GHSA-HF3C-WXG2-49Q9

Vulnerability from github – Published: 2025-04-15 21:21 – Updated: 2025-04-15 21:21
VLAI
Summary
vLLM vulnerable to Denial of Service by abusing xgrammar cache
Details

Impact

This report is to highlight a vulnerability in XGrammar, a library used by the structured output feature in vLLM. The XGrammar advisory is here: https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-389x-67px-mjg3

The xgrammar library is the default backend used by vLLM to support structured output (a.k.a. guided decoding). Xgrammar provides a required, built-in cache for its compiled grammars stored in RAM. xgrammar is available by default through the OpenAI compatible API server with both the V0 and V1 engines.

A malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service by consuming all of the system's RAM.

Note that even if vLLM was configured to use a different backend by default, it is still possible to choose xgrammar on a per-request basis using the guided_decoding_backend key of the extra_body field of the request with the V0 engine. This per-request choice is not available when using the V1 engine.

Patches

  • https://github.com/vllm-project/vllm/pull/16283

Workarounds

There is no way to workaround this issue in existing versions of vLLM other than preventing untrusted access to the OpenAI compatible API server.

References

  • https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-389x-67px-mjg3
Show details on source website

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "vllm"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0.6.5"
            },
            {
              "fixed": "0.8.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [],
  "database_specific": {
    "cwe_ids": [
      "CWE-1395",
      "CWE-770"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2025-04-15T21:21:04Z",
    "nvd_published_at": null,
    "severity": "MODERATE"
  },
  "details": "### Impact\n\nThis report is to highlight a vulnerability in XGrammar, a library used by the structured output feature in vLLM. The XGrammar advisory is here: https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-389x-67px-mjg3\n\nThe [xgrammar](https://xgrammar.mlc.ai/docs/) library is the default backend used by vLLM to support structured output (a.k.a. guided decoding). Xgrammar provides a required, built-in cache for its compiled grammars stored in RAM. xgrammar is available by default through the OpenAI compatible API server with both the V0 and V1 engines.\n\nA malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service by consuming all of the system\u0027s RAM.\n\nNote that even if vLLM was configured to use a different backend by default, it is still possible to choose xgrammar on a per-request basis using the `guided_decoding_backend` key of the `extra_body` field of the request with the V0 engine. This per-request choice is not available when using the V1 engine. \n### Patches\n\n* https://github.com/vllm-project/vllm/pull/16283\n\n### Workarounds\n\nThere is no way to workaround this issue in existing versions of vLLM other than preventing untrusted access to the OpenAI compatible API server.\n\n### References\n\n* https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-389x-67px-mjg3",
  "id": "GHSA-hf3c-wxg2-49q9",
  "modified": "2025-04-15T21:21:04Z",
  "published": "2025-04-15T21:21:04Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-389x-67px-mjg3"
    },
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-hf3c-wxg2-49q9"
    },
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/pull/16283"
    },
    {
      "type": "WEB",
      "url": "https://github.com/vllm-project/vllm/commit/cb84e45ac75b42ba6795145923e8eb323bb825ad"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/vllm-project/vllm"
    }
  ],
  "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:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "vLLM vulnerable to Denial of Service by abusing xgrammar cache"
}



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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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