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GHSA-HHR6-7642-8PMH

Vulnerability from github – Published: 2025-03-20 12:32 – Updated: 2025-03-20 12:32
VLAI
Details

vllm-project vllm version 0.5.2.2 is vulnerable to Denial of Service attacks. The issue occurs in the 'POST /v1/completions' and 'POST /v1/embeddings' endpoints. For 'POST /v1/completions', enabling 'use_beam_search' and setting 'best_of' to a high value causes the HTTP connection to time out, with vllm ceasing effective work and the request remaining in a 'pending' state, blocking new completion requests. For 'POST /v1/embeddings', supplying invalid inputs to the JSON object causes an issue in the background loop, resulting in all further completion requests returning a 500 HTTP error code ('Internal Server Error') until vllm is restarted.

Show details on source website

{
  "affected": [],
  "aliases": [
    "CVE-2024-11040"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-400"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2025-03-20T10:15:23Z",
    "severity": "HIGH"
  },
  "details": "vllm-project vllm version 0.5.2.2 is vulnerable to Denial of Service attacks. The issue occurs in the \u0027POST /v1/completions\u0027 and \u0027POST /v1/embeddings\u0027 endpoints. For \u0027POST /v1/completions\u0027, enabling \u0027use_beam_search\u0027 and setting \u0027best_of\u0027 to a high value causes the HTTP connection to time out, with vllm ceasing effective work and the request remaining in a \u0027pending\u0027 state, blocking new completion requests. For \u0027POST /v1/embeddings\u0027, supplying invalid inputs to the JSON object causes an issue in the background loop, resulting in all further completion requests returning a 500 HTTP error code (\u0027Internal Server Error\u0027) until vllm is restarted.",
  "id": "GHSA-hhr6-7642-8pmh",
  "modified": "2025-03-20T12:32:41Z",
  "published": "2025-03-20T12:32:41Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-11040"
    },
    {
      "type": "WEB",
      "url": "https://huntr.com/bounties/8ce20bbe-3c96-4cd1-97e5-25a5630925be"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
      "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.

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