, 11.8.x \u003c= 11.8.5, 11.7.x \u003c= 11.7.10, 11.10.x \u003c= 11.10.1 fail to limit the length of the post ID array accepted by the bulk reactions endpoint which allows an authenticated user to cause excessive database load via a crafted request to {{POST /api/v4/posts/ids/reactions}}.. Mattermost Advisory ID: MMSA-2026-00771",
  "id": "GHSA-h2qv-vjc9-5g55",
  "modified": "2026-09-22T15:32:37Z",
  "published": "2026-09-22T15:32:37Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-95666"
    },
    {
      "type": "WEB",
      "url": "https://mattermost.com/security-updates"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L",
      "type": "CVSS_V3"
    }
  ]
}



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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

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Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
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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.


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