CVE-2026-105754

vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the /inference/v1/generate endpoint in the disaggregated scale-out path accepts caller-supplied tensors in the features.kwargs_data field, cache identifiers in the features.mm_hashes field, ranges in the features.mm_placeholders field, and wire-selected multimodal field processors without rebinding them to the active model renderer contract. Forged grid geometry, field types, or non-positive placeholder lengths can terminate the shared EngineCore; when an attacker knows or can induce a victim's content hash, forged cache hashes can poison or retrieve cross-request encoder-cache state; and dropped sparse placeholder masks can alter replayed transport semantics. This issue is fixed in version 0.30.0.

vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the /inference/v1/generate endpoint in the disaggregated scale-out path accepts caller-supplied tensors in the features.kwargs_data field, cache identifiers in the features.mm_hashes field, ranges in the features.mm_placeholders field, and wire-selected multimodal field processors without rebinding them to the active model renderer contract. Forged grid geometry, field types, or non-positive placeholder lengths can terminate the shared EngineCore; when an attacker knows or can induce a victim's content hash, forged cache hashes can poison or retrieve cross-request encoder-cache state; and dropped sparse placeholder masks can alter replayed transport semantics. This issue is fixed in version 0.30.0.

Affected

• Ecosystem / package: pip / vllm
• Affected versions: vLLM ≤ 0.25.1 (confirmed on 0.25.1, commit 752a3a504485). The lower bound predates 0.25.1; maintainers can confirm how far back the scale-out transport path reaches.

Summary

vLLM's disaggregated scale-out transport splits a multimodal request into a trusted render step (POST /v1/chat/completions/render) and a separate generate step (POST /inference/v1/generate). The generate route decodes a caller-supplied features object — serialized encoder tensors (kwargs_data), multimodal hashes (mm_hashes), placeholder ranges (mm_placeholders), and the internal field-processor selection — and forwards it into the engine as if it had come from the trusted renderer, with no rebinding to (or validation against) the active model's renderer contract. Because the two routes are ordinary auth-guarded HTTP endpoints (the /inference prefix is registered by default on generate-capable servers), any authenticated caller can submit an otherwise-valid render body with a single forged field.

Depending on which field is forged, this produces:

• an engine-fatal crash of the shared EngineCore process (denial of service), reproduced as a CUDA illegal-memory-access, a post-admission rank-mismatch ValueError, and a hard assert — three independent forged fields (sites 1, 2, 3);
• silent cross-request encoder-cache poisoning / disclosure of shared encoder state when the cache-key hash is not bound to the payload (site 4);
• transport-level integrity loss when sparse placeholder masks are dropped during render-to-generate replay (site 5).

All five share one root cause and one fix shape: the reconstructed multimodal state on the scale-out path is trusted without being rebound to, and validated against, the active model's renderer output before it reaches the engine.

These sites are distinct from prior multimodal hardening. Site 1 survives GHSA-wv77-2vpf-vmmg (that fix validates full tensor shape in MultiModalDataParser/get_input_embeddings on the prompt-embeds path), because our request forges image_grid_thw metadata with the pixel bytes intact and reaches the Qwen2 vision RoPE/cu_seqlens and image_embeds.split sink, which the shape-check fix does not rebind. Site 4 is distinct from GHSA-c65p-x677-fgj6 (which folds metadata into MultiModalHasher.serialize_item to stop hash collisions), because the scale-out generate path trusts a caller-supplied mm_hash as the cache key with no origin binding, so that fix does not stop a caller from submitting a victim's hash or a kwargs_data=None cache read.

Affected code

Links pinned to the confirmed commit 752a3a504485 (v0.25.1).

Shared entry point and control surface for all five sites:

• POST /inference/v1/generate route: vllm/entrypoints/scale_out/token_in_token_out/api_router.py#L46-L75.
• Public features schema (kwargs_data, mm_hashes, mm_placeholders): vllm/entrypoints/scale_out/token_in_token_out/protocol.py#L42-L63.
• ServingTokens.serve_tokens() copies the decoded geometry and hashes into engine structures without rebinding to the renderer schema:…

vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the /inference/v1/generate endpoint in the disaggregated scale-out path accepts caller-supplied tensors in the features.kwargs_data field, cache identifiers in the features.mm_hashes field, ranges in the features.mm_placeholders field, and wire-selected multimodal field processors without rebinding them to the active model renderer contract. Forged grid geometry, field types, or non-positive placeholder lengths can terminate the shared EngineCore; when an attacker knows or can induce a victim's content hash, forged cache hashes can poison or retrieve cross-request encoder-cache state; and dropped sparse placeholder masks can alter replayed transport semantics. This issue is fixed in version 0.30.0.

vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the /inference/v1/generate endpoint in the disaggregated scale-out path accepts caller-supplied tensors in the features.kwargs_data field, cache identifiers in the features.mm_hashes field, ranges in the features.mm_placeholders field, and wire-selected multimodal field processors without rebinding them to the active model renderer contract. Forged grid geometry, field types, or non-positive placeholder lengths can terminate the shared EngineCore; when an attacker knows or can induce a victim's content hash, forged cache hashes can poison or retrieve cross-request encoder-cache state; and dropped sparse placeholder masks can alter replayed transport semantics. This issue is fixed in version 0.30.0.

Affected

• Ecosystem / package: pip / vllm
• Affected versions: vLLM ≤ 0.25.1 (confirmed on 0.25.1, commit 752a3a504485). The lower bound predates 0.25.1; maintainers can confirm how far back the scale-out transport path reaches.

