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vLLM vulnerable to DoS via large Chat Completion or Tokenization requests with specially crafted `chat_template_kwargs`

  • Publicada: 20.11.2025
  • Actualizado: 11.10.2026 15:17

Summary

The /v1/chat/completions and /tokenize endpoints allow a chat_template_kwargs request parameter that is used in the code before it is properly validated against the chat template. With the right chat_template_kwargs parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests

Details

In serving_engine.py, the chat_template_kwargs are unpacked into kwargs passed to chat_utils.py apply_hf_chat_template with no validation on the keys or values in that chat_template_kwargs dict. This means they can be used to override optional parameters in the apply_hf_chat_template method, such as tokenize, changing its default from False to True.

https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/openai/serving_engine.py#L809-L814

https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/chat_utils.py#L1602-L1610

Both serving_chat.py and serving_tokenization.py call into this _preprocess_chat method of serving_engine.py and they both pass in chat_template_kwargs.

So, a chat_template_kwargs like {"tokenize": True} makes tokenization happen as part of applying the chat template, even though that is not expected. Tokenization is a blocking operation, and with sufficiently large input can block the API server's event loop, which blocks handling of all other requests until this tokenization is complete.

This optional tokenize parameter to apply_hf_chat_template does not appear to be used, so one option would be to just hard-code that to always be False instead of allowing it to be optionally overridden by callers. A better option may be to not pass chat_template_kwargs as unpacked kwargs but instead as a dict, and only unpack them after the logic in apply_hf_chat_template that resolves the kwargs against the chat template.

Impact

Any authenticated user can cause a denial of service to a vLLM server with Chat Completion or Tokenize requests.

Fix

https://github.com/vllm-project/vllm/pull/27205

Software afectado

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PlataformaPaqueteVersiones vulnerablesCorregida en
Python (PyPI) vllm >= 0.5.5, < 0.11.1 0.11.1

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-770
Plataforma
Python (PyPI)
Paquete
vllm
Publicada
20.11.2025
Actualizado
11.10.2026 15:17

Fuentes y licencias

OSV / GitHub Advisory Database - PYSEC-2026-2012 - PyPA Advisory Database; CC BY 4.0

OSV / GitHub Advisory Database - GHSA-69j4-grxj-j64p - 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. 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
  2. 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
  3. vLLM has Remote DoS via Invalid Recovered Token Reinjection
    Alta · 7.5 CVE-2026-54234 >= 0.17.1, < 0.24.0 0.24.0
  4. 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
  5. vLLM denial of service via prompt embeds on M-RoPE models
    Alta CVE-2026-55514 >= 0.12.0, < 0.24.0 0.24.0
  6. 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
  7. vLLM: OpenAI auth bypass
    Crítica · 9.1 CVE-2026-48746 >= 0.3.0, < 0.22.0 0.22.0
  8. 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
  9. 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
  10. 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
  11. vLLM has RCE In Video Processing
    Crítica · 9.8 CVE-2026-22778 >= 0.8.3, < 0.14.1 0.14.1
  12. vLLM vulnerable to Server-Side Request Forgery (SSRF) through MediaConnector
    Alta · 7.1 CVE-2026-24779 < 0.14.1 0.14.1
  13. 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
  14. vLLM introduced enhanced protection for CVE-2025-62164
    Alta · 8.8 CVE-2026-56340 >= 0.10.2, < 0.13.0 0.13.0
  15. vLLM vulnerable to remote code execution via transformers_utils/get_config
    Alta · 7.1 CVE-2025-66448 < 0.11.1 0.11.1
  16. vLLM vulnerable to DoS via large Chat Completion or Tokenization requests with specially crafted `chat_template_kwargs` esta entrada
    Media · 6.5 CVE-2025-62426 >= 0.5.5, < 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
  22. vllm API endpoints vulnerable to Denial of Service Attacks
    Alta · 7.5 CVE-2025-48956 >= 0.1.0, < 0.10.1.1 0.10.1.1
  23. vLLM Allows Remote Code Execution via PyNcclPipe Communication Service
    Crítica · 9.8 CVE-2025-47277 >= 0.6.5, < 0.8.5 0.8.5
  24. Remote Code Execution Vulnerability in vLLM Multi-Node Cluster Configuration
    Alta · 8.0 CVE-2025-30165 >= 0.5.2, < 0.10.0 0.10.0
  25. vLLM Vulnerable to Remote Code Execution via Mooncake Integration
    Crítica · 10.0 CVE-2025-32444 >= 0.6.5, < 0.8.5 0.8.5
  26. Data exposure via ZeroMQ on multi-node vLLM deployment
    Alta · 7.5 CVE-2025-30202 >= 0.5.2, < 0.8.5 0.8.5
  27. CVE-2025-24357 Malicious model remote code execution fix bypass with PyTorch < 2.6.0
    Crítica · 9.8 ghsa-ggpf-24jw-3fcw < 0.8.0 0.8.0
  28. vLLM deserialization vulnerability in vllm.distributed.GroupCoordinator.recv_object
    Crítica · 9.8 CVE-2024-9052 <= 0.8.1 sin corrección
  29. vLLM Deserialization of Untrusted Data vulnerability
    Crítica · 9.8 CVE-2024-11041 <= 0.6.2 sin corrección
  30. vLLM allows Remote Code Execution by Pickle Deserialization via AsyncEngineRPCServer() RPC server entrypoints
    Crítica · 9.8 CVE-2024-9053 <= 0.6.0 sin corrección
  31. vLLM Allows Remote Code Execution via Mooncake Integration
    Crítica · 9.0 CVE-2025-29783 >= 0.6.5, < 0.8.0 0.8.0
  32. vllm: Malicious model to RCE by torch.load in hf_model_weights_iterator
    Alta · 7.5 CVE-2025-24357 < 0.7.0 0.7.0
  33. vLLM denial of service vulnerability
    Alta · 7.5 CVE-2024-8768 < 0.5.5 0.5.5

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