Bug 2546186 (CVE-2026-105754)

Summary: CVE-2026-105754 vllm: vllm: Denial of Service via unvalidated multimodal parameters in scale-out endpoint
Product: [Other] Security Response Reporter: OSIDB Bzimport <bzimport>
Component: vulnerabilityAssignee: Product Security DevOps Team <prodsec-dev>
Status: NEW --- QA Contact:
Severity: medium Docs Contact:
Priority: medium    
Version: unspecifiedCC: alinfoot, bbrownin, dtrifiro, weaton
Target Milestone: ---Keywords: Security
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Hardware: All   
OS: Linux   
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A flaw was found in vLLM. The scale-out inference service fails to properly validate caller-supplied multimodal parameters, such as image geometry and cache identifiers, against the active model's configuration. An authenticated remote attacker can exploit this vulnerability by submitting crafted feature data to the generation endpoint, terminating the shared engine process and causing a Denial of Service (DoS). In addition, this issue may allow attackers to poison or retrieve shared multimodal cache state across different user requests.
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Description OSIDB Bzimport 2026-10-05 23:01:40 UTC 
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.