FKIE_CVE-2026-41523

Vulnerability from fkie_nvd - Published: 2026-06-22 23:16 - Updated: 2026-10-25 23:10
Summary
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, an assert-based security check in vLLM's activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLLM runs in Python optimized mode (python -O or PYTHONOPTIMIZE=1). This vulnerability is fixed in 0.22.0.
References
security-advisories@github.comhttps://github.com/vllm-project/vllm/commit/b3c7ffcab82c2439726f8cb213800f6f38c023d3Patch
security-advisories@github.comhttps://github.com/vllm-project/vllm/security/advisories/GHSA-q8gq-377p-jq3rExploit, Third Party Advisory
security-advisories@github.comhttps://huntr.com/bounties/dcb05b04-e625-41e7-adbc-bbae0cc2d64cThird Party Advisory
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:36005
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:36006
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:57380
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:57387
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:57389
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:57390
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:59138
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:59139
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:59144
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:59151
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:61627
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:61629
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:62335
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/errata/RHSA-2026:62336
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://access.redhat.com/security/cve/CVE-2026-41523
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://bugzilla.redhat.com/show_bug.cgi?id=2491582
0b0ca135-0b70-47e7-9f44-1890c2a1c46chttps://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-41523.json
Impacted products
Vendor Product Version
vllm vllm *

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  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.0, an assert-based security check in vLLM\u0027s activation function loading allows any unauthenticated attacker to achieve arbitrary code execution on the server by publishing a malicious HuggingFace model, when vLLM runs in Python optimized mode (python -O or PYTHONOPTIMIZE=1). This vulnerability is fixed in 0.22.0."
    },
    {
      "lang": "es",
      "value": "vLLM es un motor de inferencia y servicio para modelos de lenguaje grandes (LLM). Antes de la 0.22.0, una comprobaci\u00f3n de seguridad basada en assert en la carga de funciones de activaci\u00f3n de vLLM permite a cualquier atacante no autenticado lograr la ejecuci\u00f3n de c\u00f3digo arbitrario en el servidor al publicar un modelo malicioso de HuggingFace, cuando vLLM se ejecuta en modo optimizado de Python (python -O o PYTHONOPTIMIZE=1). Esta vulnerabilidad se corrige en la 0.22.0."
    }
  ],
  "id": "CVE-2026-41523",
  "lastModified": "2026-10-25T23:10:00.167",
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  ]
}



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Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.

Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

JSON
How durations are calculated

Following the VTEM framework (Black Hat USA 2015), time to patch starts when the vendor or a coordinator was informed, total time to patch at discovery, and disclosure to patch at public disclosure; vendor response time runs from the report to its acknowledgement. Time to exploit and time unpatched start at the earliest discovery or report, falling back to public disclosure. Unpatched time ends at the first recorded patch, or the calculation time when no patch is recorded. Exploitation to patch shows the interval between those two events; a published proof of concept is a public exploit, not exploitation.

Each result shows its source dates. A negative interval means the end event came first; when date-only inputs leave the order open, the result says so. Date-only inputs are approximate. The earliest evidence wins: KEV report dates and sightings are labelled as such. Records describing another vulnerability are listed but never calculated from, and a PoC alone does not establish exploitation. Missing patch evidence does not prove that no patch exists. Filters below change the event list only.

Events included in the timeline

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    Related by attack behaviour

    Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.