{"id":"CVE-2025-61620","aliases":["GHSA-6fvq-23cw-5628","PYSEC-2026-2013"],"title":"vLLM: Resource-Exhaustion (DoS) through Malicious Jinja Template in OpenAI-Compatible Server","summary":"vLLM: Resource-Exhaustion (DoS) through Malicious Jinja Template in OpenAI-Compatible Server","severity":"medium","cvss":6.5,"cvssVector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","vendor":"vllm","product":"vllm","ecosystem":"pip","affected":["vllm >= 0.5.1, < 0.11.0"],"patched":["vllm 0.11.0"],"published":"2025-10-07","updated":"2026-09-10","sourceUpdated":"2026-09-10T03:50:29.476458951Z","source":"OSV","sourceUrl":"https://osv.dev/vulnerability/GHSA-6fvq-23cw-5628","references":[{"url":"https://github.com/vllm-project/vllm/security/advisories/GHSA-6fvq-23cw-5628"},{"url":"https://github.com/vllm-project/vllm/pull/25794"},{"url":"https://github.com/vllm-project/vllm/commit/7977e5027c2250a4abc1f474c5619c40b4e5682f"},{"url":"https://github.com/vllm-project/vllm"}],"tags":["osv","pip"],"ingestedAt":"2026-07-08T18:25:46.452Z","slug":"CVE-2025-61620","body":"## Overview\n\n### 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\n\n## Affected packages\n\n- `vllm >= 0.5.1, < 0.11.0`\n\n## Remediation\n\nUpgrade to a patched release:\n\n- `vllm 0.11.0`","depth":"sunlit","depthScore":36,"depthScoreParts":{"impact":35.8,"likelihood":0,"exploitation":0,"ransomware":0},"changes":[]}