vLLM: Scale-out disaggregated multimodal transport trusts caller-supplied features
Default status is the baseline for the product, each version can override it (e.g. patched versions marked unaffected).
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Github GHSA |
GHSA-ph72-cqr5-qpp7 | vLLM: Scale-out disaggregated multimodal transport trusts caller-supplied features |
No CVSS v4.0
No CVSS v3.0
No CVSS v2
This CVE is not in the KEV list.
Exploit prediction score: < 1% (0.269% probability within 30 days)
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Vllm
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| CPEs | cpe:2.3:a:vllm:vllm:*:*:*:*:*:*:*:* | |
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Vllm
Vllm vllm |
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Vllm-project
Vllm-project vllm |
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| Description | 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. | |
| Title | vLLM: Scale-out disaggregated multimodal transport trusts caller-supplied features | |
| Weaknesses | CWE-1284 CWE-20 CWE-617 CWE-639 CWE-668 CWE-704 |
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cvssV3_1
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