vLLM: Request-controlled mm_processor_kwargs.code_revision allows remote code execution

## Summary An untrusted multimodal request can set `mm_processor_kwargs.code_revision`. When vLLM serves a model whose Hugging Face processor uses dynamic `auto_map` code and the operator enabled `--trust-remote-code`, this request value reaches `AutoProcessor.from_pretrained` and selects the Python revision imported by Transformers. This lets a client replace the operator-reviewed processor module with code from another revision of the same model repository. The operator-selected model/data `revision` remains pinned; only the executed processor code changes. Importing a Python module is arbitrary code execution in the vLLM API/renderer process. The PoC below demonstrates this with an import-time filesystem write. It is fully offline and does not require a GPU, model weights, or changes to a real Hub repository. ## Affected snapshot - Repository: [`vllm-project/vllm`](https://github.com/vllm-project/vllm) - Verified commit: [`570735520942b6428ae98f1eb442c6d88adae55e`](https://github.com/vllm-project/vllm/commit/570735520942b6428ae98f1eb442c6d88adae55e) - Verified with Transformers 5.15.0 and 5.16.1 - Affected release range has not been bisected ## Affected configuration The vulnerable path exists when all of the following are true: 1. The served model's processor uses dynamic `auto_map.AutoProcessor` code. 2. The operator enabled `trust_remote_code`. 3. The server can fetch the request-selected revision, or it is already cached. 4. The attacker can create or otherwise control a revision in the served model repository. An example is a [Hugging Face community pull-request ref](https://huggingface.co/docs/hub/en/repositories-pull-requests-discussions#where-in-the-git-repo-are-changes-stored) when that repository permits them. 5. A multimodal request reaches the processor path. The default `trust_remote_code=False` configuration is not affected. ## Attack surface `mm_processor_kwargs` is accepted directly by public request models: - [`TokenizeChatRequest.mm_processor_kwargs`](https://github.com/vllm-project/vllm/blob/570735520942b6428ae98f1eb442c6d88adae55e/vllm/entrypoints/serve/tokenize/protocol.py#L111-L114) - [`ChatCompletionRequest.mm_processor_kwargs`](https://github.com/vllm-project/vllm/blob/570735520942b6428ae98f1eb442c6d88adae55e/vllm/entrypoints/openai/chat_completion/protocol.py#L372-L375) `/tokenize` is particularly relevant because vLLM's authentication middleware guards only `/v1`, `/v2`, `/inference`, and `/cohere` prefixes: - [`AuthenticationMiddleware`](https://github.com/vllm-project/vllm/blob/570735520942b6428ae98f1eb442c6d88adae55e/vllm/entrypoints/serve/middleware/authenticate.py#L11-L62) Consequently, `/tokenize` remains unauthenticated even when `--api-key` is configured, unless an external proxy protects it. The vLLM [Security Guide](https://docs.vllm.ai/en/latest/usage/security/#unprotected-endpoints-no-api-key-required) documents `/tokenize` as an unprotected endpoint. ## Root cause The request value follows this path: 1. [`OnlineRenderer.preprocess_chat`](https://github.com/vllm-project/vllm/blob/570735520942b6428ae98f1eb442c6d88adae55e/vllm/renderers/online_renderer.py#L406-L435) places request `mm_processor_kwargs` in prompt extras. 2. Multimodal rendering forwards those kwargs into processor construction through [`MMProcessorInputs`](https://github.com/vllm-project/vllm/blob/570735520942b6428ae98f1eb442c6d88adae55e/vllm/renderers/base.py#L751-L760) and [`_process_multimodal_async`](https://github.com/vllm-project/vllm/blob/570735520942b6428ae98f1eb442c6d88adae55e/vllm/renderers/base.py#L831-L847). 3. [`MultiModalConfig.merge_mm_processor_kwargs`](https://github.com/vllm-project/vllm/blob/570735520942b6428ae98f1eb442c6d88adae55e/vllm/config/multimodal.py#L506-L518) overlays inference values on startup values, so the request wins. 4. [`cached_get_processor_without_dynamic_kwargs`](https://github.com/vllm-project/vllm/blob/570735520942b6428ae98f1eb442c6d88adae55e/vllm/transformers_utils/processor.py#L344-L375) filters only recognized processor-call keys. `code_revision` is retained as a processor-construction kwarg. 5. [`cached_processor_from_config`](https://github.com/vllm-project/vllm/blob/570735520942b6428ae98f1eb442c6d88adae55e/vllm/transformers_utils/processor.py#L380-L390) pins `revision` and `trust_remote_code`, but forwards the remaining kwargs without reserving `code_revision`. 6. [`get_processor`](https://github.com/vllm-project/vllm/blob/570735520942b6428ae98f1eb442c6d88adae55e/vllm/transformers_utils/processor.py#L205-L245) calls `AutoProcessor.from_pretrained(..., revision=revision, trust_remote_code=trust_remote_code, **kwargs)`. 7. Transformers [`AutoProcessor.from_pretrained`](https://github.com/huggingface/transformers/blob/v5.15.0/src/transformers/models/auto/processing_auto.py#L294-L302) uses `code_revision` to retrieve and import the dynamic module through [`get_class_from_dynamic_module`](https://github.com/huggingface/transformers/blob/v5.15.0/src/transformers/dynamic_module_utils.py#L474-L575), then removes it before loading processor data. This is why code comes from the attacker revision while configuration/data remain operator-pinned. Request-time processor arguments are not separated from processor-construction arguments, and `code_revision` is not reserved. ## Suggested remediation 1. Separate processor-construction arguments from untrusted processor-call arguments. An allowlist of supported request-time preprocessing controls is safest. 2. Reject `code_revision` and other loader/source controls in request `mm_processor_kwargs`, and pass `model_config.code_revision` explicitly as the only permitted processor-code revision. 3. Add a regression test proving a request cannot change the loaded module revision while operator pins remain effective. 4. Protect `/tokenize` with the configured authentication policy as defense in depth. Expected safe behavior is either a 4xx response for reserved request keys or silently ig…