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vLLM vulnerable to Denial of Service by abusing xgrammar cache

  • Publicada: 15.04.2025
  • Actualizado: 11.10.2026 12:19

Impact

This report is to highlight a vulnerability in XGrammar, a library used by the structured output feature in vLLM. The XGrammar advisory is here: https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-389x-67px-mjg3

The xgrammar library is the default backend used by vLLM to support structured output (a.k.a. guided decoding). Xgrammar provides a required, built-in cache for its compiled grammars stored in RAM. xgrammar is available by default through the OpenAI compatible API server with both the V0 and V1 engines.

A malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service by consuming all of the system's RAM.

Note that even if vLLM was configured to use a different backend by default, it is still possible to choose xgrammar on a per-request basis using the guided_decoding_backend key of the extra_body field of the request with the V0 engine. This per-request choice is not available when using the V1 engine.

Patches

Workarounds

There is no way to workaround this issue in existing versions of vLLM other than preventing untrusted access to the OpenAI compatible API server.

References

Software afectado

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PlataformaPaqueteVersiones vulnerablesCorregida en
Python (PyPI) vllm >= 0.6.5, < 0.8.4 0.8.4

Referencias

Puntuación CVSS 6.5/10
Vector CVSS
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Debilidad (CWE)
CWE-1395, CWE-770
Plataforma
Python (PyPI)
Paquete
vllm
Publicada
15.04.2025
Actualizado
11.10.2026 12:19

Fuentes y licencias

OSV / GitHub Advisory Database - GHSA-hf3c-wxg2-49q9 - 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 (94) en el archivo
Entradas en total
94
Primera → última
09.2024 10.2026
Sin corrección
5
Explotada en ataques
0

Distribución por gravedad

  • Crítica 9
  • Alta 23
  • Media 47
  • Baja 4
  • Sin evaluar 11

Divulgaciones por año

Gravedad:
  1. vLLM vulnerable to Denial of Service by abusing xgrammar cache esta entrada
    Media · 6.5 ghsa-hf3c-wxg2-49q9 >= 0.6.5, < 0.8.4 0.8.4
  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 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
  4. vLLM has Remote DoS via Invalid Recovered Token Reinjection
    Alta · 7.5 CVE-2026-54234 >= 0.17.1, < 0.24.0 0.24.0
  5. 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
  6. vLLM denial of service via prompt embeds on M-RoPE models
    Alta CVE-2026-55514 >= 0.12.0, < 0.24.0 0.24.0
  7. 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
  8. vLLM: OpenAI auth bypass
    Crítica · 9.1 CVE-2026-48746 >= 0.3.0, < 0.22.0 0.22.0
  9. 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
  10. 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
  11. 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
  12. vLLM has RCE In Video Processing
    Crítica · 9.8 CVE-2026-22778 >= 0.8.3, < 0.14.1 0.14.1
  13. vLLM vulnerable to Server-Side Request Forgery (SSRF) through MediaConnector
    Alta · 7.1 CVE-2026-24779 < 0.14.1 0.14.1
  14. 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
  15. vLLM introduced enhanced protection for CVE-2025-62164
    Alta · 8.8 CVE-2026-56340 >= 0.10.2, < 0.13.0 0.13.0
  16. vLLM vulnerable to remote code execution via transformers_utils/get_config
    Alta · 7.1 CVE-2025-66448 < 0.11.1 0.11.1
  17. 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
  18. vLLM deserialization vulnerability leading to DoS and potential RCE
    Alta · 8.8 CVE-2025-62164 >= 0.10.2, < 0.11.1 0.11.1
  19. 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
  20. vLLM is vulnerable to timing attack at bearer auth
    Alta · 7.5 CVE-2025-59425 < 0.11.0 0.11.0
  21. vLLM has remote code execution vulnerability in the tool call parser for Qwen3-Coder
    Alta · 8.8 CVE-2025-9141 >= 0.10.0, < 0.10.1.1 0.10.1.1
Mostrar más (12) Mostrando 21 de 33

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