Iniciar sesión
Deutsch English Español Polski
Sin evaluar

vLLM is an inference and serving engine for large language models. In versions from 0.22.1 through 0.28.0, the operator-supplied model revision pin (--revision / --code-revision) is not propagated to

  • Publicada: 26.09.2026
  • Actualizado: 23.10.2026 22:17

vLLM is an inference and serving engine for large language models. In versions from 0.22.1 through 0.28.0, the operator-supplied model revision pin (--revision / --code-revision) is not propagated to several Hugging Face artifact loads for the FunAudioChat and Tarsier2 architectures: the WhisperFeatureExtractor and speech_tokenizer PreTrainedTokenizerFast loads in vllm/model_executor/models/funaudiochat.py and the Qwen2VLConfig.from_pretrained call used by Tarsier2ProcessingInfo in vllm/model_executor/models/qwen2_vl.py. As a result, deployments pinned to a reviewed revision still resolve these behavior-affecting processor, tokenizer, and config artifacts from the repository's default revision, so a later change to the upstream default branch can alter audio preprocessing, speech tokenizer behavior, or Tarsier2 configuration without any change to the operator's configured pin. This is a supply-chain integrity and reproducibility failure for pinned deployments; it is residual to the earlier fix tracked as GHSA-3ww4-5jv9-j5gm / CVE-2026-47155 and does not constitute remote code execution or a trust_remote_code=False bypass. The issue is fixed in version 0.28.0.

Software afectado

Haz clic en una tecnología para ver todas sus vulnerabilidades.

PlataformaPaqueteVersiones vulnerablesCorregida en
Python (PyPI) vllm >= 0.22.1, < 0.28.0 0.28.0

Referencias

Puntuación CVSS -
Vector CVSS
CVSS:4.0/AV:N/AC:H/AT:P/PR:N/UI:N/VC:L/VI:H/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Debilidad (CWE)
-
Plataforma
Python (PyPI)
Paquete
vllm
Publicada
26.09.2026
Actualizado
23.10.2026 22:17

Fuentes y licencias

OSV / GitHub Advisory Database - PYSEC-2026-4324 - PyPA Advisory Database; CC BY 4.0

Historial de vulnerabilidades: vllm

Todas las vulnerabilidades publicadas de este paquete (Python (PyPI)), de la más reciente a la más antigua. Muestra si es un fallo aislado o un paquete que hay que vigilar.

Todas (97) en el archivo
Entradas en total
97
Primera → última
09.2024 10.2026
Sin corrección
5
Explotada en ataques
0

Distribución por gravedad

  • Crítica 9
  • Alta 24
  • Media 48
  • Baja 4
  • Sin evaluar 12

Divulgaciones por año

Gravedad:
  1. vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the Rust frontend's track_http_metrics middleware records the raw HTTP method token as a Prometheus label for reques
    Alta · 7.5 CVE-2026-105759 < 0.30.0 0.30.0
  2. A flaw has been found in vllm-project vLLM up to 0.26.0. This vulnerability affects unknown code of the file rust/src/parser/src/unified/gemma4.rs of the component Gemma4UnifiedParser. Executing a man
    Alta · 7.5 CVE-2026-103241 < 0.27.0 0.27.0
  3. vLLM is an inference and serving engine for large language models. In versions from 0.22.1 through 0.28.0, the operator-supplied model revision pin (--revision / --code-revision) is not propagated to esta entrada
    Sin evaluar CVE-2026-100653 >= 0.22.1, < 0.28.0 0.28.0
  4. vLLM versions 0.22.0 through 0.23.0 fail to validate stop_token_ids against vocabulary bounds in Rust HTTP and gRPC frontends, allowing out-of-vocabulary token IDs to reach MinTokensLogitsProcessor. A
    Alta · 7.5 CVE-2026-100652 >= 0.22.0, < 0.24.0 0.24.0
  5. vLLM has Remote DoS via Invalid Recovered Token Reinjection
    Alta · 7.5 CVE-2026-54234 >= 0.17.1, < 0.24.0 0.24.0
  6. vLLM: ReDoS via structured_outputs.regex compiled without timeout in xgrammar and outlines backends
    Alta · 7.5 CVE-2026-55574 < 0.24.0 0.24.0
  7. vLLM denial of service via prompt embeds on M-RoPE models
    Alta CVE-2026-55514 >= 0.12.0, < 0.24.0 0.24.0
  8. vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package.
    Alta · 8.8 CVE-2026-54232 < 0.22.1 0.22.1
  9. vLLM: OpenAI auth bypass
    Crítica · 9.1 CVE-2026-48746 >= 0.3.0, < 0.22.0 0.22.0
  10. vLLM: Security Check Bypass via assert Statement in Activation Function Loading Allows Arbitrary Code Execution
    Alta · 7.5 CVE-2026-41523 < 0.22.0 0.22.0
  11. 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 processin
    Alta · 7.5 CVE-2026-5497 >= 0.8.0, < 0.19.0 0.19.0
  12. vLLM has Hardcoded Trust Override in Model Files Enables RCE Despite Explicit User Opt-Out
    Alta · 8.8 CVE-2026-27893 >= 0.10.1, < 0.18.0 0.18.0
  13. vLLM has RCE In Video Processing
    Crítica · 9.8 CVE-2026-22778 >= 0.8.3, < 0.14.1 0.14.1
  14. vLLM vulnerable to Server-Side Request Forgery (SSRF) through MediaConnector
    Alta · 7.1 CVE-2026-24779 < 0.14.1 0.14.1
  15. vLLM affected by RCE via auto_map dynamic module loading during model initialization
    Alta · 8.8 CVE-2026-22807 >= 0.10.1, < 0.14.0 0.14.0
  16. vLLM introduced enhanced protection for CVE-2025-62164
    Alta · 8.8 CVE-2026-56340 >= 0.10.2, < 0.13.0 0.13.0
  17. vLLM vulnerable to remote code execution via transformers_utils/get_config
    Alta · 7.1 CVE-2025-66448 < 0.11.1 0.11.1
  18. vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs
    Alta · 6.5 CVE-2025-62372 >= 0.5.5, < 0.11.1 0.11.1
  19. vLLM deserialization vulnerability leading to DoS and potential RCE
    Alta · 8.8 CVE-2025-62164 >= 0.10.2, < 0.11.1 0.11.1
  20. vLLM is vulnerable to Server-Side Request Forgery (SSRF) through `MediaConnector` class
    Alta · 7.1 CVE-2025-6242 >= 0.5.0, < 0.11.0 0.11.0
Mostrar más (14) Mostrando 20 de 34

Identifica las vulnerabilidades de tu aplicación

Con la integración de DockRay y el inventario de software activado, DockRay compara las versiones notificadas de los componentes con las vulnerabilidades conocidas y avisa de las coincidencias confirmadas. La cobertura depende de la tecnología compatible y de los avisos disponibles.

Conectar tu aplicación
Newsletter DockRay

La seguridad de tus aplicaciones en tu correo

Vulnerabilidades importantes, recomendaciones concretas y consejos prácticos para tus aplicaciones. Elige tus tecnologías y sigue lo que importa para tu proyecto.

  • Un resumen diario de nuevas vulnerabilidades, también las críticas.
  • Avisos sobre actualizaciones importantes.
  • Consejos prácticos sobre seguridad y monitorización de aplicaciones.

Déjalo vacío para recibir avisos de todas las tecnologías.

Política de privacidad

Suscripción gratuita. Date de baja con un clic en cualquier correo.