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    CVE-2026-22773

    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.

    Published:Jan 10, 2026
    Last Modified:Apr 18, 2026
    EPS:Jan 10, 2026
    EPSS Score:0.00021
    CVSS Score:6.5

    Affected Products

    Vendor
    Vllm
    Product
    Vllm
    Vendor
    Vllm-project
    Product
    Vllm

    Exploits

    https://github.com/vllm-project/vllm/security/advisories/GHSA-grg2-63fw-f2qr

    Related CVEs

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    CVE-2026-56340 (CVSS 8.7) — vLLM versions >= 0.10.2 and < 0.13.0 are missing sparse tensor validation in multimodal embeddings processing.…
    CVE-2025-71379 (CVSS 5.3) — vLLM versions >= 0.6.3 and < 0.9.0 contain multiple regular expression denial of service (ReDoS) vulnerabiliti…
    CVE-2026-5497 (CVSS 7.5) — vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to u…
    CVE-2026-44223 (CVSS 6.5) — vLLM is an inference and serving engine for large language models (LLMs). From 0.18.0 to before 0.20.0, the ex…
    CVE-2026-44222 (CVSS 6.5) — vLLM is an inference and serving engine for large language models (LLMs). From 0.6.1 to before 0.20.0, there i…
    CVE-2026-7141 (CVSS 2.9) — A vulnerability was found in vLLM up to 0.19.0. The affected element is the function has_mamba_layers of the f…
    CVE-2026-34756 (CVSS 6.5) — vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.19.0, a Denia…

    Common Vulnerability Scoring System

    Attack Vector
    Network
    Adjacent
    Local
    Physical
    Privileges Required
    None
    Low
    High
    User Interaction
    None
    Required
    Scope
    Unchanged
    Changed
    Confidentiality
    None
    Low
    High
    Integrity
    None
    Low
    High
    Availability
    None
    Low
    High