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Advisory details: pypa / vllm/PYSEC-2026-143
Advisory ID pypa/vllm/PYSEC-2026-143
Aliases CVE-2026-22773
GHSA-grg2-63fw-f2qr
PYSEC-2026-143
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 6.8
Risk 3.4
Affected and Fixed Packages Package Details
Introduced and Fixed Package Commit Patches Package Commit Patches Details
Weaknesses (0)
There are no known CWE.
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:N/UI:N/S:U/C:N/I:N/A:H Found at https://github.com/pypa/advisory-database/blob/main/vulns/vllm/PYSEC-2026-143.yaml
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/

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'}]