Summary

An untrusted multimodal request can set mmprocessorkwargs.coderevision. When vLLM serves a model whose Hugging Face processor uses dynamic automap code and the operator enabled --trust-remote-code, this request value reaches AutoProcessor.frompretrained 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 - Verified 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 automap.AutoProcessor code. 2. The operator enabled trustremotecode. 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 when that repository permits them. 5. A multimodal request reaches the processor path.

The default trustremotecode=False configuration is not affected.

Attack surface

mmprocessorkwargs is accepted directly by public request models:

- TokenizeChatRequest.mmprocessorkwargs - ChatCompletionRequest.mmprocessorkwargs

/tokenize is particularly relevant because vLLM's authentication middleware guards only /v1, /v2, /inference, and /cohere prefixes:

- AuthenticationMiddleware

Consequently, /tokenize remains unauthenticated even when --api-key is configured, unless an external proxy protects it. The vLLM Security Guide documents /tokenize as an unprotected endpoint.

Root cause

The request value follows this path:

1. OnlineRenderer.preprocesschat places request mmprocessorkwargs in prompt extras. 2. Multimodal rendering forwards those kwargs into processor construction through MMProcessorInputs and processmultimodalasync. 3. MultiModalConfig.mergemmprocessorkwargs overlays inference values on startup values, so the request wins. 4. cachedgetprocessorwithoutdynamickwargs filters only recognized processor-call keys. coderevision is retained as a processor-construction kwarg. 5. cachedprocessorfromconfig pins revision and trustremotecode, but forwards the remaining kwargs without reserving coderevision. 6. getprocessor calls AutoProcessor.frompretrained(..., revision=revision, trustremotecode=trustremotecode, kwargs). 7. Transformers AutoProcessor.frompretrained uses coderevision to retrieve and import the dynamic module through getclassfromdynamicmodule, 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 coderevision 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 coderevision and other loader/source controls in request mmprocessorkwargs, and pass modelconfig.coderevision 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 ignoring them before processor construction. Rejection is preferable because it makes attempted misuse visible.

A patch for this was merged here: https://github.com/vllm-project/vllm/pull/58830

Summary

API clients can supply arbitrary mmprocessorkwargs per request. vLLM forwards these values through the multimodal rendering and processing pipeline to Hugging Face and custom image/video processor constructors and calls. Resource-sensitive values such as image size, patch size, patch count, crop count, or frame count can therefore be controlled by a remote client.

For processors that upscale inputs, a single request can force an allocation large enough to terminate the API server or worker with an out-of-memory condition.

Affected Components

The per-request field was introduced in commit 041e29471671a3d43afedb6a749421d56ef4d3e7 and first shipped in v0.7.3. The generic forwarding path remains present in current main.

The Isaac path is a concrete high-amplification example:

- ChatCompletionRequest.mmprocessorkwargs - OnlineRenderer request extras - MMProcessorInputs.hfprocessormmkwargs - BaseMultiModalProcessor.info.gethfprocessor(hfprocessormmkwargs) - IsaacProcessingInfo.getimageprocessor(kwargs) - IsaacImageProcessor(kwargs) - processvisionforpatches()

Equivalent constructor forwarding exists in InternVL-derived processors, H2OVL, Step3VL, NemotronVL, and other multimodal processors. The issue is at the shared remote-input boundary and is not limited to a fixed model list.

Impact

- Availability: one request can exhaust CPU or GPU memory and terminate a shared service process. - Scope: all users of the affected service instance lose availability. - Authentication: vLLM's API key is optional, so deployments without an external authentication layer are remotely reachable without privileges.

Remediation

Reject non-empty per-request mmprocessorkwargs at every API-server request schema by default. Operators serving only trusted clients can explicitly opt in to the existing override behavior. Server-level --mm-processor-kwargs and offline LLM overrides remain available.

This shared boundary covers current and future multimodal processors instead of maintaining a model-specific list or a denylist that can miss custom processor arguments.

A patch for this was merged here: https://github.com/vllm-project/vllm/pull/58830