--- id: CVE-2026-55514 aliases: - GHSA-33cg-gxv8-3p8g - PYSEC-2026-2303 title: vLLM denial of service via prompt embeds on M-RoPE models summary: vLLM denial of service via prompt embeds on M-RoPE models severity: high cvss: 6.5 cvssVector: 'CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H' vendor: vllm product: vllm ecosystem: pip affected: - 'vllm >= 0.12.0, < 0.24.0' patched: - vllm 0.24.0 published: '2026-07-20' updated: '2026-09-10' sourceUpdated: '2026-09-10T03:50:51.451622987Z' source: OSV sourceUrl: 'https://osv.dev/vulnerability/GHSA-33cg-gxv8-3p8g' references: - url: >- https://github.com/vllm-project/vllm/security/advisories/GHSA-33cg-gxv8-3p8g - url: 'https://nvd.nist.gov/vuln/detail/CVE-2026-55514' - url: 'https://github.com/vllm-project/vllm/pull/45252' - url: >- https://github.com/vllm-project/vllm/commit/470229c37efaf69c86e8bc97482b0b1ff7551c65 - url: >- https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2026-2303.yaml - url: 'https://github.com/vllm-project/vllm' - url: 'https://github.com/vllm-project/vllm/releases/tag/v0.24.0' tags: - osv - pip epss: 0.00665 epssPercentile: 0.4966 cvssSource: osv ingestedAt: '2026-07-13T18:58:08.955Z' --- ## Overview ### Summary _Short summary of the problem. Make the impact and severity as clear as possible. For example: An unsafe deserialization vulnerability allows any unauthenticated user to execute arbitrary code on the server._ Sending a pure prompt embeds payload in a `/v1/completions` request with a model using M-RoPE causes the EngineCore to fail an assertion and fatally crash, shutting down the entire server application. Any remote user who is authorized to make a `/v1/completions` endpoint can trivially make such a request and induce a crash. ### Details _Give all details on the vulnerability. Pointing to the incriminated source code is very helpful for the maintainer._ In commit [56669c1](https://github.com/vllm-project/vllm/commit/56669c1f293d5c53b6a19ddf2f78802fa9fff2c2), a simple assert intended to be a type-narrowing assert was added to the `_init_mrope_positions` method in `GPUModelRunner` (the offending line on main at the time of writing: https://github.com/vllm-project/vllm/blob/2d481f8a946ee0521872af0f098674a8ee01ce4a/vllm/v1/worker/gpu_model_runner.py#L1588-L1607). ```python assert req_state.prompt_token_ids is not None, ( "M-RoPE requires prompt_token_ids to be available." ) ``` This type narrowing assert is to prevent mypy errors later in the function because `None` is not a valid type for `mrope_model.get_mrope_input_positions`. Unfortunately, this assertion is not always true. `/v1/completions` requests that specify `prompt=None` and `prompt_embeds=` will indeed create a CachedRequestState where `prompt_token_ids` is `None`. This triggers the assertion, which in turn crashes the EngineCore and the Server application. ``` (EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 3997, in execute_model (EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] deferred_state_corrections_fn = self._update_states(scheduler_output) (EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 1239, in _update_states (EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] self._init_mrope_positions(req_state) (EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 1582, in _init_mrope_positions (EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] assert req_state.prompt_token_ids is not None, ( (EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=351) ERROR 06-11 00:48:03 [core.py:1167] AssertionError: M-RoPE requires prompt_token_ids to be available. (APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] AsyncLLM output_handler failed. (APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] Traceback (most recent call last): (APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py", line 660, in output_handler (APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] outputs = await engine_core.get_output_async() (APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 1030, in get_output_async (APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] raise self._format_exception(outputs) from None (APIServer pid=1) ERROR 06-11 00:48:03 [async_llm.py:704] vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause. ``` All requests using the `/v1/chat/completions` endpoints will have text/`prompt_token_ids` parts (corresponding to the chat template), and `prompt_embeds` parts are handled as mm_features. This method (rightly) filters out those `prompt_embeds` content parts as they are treated as text positions. A sufficient solution to type narrowing here without raising a fatal assertion is to instead replace the assertion with a using dummy token ids: ```python def _init_mrope_positions(self, req_state: CachedRequestState): model = self.get_model() assert supports_mrope(model), "M-RoPE support is not implemented." mrope_model = cast(SupportsMRoPE, model) # Filter out prompt_embeds modality (text-only position info) mrope_features = [ f for f in req_state.mm_features if f.modality != "prompt_embeds" ] # Handle both token_ids and embeddings-only inputs if req_state.prompt_token_ids is not None: input_tokens = req_state.prompt_token_ids elif req_state.prompt_embeds is not None: # For text-only embeddings, dummy token IDs are safe since # get_mrope_input_positions only uses len(input_tokens) when mm_features is empty seq_len = req_state.prompt_embeds.shape[0] input_tokens = list(range(seq_len)) # Verify no mm_features remain (should be true after prompt_embeds filter) assert len(mrope_features) == 0, ( "M-RoPE with prompt_embeds-only input should have no multimodal features" ) else: raise ValueError( "M-RoPE requires either prompt_token_ids or prompt_embeds." ) req_state.mrope_positions, req_state.mrope_position_delta = ( mrope_model.get_mrope_input_positions( input_tokens, mrope_features, ) ) ``` Technically, in isolation, this method still crashes in the case where `req_state.prompt_token_ids is None and req_state.mm_features`, so the solution above still leaves that potential vector open. As far as can be determined, however, such a `req_state` is impossible in the first place in online mode, because it would require a `/v1/completions` request with `prompt_embeds` AND multimodal features, but the `/v1/completions` request schema does not expose multimodal inputs in any discernible way. Today, those are the only two endpoints with `prompt_embeds` support. When in offline mode, it *is technically* possible to directly create an `EngineCoreRequest` that has `prompt_embeds and not prompt_token_ids and mm_features`, and pass that to `LLM.generate`. That would trigger this same assertion, and no validation would prevent that combination. It is strongly suspected, though, that this combination would be undefined in any model that support M-RoPE, because it would not be possible to determine which token positions correspond to `mm_features`. The proposed solution above would end up not setting `req_state.mrope_positions` and `req_state.mrope_position_delta` in this scenario, which could result in undefined behavior. `prompt_embeds` is far more familiar here than M-RoPE, and it is understood that each model that supports it is responsible for defining its own `get_mrope_input_positions` which have varying implementations. There is insufficient knowledge to be prescriptive in how the two features should interact in the offline case, other than possibly raising a validation error earlier on preventing that combination (which would emulate the current assertion behavior). Regardless, in offline mode, the chances of a remote user being able to exploit this are slim-to-nil compared to the online case which is incredibly straightforward. ### Impact _What kind of vulnerability is it? Who is impacted?_ - Denial of Service caused by an incorrect assertion inside of the `GPUModelRunner` which causes a fatal EngineCore exception - Any configuration with `--enable-prompt-embeds` and M-RoPE-supported model is vulnerable - The attack is extremely easy from the remote attacker's perspective (copying the official `prompt_embeds` online mode docs examples almost-verbatim, accounting for model-name and connection details, of course, will induce a guaranteed shutdown) ## Affected packages - `vllm >= 0.12.0, < 0.24.0` ## Remediation Upgrade to a patched release: - `vllm 0.24.0`