FKIE_CVE-2026-5497
Vulnerability from fkie_nvd - Published: 2026-06-11 10:16 - Updated: 2026-10-26 11:10
Severity
Summary
vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.
References
{
"affected": [
{
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"version": "unspecified",
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}
],
"source": "0b0ca135-0b70-47e7-9f44-1890c2a1c46c"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
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"versionEndExcluding": "0.19.0",
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],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication."
},
{
"lang": "es",
"value": "Las versiones 0.8.0 y posteriores de vLLM son vulnerables a un ataque de denegaci\u00f3n de servicio (DoS) por falta de memoria (OOM) debido al procesamiento ilimitado del recuento de fotogramas en el m\u00e9todo \u2019VideoMediaIO.load_base64()\u2019. Al procesar URLs de datos \u2019video/jpeg\u2019, el m\u00e9todo divide la cadena de datos base64 por comas para extraer fotogramas JPEG individuales sin aplicar un l\u00edmite de recuento de fotogramas. Un atacante puede explotar esto creando una \u00fanica solicitud de API que contenga miles de fotogramas JPEG codificados en base64 y separados por comas en una URL de datos, lo que hace que el servidor decodifique todos los fotogramas en la memoria y se bloquee debido al consumo excesivo de memoria. Esta vulnerabilidad es accesible a trav\u00e9s de la API de finalizaci\u00f3n de chat compatible con OpenAI y no requiere autenticaci\u00f3n."
}
],
"id": "CVE-2026-5497",
"lastModified": "2026-10-26T11:10:00.157",
"metrics": {
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"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
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"role": "CISA Coordinator",
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}
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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
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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