Summary
vLLM: SSRF + arbitrary local file read in MiMoV2OmniMultiModalProcessor fetchimage and audio loader bypass MediaConnector protections
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):
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):
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:
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.
Suggested remediation
Replace direct requests.get and bare Image.open paths with MediaConnector.fetch_image / fetch_audio_async (or pass the inputs through MediaConnector before they reach the processor):
# vllm/transformers_utils/processors/mimo_v2_omni.py
from vllm.multimodal.utils import MediaConnector
_connector = MediaConnector()
def _fetch_image(src):
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):
return _to_rgb(_connector.fetch_image(src)) # delegates to the hardened path
raise ValueError(f"Unrecognized image source: {type(src)}")
Same change for the audio loader at line 471. This re-uses the SSRF allowlist, allowed_local_media_path policy, and size caps that the previous patches added.
Alternative: forbid str src from reaching the processor and require all multi-modal pre-processing to go through chat_utils.py / MediaConnector before hitting the model. Larger surface change, but completes the architectural fix.
Discovery
Static review on vllm@main (HEAD as of 2026-04-30), found by triaging the file list against the three recent SSRF advisories: the mimo_v2_omni.py processor, added after those fixes, reintroduced the same bypass class.
Reporter
Ievgen Bondarenko, [email protected], GitHub @ibondarenko1
Impact
- SSRF, internal-network probing / cloud-metadata theft. Standard
requests.getfollows redirects and accepts any URL. An attacker who controls amulti_modal_datavalue 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.
- read AWS / GCP / Azure instance metadata (e.g.
- Arbitrary local file read via
file://path(line 242) and the unguarded fallbackImage.open(src)(line 248). Any file readable by the vLLM process is reachable through the model pipeline; with suitable formats this exposes/etc/passwd,~/.aws/credentials, etc. - Server-side traffic generation / amplification by hammering arbitrary URLs from the vLLM host, with a 30-second timeout per request.
Untrusted input controls the target URL of a server-initiated request, which may reach internal services not otherwise accessible from outside. Typical impact: access to internal metadata services, internal APIs, or cloud credentials.
CVE-2026-73560 has a CVSS score of 6.5 (Medium). The vector is network-reachable, low privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (0.26.0); upgrading removes the vulnerable code path.
Affected versions
Security releases
Kodem intelligence
Severity tells you how bad this could be in the worst case. It does not tell you whether you are exposed. Exploitability and impact are functions of runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A vulnerable package can sit in your dependency tree and never run.
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Frequently Asked Questions
- What is CVE-2026-73560? CVE-2026-73560 is a medium-severity server-side request forgery (SSRF) vulnerability in vllm (pip), affecting versions < 0.26.0. It is fixed in 0.26.0. Untrusted input controls the target URL of a server-initiated request, which may reach internal services not otherwise accessible from outside.
- How severe is CVE-2026-73560? CVE-2026-73560 has a CVSS score of 6.5 (Medium). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
- Which versions of vllm are affected by CVE-2026-73560? vllm (pip) versions < 0.26.0 is affected.
- Is there a fix for CVE-2026-73560? Yes. CVE-2026-73560 is fixed in 0.26.0. Upgrade to this version or later.
- Is CVE-2026-73560 exploitable, and should I be worried? Whether CVE-2026-73560 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
- What actually determines whether CVE-2026-73560 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
- How do I fix CVE-2026-73560? Upgrade
vllmto 0.26.0 or later.