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PYSEC-2026-2019
No affected components available
Summary
Users can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct ndim but incorrect shape (e.g. hidden dimension is wrong), regardless of whether the model is intended to support such inputs (as defined in the Supported Models page).
The issue has existed ever since we added support for image embedding inputs, i.e. #6613 (released in v0.5.5)
Details
Using image embeddings as an example:
- For models that support image embedding inputs, the engine crashes when scattering the embeddings to
inputs_embeds(mismatched shape) - For models that don't support image embedding inputs, the engine crashes when validating the inputs inside
get_input_embeddings(validation fails).
This happens because we only validate ndim of the tensor, but not the full shape, in input processor (via MultiModalDataParser).
Impact
- Denial of service by crashing the engine
Mitigation
- Use API key to limit access to trusted users.
- Set
--limit-mm-per-promptto 0 for all non-text modalities to ban multimodal inputs, which includes multimodal embedding inputs. However, the model would then only accept text, defeating the purpose of using a multi-modal model.
Resolution
- https://github.com/vllm-project/vllm/pull/27204
The vulnerability can be exploited over the network without needing physical access. It is easy for an attacker to exploit this vulnerability. An attacker needs basic access or low-level privileges. No user interaction is needed for the attacker to exploit this vulnerability. The impact is confined to the system where the vulnerability exists. There is a high impact on the availability of the system.
Exploitation attempts have been detected. Elevated vigilance and prompt remediation are advised.
The exploit probability is very low. The vulnerability is unlikely to be exploited in the next 30 days.
We did not find any exploit available. Neither in GitHub repositories nor in the Exploit-Database.
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