GHSA-24PP-M59V-92J8

Vulnerability from github – Published: 2026-07-31 03:31 – Updated: 2026-07-31 03:31
VLAI
Details

An improper authorization and security-boundary bypass vulnerability in the bigquery-execute-sql tool component of Google mcp-toolbox versions 0.16.1 through 1.4.0 allows an authenticated attacker to bypass allowedDatasets validation checks. The toolbox relies on the BigQuery dry-run API to enforce dataset restrictions, but due to a fail-open logic flaw, it bypasses validation when the API returns an empty array for specialized constructs. This allows the attacker to extract structural DDL schemas for explicitly excluded datasets via INFORMATION_SCHEMA, and access downstream federated row data via EXTERNAL_QUERY connections.

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{
  "affected": [],
  "aliases": [
    "CVE-2026-14538"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-285"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2026-07-31T02:16:28Z",
    "severity": "MODERATE"
  },
  "details": "An improper authorization and security-boundary bypass vulnerability in the bigquery-execute-sql tool component of Google mcp-toolbox versions 0.16.1 through 1.4.0 allows an authenticated attacker to bypass allowedDatasets validation checks. The toolbox relies on the BigQuery dry-run API to enforce dataset restrictions, but due to a fail-open logic flaw, it bypasses validation when the API returns an empty array for specialized constructs. This allows the attacker to extract structural DDL schemas for explicitly excluded datasets via INFORMATION_SCHEMA, and access downstream federated row data via EXTERNAL_QUERY connections.",
  "id": "GHSA-24pp-m59v-92j8",
  "modified": "2026-07-31T03:31:15Z",
  "published": "2026-07-31T03:31:15Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-14538"
    },
    {
      "type": "WEB",
      "url": "https://github.com/googleapis/mcp-toolbox/pull/3452"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N/E:U/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
      "type": "CVSS_V4"
    }
  ]
}



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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.

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Nomenclature

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