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Advisory details: vulnrichment / CVE-2026-22773
Advisory ID vulnrichment/CVE-2026-22773
Aliases
Summary vLLM is an inference and serving engine for large language models (LLMs). In versions from 0.6.4 to before 0.12.0, users can crash the vLLM engine serving multimodal models that use the Idefics3 vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination. This issue has been patched in version 0.12.0.
Status Published
Exploitability 0.5
Weighted Severity 0.0
Risk
Affected and Fixed Packages Package Details
Introduced and Fixed Package Commit Patches Package Commit Patches Details
Weaknesses (1)
CWE-770 Allocation of Resources Without Limits or Throttling
System Score Found at
cvssv3.1 6.5
ssvc Track
Reference id Reference type URL
GHSA-grg2-63fw-f2qr https://github.com/vllm-project/vllm/security/advisories/GHSA-grg2-63fw-f2qr
No exploits are available.

No EPSS data available for this advisory.

Patch URL
There are no known patches.
Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H Found at None
Attack Vector (AV) Attack Complexity (AC) Privileges Required (PR) User Interaction (UI) Scope (S) Confidentiality Impact (C) Integrity Impact (I) Availability Impact (A)

network

adjacent_network

local

physical

low

high

none

low

high

none

required

unchanged

changed

high

low

none

high

low

none

high

low

none
Vector: SSVCv2/E:N/A:N/T:P/P:M/B:A/M:M/D:T/2026-01-12T13:22:42Z/ Found at None

Vector: SSVCv2/E:N/A:N/T:P/P:M/B:A/M:M/D:T/2026-01-12T13:22:42Z/

Decision: Track

Source URL: https://github.com/cisagov/vulnrichment/blob/develop/2026/22xxx/CVE-2026-22773.json

Source Advisory: vulnrichment/CVE-2026-22773

View SSVC decision tree 
[{'Exploitation': 'none'},
 {'Automatable': 'no'},
 {'Technical Impact': 'partial'},
 {'Mission Prevalence': 'minimal'},
 {'Public Well-being Impact': 'material'},
 {'Mission & Well-being': 'medium'}]