Summary

vLLM's disaggregated scale-out transport splits a multimodal request into a trusted render step (POST /v1/chat/completions/render) and a separate generate step (POST /inference/v1/generate). The generate route decodes a caller-supplied features object — serialized encoder tensors (kwargs_data), multimodal hashes (mm_hashes), placeholder ranges (mm_placeholders), and the internal field-processor selection — and forwards it into the engine as if it had come from the trusted renderer, with no rebinding to (or validation against) the active model's renderer contract. Because the two routes are ordinary auth-guarded HTTP endpoints (the /inference prefix is registered by default on generate-capable servers), any authenticated caller can submit an otherwise-valid render body with a single forged field.

Depending on which field is forged, this produces:

• an engine-fatal crash of the shared EngineCore process (denial of service), reproduced as a CUDA illegal-memory-access, a post-admission rank-mismatch ValueError, and a hard assert — three independent forged fields (sites 1, 2, 3);
• silent cross-request encoder-cache poisoning / disclosure of shared encoder state when the cache-key hash is not bound to the payload (site 4);
• transport-level integrity loss when sparse placeholder masks are dropped during render-to-generate replay (site 5).

All five share one root cause and one fix shape: the reconstructed multimodal state on the scale-out path is trusted without being rebound to, and validated against, the active model's renderer output before it reaches the engine.

These sites are distinct from prior multimodal hardening. Site 1 survives GHSA-wv77-2vpf-vmmg (that fix validates full tensor shape in MultiModalDataParser/get_input_embeddings on the prompt-embeds path), because our request forges image_grid_thw metadata with the pixel bytes intact and reaches the Qwen2 vision RoPE/cu_seqlens and image_embeds.split sink, which the shape-check fix does not rebind. Site 4 is distinct from GHSA-c65p-x677-fgj6 (which folds metadata into MultiModalHasher.serialize_item to stop hash collisions), because the scale-out generate path trusts a caller-supplied mm_hash as the cache key with no origin binding, so that fix does not stop a caller from submitting a victim's hash or a kwargs_data=None cache read.

Affected code

Links pinned to the confirmed commit 752a3a504485 (v0.25.1).

Shared entry point and control surface for all five sites:

• POST /inference/v1/generate route: vllm/entrypoints/scale_out/token_in_token_out/api_router.py#L46-L75.
• Public features schema (kwargs_data, mm_hashes, mm_placeholders): vllm/entrypoints/scale_out/token_in_token_out/protocol.py#L42-L63.
• ServingTokens.serve_tokens() copies the decoded geometry and hashes into engine structures without rebinding to the renderer schema:…

Affected

• Ecosystem / package: pip / vllm
• Affected versions: vLLM ≤ 0.25.1 (confirmed on 0.25.1, commit 752a3a504485). The lower bound predates 0.25.1; maintainers can confirm how far back the scale-out transport path reaches.

Summary

vLLM's disaggregated scale-out transport splits a multimodal request into a trusted render step (POST /v1/chat/completions/render) and a separate generate step (POST /inference/v1/generate). The generate route decodes a caller-supplied features object — serialized encoder tensors (kwargs_data), multimodal hashes (mm_hashes), placeholder ranges (mm_placeholders), and the internal field-processor selection — and forwards it into the engine as if it had come from the trusted renderer, with no rebinding to (or validation against) the active model's renderer contract. Because the two routes are ordinary auth-guarded HTTP endpoints (the /inference prefix is registered by default on generate-capable servers), any authenticated caller can submit an otherwise-valid render body with a single forged field.

Depending on which field is forged, this produces:

• an engine-fatal crash of the shared EngineCore process (denial of service), reproduced as a CUDA illegal-memory-access, a post-admission rank-mismatch ValueError, and a hard assert — three independent forged fields (sites 1, 2, 3);
• silent cross-request encoder-cache poisoning / disclosure of shared encoder state when the cache-key hash is not bound to the payload (site 4);
• transport-level integrity loss when sparse placeholder masks are dropped during render-to-generate replay (site 5).

All five share one root cause and one fix shape: the reconstructed multimodal state on the scale-out path is trusted without being rebound to, and validated against, the active model's renderer output before it reaches the engine.

These sites are distinct from prior multimodal hardening. Site 1 survives GHSA-wv77-2vpf-vmmg (that fix validates full tensor shape in MultiModalDataParser/get_input_embeddings on the prompt-embeds path), because our request forges image_grid_thw metadata with the pixel bytes intact and reaches the Qwen2 vision RoPE/cu_seqlens and image_embeds.split sink, which the shape-check fix does not rebind. Site 4 is distinct from GHSA-c65p-x677-fgj6 (which folds metadata into MultiModalHasher.serialize_item to stop hash collisions), because the scale-out generate path trusts a caller-supplied mm_hash as the cache key with no origin binding, so that fix does not stop a caller from submitting a victim's hash or a kwargs_data=None cache read.

Affected code

Links pinned to the confirmed commit 752a3a504485 (v0.25.1).

Shared entry point and control surface for all five sites:

• POST /inference/v1/generate route: vllm/entrypoints/scale_out/token_in_token_out/api_router.py#L46-L75.
• Public features schema (kwargs_data, mm_hashes, mm_placeholders): vllm/entrypoints/scale_out/token_in_token_out/protocol.py#L42-L63.
• ServingTokens.serve_tokens() copies the decoded geometry and hashes into engine structures without rebinding to the renderer schema:…