vLLM: server-side request forgery

### Summary `vllm/transformers_utils/processors/mimo_v2_omni.py` , the multimodal processor for `MiMoV2OmniForCausalLM` , issues `requests.get(...)` directly on user-supplied image and audio URL strings and `Image.open(...)` on user-supplied local paths, **without** the SSRF / `allowed_local_media_path` checks that `vllm.multimodal.utils.MediaConnector` was hardened with in **GHSA-qh4c-xf7m-gxfc**, **GHSA-v359-jj2v-j536**, and **GHSA-pf3h-qjgv-vcpr**. This is the same bug class as those three published advisories, in a code path the patches missed. When a user passes a URL or local-file string through `multi_modal_data` (e.g. `LLM.generate(multi_modal_data={"image": "http://..."})`), the processor takes the unsanitized string and dispatches it without any URL-scheme allowlist, network-target allowlist, size cap, or local-path allowlist. ### Details **File:** `vllm/transformers_utils/processors/mimo_v2_omni.py` (current `main`) **Sink 1 , image SSRF + local-file read (`_fetch_image`, lines 231–249):** ```python def _fetch_image(src: Any) -> Image.Image: if isinstance(src, Image.Image): return _to_rgb(src) if isinstance(src, bytes): return _to_rgb(copy.deepcopy(Image.open(BytesIO(src)))) if isinstance(src, str): if src.startswith(("http://", "https://")): r = requests.get(src, timeout=30) # SSRF: no allowlist, follows redirects r.raise_for_status() return _to_rgb(copy.deepcopy(Image.open(BytesIO(r.content)))) if src.startswith("file://"): return _to_rgb(Image.open(src[7:])) # arbitrary local file read if src.startswith("data:image"): ... return _to_rgb(Image.open(src)) # fallback also opens local files raise ValueError(f"Unrecognized image source: {type(src)}") ``` **Sink 2 , audio SSRF (around line 471):** ```python elif audio.startswith(("http://", "https://")): r = requests.get(audio, timeout=30) # SSRF: same pattern r.raise_for_status() file_obj = io.BytesIO(r.content) ``` **Reachability.** `_fetch_image` is invoked from `MiMoVLProcessor.process_image`: ```python def process_image(self, image: ImageInput) -> torch.Tensor: kw = self._resolve_img_kw(image) src = image.image if isinstance(src, (str, bytes)): src = _fetch_image(src) ... ``` `MiMoVLProcessor` is wrapped by `MiMoV2OmniMultiModalProcessor` and registered for the `MiMoV2OmniForCausalLM` model architecture (`vllm/model_executor/models/mimo_v2_omni.py:1169`). Whenever a user passes a string into `multi_modal_data["image"]` (or `["audio"]`) for this model, the unsanitized URL/path reaches the sink. **Comparison to the recent fixes.** The remediation pattern adopted in the three earlier advisories was to route every external resource fetch through `MediaConnector`, which checks `allowed_local_media_path` and applies SSRF protection before issuing the network request. `chat_utils.py` (lines 838, 902, 924, 963, 1053, 1081) already uses `self._connector.fetch_image / fetch_audio / fetch_video`. The model processor in `mimo_v2_omni.py` was added later and skipped the connector , it calls `requests.get` and `Image.open` directly. Result: the public OpenAI chat-completion path is protected, but library use (`LLM.generate(multi_modal_data=...)`), batch processing, and any other path that lets a string reach the processor receive no protection. ### Impact 1. **SSRF , internal-network probing / cloud-metadata theft.** Standard `requests.get` follows redirects and accepts any URL. An attacker who controls a `multi_modal_data` value can: - read AWS / GCP / Azure instance metadata (e.g. `http://169.254.169.254/latest/meta-data/iam/security-credentials/`), - probe internal services on the vLLM host (`http://127.0.0.1:<port>`, `http://10.x.y.z`), - exfiltrate via DNS / HTTP timing oracles even when the body is rejected by `Image.open`. 2

CVSS 3.1

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N

Severity from

GitHub (reviewed advisory)

Weakness

CWE-918

Also known as

CVE-2026-73560, PYSEC-2026-3934

• github.com/vllm-project/vllm/security/advisories/GHSA-4hhp-h66f-j5j7
• nvd.nist.gov/vuln/detail/CVE-2026-73560
• github.com/vllm-project/vllm/pull/43117
• github.com/vllm-project/vllm/commit/54503ecec0f3ac31e5ecfc5f28652e4cc42307b5
• github.com/vllm-project/vllm
• github.com/vllm-project/vllm/releases/tag/v0.26.0