Affected: vllm-project vllm, Red Hat AI Inference Server 3.2, Red Hat AI Inference Server 3.4, Red Hat Enterprise Linux AI 3.4, Red Hat AI Inference Server, Red Hat OpenShift AI (RHOAI)

vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.19.0, a Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the n parameter in the ChatCompletionRequest and CompletionRequest Pydantic models, an unauthenticated attacker can send a single HTTP request with an astronomically large n value. This completely blocks the Python asyncio event loop and causes immediate Out-Of-Memory crashes by allocating millions of request object copies in the heap before the request even reaches the scheduling queue. This vulnerability is fixed in 0.19.0.

Affected: PyPI: vllm

### Summary A Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the `n` parameter in the `ChatCompletionRequest` and `CompletionRequest` Pydantic models, an unauthenticated attacker can send a single HTTP request with an astronomically large `n` value. This completely blocks the Python `asyncio` event loop and causes immediate Out-Of-Memory crashes by allocating millions of request object copies in the heap before the request even reaches the scheduling queue.

### Details The root cause of this vulnerability lies in the missing upper bound checks across the request parsing and asynchronous scheduling layers:

1. **Protocol Layer:** In `vllm/entrypoints/openai/chat_completion/protocol.py`, the `n` parameter is defined simply as an integer without any `pydantic.Field` constraints for an upper bound. ```python class ChatCompletionRequest(OpenAIBaseModel): # Ordered by official OpenAI API documentation # https://platform.openai.com/docs/api/reference/chat/create messages: list[ChatCompletionMessageParam] model: str | None = None frequency_penalty: float | None = 0.0 logit_bias: dict[str, float] | None = None logprobs: bool | None = False top_logprobs: int | None = 0 max_tokens: int | None = Field( default=None, deprecated="max_tokens is deprecated in favor of " "the max_completion_tokens field", ) max_completion_tokens: int | None…