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    CVE-2025-48956

    vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.10.1.1, a Denial of Service (DoS) vulnerability can be triggered by sending a single HTTP GET request with an extremely large header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does not require authentication, making it exploitable by any remote user. This vulnerability is fixed in 0.10.1.1.

    Published:Aug 21, 2025
    Last Modified:Oct 9, 2025
    EPS:Aug 21, 2025
    EPSS Score:0.00425
    CVSS Score:7.5

    Affected Products

    Vendor
    Vllm
    Product
    Vllm
    Vendor
    Vllm-project
    Product
    Vllm

    Exploits

    No exploit reference

    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