CVE-2025-61620: vLLM Remote Code Execution Vulnerability

{
  "schema_version" : "1.4.0",
  "id" : "GHSA-6fvq-23cw-5628",
  "modified" : "2025-10-07T21:35:23Z",
  "published" : "2025-10-07T21:35:22Z",
  "aliases" : [ "CVE-2025-61620" ],
  "summary" : "vLLM: Resource-Exhaustion (DoS) through Malicious Jinja Template in OpenAI-Compatible Server",
  "details" : "### Summary\n\nA resource-exhaustion (denial-of-service) vulnerability exists in multiple endpoints of the OpenAI-Compatible Server due to the ability to specify Jinja templates via the `chat_template` and `chat_template_kwargs` parameters. If an attacker can supply these parameters to the API, they can cause a service outage by exhausting CPU and/or memory resources.\n\n### Details\n\nWhen using an LLM as a chat model, the conversation history must be rendered into a text input for the model. In `hf/transformer`, this rendering is performed using a Jinja template. The OpenAI-Compatible Server launched by vllm serve exposes a `chat_template` parameter that lets users specify that template. In addition, the server accepts a `chat_template_kwargs` parameter to pass extra keyword arguments to the rendering function.\n\nBecause Jinja templates support programming-language-like constructs (loops, nested iterations, etc.), a crafted template can consume extremely large amounts of CPU and memory and thereby trigger a denial-of-service condition.\n\nImportantly, simply forbidding the `chat_template` parameter does not fully mitigate the issue. The implementation constructs a dictionary of keyword arguments for `apply_hf_chat_template` and then updates that dictionary with the user-supplied `chat_template_kwargs` via `dict.update`. Since `dict.update` can overwrite existing keys, an attacker can place a `chat_template` key inside `chat_template_kwargs` to replace the template that will be used by `apply_hf_chat_template`.\n\n\n```python\n# vllm/entrypoints/openai/serving_engine.py#L794-L816\n_chat_template_kwargs: dict[str, Any] = dict(\n    chat_template=chat_template,\n    add_generation_prompt=add_generation_prompt,\n    continue_final_message=continue_final_message,\n    tools=tool_dicts,\n    documents=documents,\n)\n_chat_template_kwargs.update(chat_template_kwargs or {})\n\nrequest_prompt: Union[str, list[int]]\nif isinstance(tokenizer, MistralTokenizer):\n    ...\nelse:\n    request_prompt = apply_hf_chat_template(\n        tokenizer=tokenizer,\n        conversation=conversation,\n        model_config=model_config,\n        **_chat_template_kwargs,\n    )\n```\n\n### Impact\n\nIf an OpenAI-Compatible Server exposes endpoints that accept `chat_template` or `chat_template_kwargs` from untrusted clients, an attacker can submit a malicious Jinja template (directly or by overriding `chat_template` inside `chat_template_kwargs`) that consumes excessive CPU and/or memory. This can result in a resource-exhaustion denial-of-service that renders the server unresponsive to legitimate requests.\n\n### Fixes\n\n* https://github.com/vllm-project/vllm/pull/25794",
  "severity" : [ {
    "type" : "CVSS_V3",
    "score" : "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H"
  } ],
  "affected" : [ {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "vllm"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "0.5.1"
      }, {
        "fixed" : "0.11.0"
      } ]
    } ]
  } ],
  "references" : [ {
    "type" : "WEB",
    "url" : "https://github.com/vllm-project/vllm/security/advisories/GHSA-6fvq-23cw-5628"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/vllm-project/vllm/pull/25794"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/vllm-project/vllm/commit/7977e5027c2250a4abc1f474c5619c40b4e5682f"
  }, {
    "type" : "PACKAGE",
    "url" : "https://github.com/vllm-project/vllm"
  } ],
  "database_specific" : {
    "cwe_ids" : [ "CWE-20", "CWE-400", "CWE-770" ],
    "severity" : "MODERATE",
    "github_reviewed" : true,
    "github_reviewed_at" : "2025-10-07T21:35:22Z",
    "nvd_published_at" : null
  }
}