CVE-2026-105754
Published:October 05, 2026
Updated:October 10, 2026
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 Packages
vllm (CONDA):
Affected version(s) >=0.20.0 <0.30.0Fix Suggestion:
Update to version 0.30.0https://github.com/vllm-project/vllm.git (GITHUB):
Affected version(s) >=v0.19.2rc0 <v0.30.0Fix Suggestion:
Update to version v0.30.0vllm (PYTHON):
Affected version(s) >=0.20.0 <0.30.0Fix Suggestion:
Update to version 0.30.0Related Resources (7)
Do you need more information?
Contact UsCVSS v4
Base Score:
7.1
Attack Vector
NETWORK
Attack Complexity
LOW
Attack Requirements
NONE
Privileges Required
LOW
User Interaction
NONE
Vulnerable System Confidentiality
NONE
Vulnerable System Integrity
NONE
Vulnerable System Availability
HIGH
Subsequent System Confidentiality
NONE
Subsequent System Integrity
NONE
Subsequent System Availability
NONE
CVSS v3
Base Score:
6.5
Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality
NONE
Integrity
NONE
Availability
HIGH
Weakness Type (CWE)
EPSS
Base Score:
0.27