Sign in
Deutsch English Español Polski
Medium

vLLM Denial of Service via the best_of parameter

  • Disclosed: 17.09.2024
  • Updated: 11.10.2026 14:19

A vulnerability was found in the ilab model serve component, where improper handling of the best_of parameter in the vllm JSON web API can lead to a Denial of Service (DoS). The API used for LLM-based sentence or chat completion accepts a best_of parameter to return the best completion from several options. When this parameter is set to a large value, the API does not handle timeouts or resource exhaustion properly, allowing an attacker to cause a DoS by consuming excessive system resources. This leads to the API becoming unresponsive, preventing legitimate users from accessing the service.

Affected software

Click a technology to see all vulnerabilities affecting it.

PlatformPackageVulnerable versionsFixed in
Python (PyPI) vllm <= 0.5.0.post1 no fix

References

CVSS score 6.2/10
CVSS vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Weakness (CWE)
CWE-400
Platform
Python (PyPI)
Package
vllm
Disclosed
17.09.2024
Updated
11.10.2026 14:19

Sources and licenses

OSV / GitHub Advisory Database - PYSEC-2026-2025 - PyPA Advisory Database; CC BY 4.0

OSV / GitHub Advisory Database - GHSA-wc36-9694-f9rf - CC BY 4.0

Vulnerability history: vllm

All published vulnerabilities of this package (Python (PyPI)), newest first. Shows whether this is a one-off slip or a package that needs watching.

All (94) in the archive
Total entries
94
First → latest
09.2024 10.2026
Without a fix
5
Exploited in attacks
0

Severity breakdown

  • Critical 9
  • High 23
  • Medium 47
  • Low 4
  • Unrated 11

Disclosures per year

Severity:
  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
    High · 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
    High · 7.5 CVE-2026-100652 >= 0.22.0, < 0.24.0 0.24.0
  3. vLLM has Remote DoS via Invalid Recovered Token Reinjection
    High · 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
    High · 7.5 CVE-2026-55574 < 0.24.0 0.24.0
  5. vLLM denial of service via prompt embeds on M-RoPE models
    High 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.
    High · 8.8 CVE-2026-54232 < 0.22.1 0.22.1
  7. vLLM: OpenAI auth bypass
    Critical · 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
    High · 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
    High · 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
    High · 8.8 CVE-2026-27893 >= 0.10.1, < 0.18.0 0.18.0
  11. vLLM has RCE In Video Processing
    Critical · 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
    High · 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
    High · 8.8 CVE-2026-22807 >= 0.10.1, < 0.14.0 0.14.0
  14. vLLM introduced enhanced protection for CVE-2025-62164
    High · 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
    High · 7.1 CVE-2025-66448 < 0.11.1 0.11.1
  16. vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs
    High · 6.5 CVE-2025-62372 >= 0.5.5, < 0.11.1 0.11.1
  17. vLLM deserialization vulnerability leading to DoS and potential RCE
    High · 8.8 CVE-2025-62164 >= 0.10.2, < 0.11.1 0.11.1
  18. vLLM is vulnerable to Server-Side Request Forgery (SSRF) through `MediaConnector` class
    High · 7.1 CVE-2025-6242 >= 0.5.0, < 0.11.0 0.11.0
  19. vLLM is vulnerable to timing attack at bearer auth
    High · 7.5 CVE-2025-59425 < 0.11.0 0.11.0
  20. vLLM has remote code execution vulnerability in the tool call parser for Qwen3-Coder
    High · 8.8 CVE-2025-9141 >= 0.10.0, < 0.10.1.1 0.10.1.1
  21. vllm API endpoints vulnerable to Denial of Service Attacks
    High · 7.5 CVE-2025-48956 >= 0.1.0, < 0.10.1.1 0.10.1.1
  22. vLLM Allows Remote Code Execution via PyNcclPipe Communication Service
    Critical · 9.8 CVE-2025-47277 >= 0.6.5, < 0.8.5 0.8.5
  23. Remote Code Execution Vulnerability in vLLM Multi-Node Cluster Configuration
    High · 8.0 CVE-2025-30165 >= 0.5.2, < 0.10.0 0.10.0
  24. vLLM Vulnerable to Remote Code Execution via Mooncake Integration
    Critical · 10.0 CVE-2025-32444 >= 0.6.5, < 0.8.5 0.8.5
  25. Data exposure via ZeroMQ on multi-node vLLM deployment
    High · 7.5 CVE-2025-30202 >= 0.5.2, < 0.8.5 0.8.5
  26. CVE-2025-24357 Malicious model remote code execution fix bypass with PyTorch < 2.6.0
    Critical · 9.8 ghsa-ggpf-24jw-3fcw < 0.8.0 0.8.0
  27. vLLM deserialization vulnerability in vllm.distributed.GroupCoordinator.recv_object
    Critical · 9.8 CVE-2024-9052 <= 0.8.1 no fix
  28. vLLM Deserialization of Untrusted Data vulnerability
    Critical · 9.8 CVE-2024-11041 <= 0.6.2 no fix
  29. vLLM allows Remote Code Execution by Pickle Deserialization via AsyncEngineRPCServer() RPC server entrypoints
    Critical · 9.8 CVE-2024-9053 <= 0.6.0 no fix
  30. vLLM Allows Remote Code Execution via Mooncake Integration
    Critical · 9.0 CVE-2025-29783 >= 0.6.5, < 0.8.0 0.8.0
  31. vllm: Malicious model to RCE by torch.load in hf_model_weights_iterator
    High · 7.5 CVE-2025-24357 < 0.7.0 0.7.0
  32. vLLM Denial of Service via the best_of parameter this entry
    Medium · 6.2 CVE-2024-8939 <= 0.5.0.post1 no fix
  33. vLLM denial of service vulnerability
    High · 7.5 CVE-2024-8768 < 0.5.5 0.5.5

Know which vulnerabilities affect your application

With the DockRay integration and software inventory enabled, DockRay checks reported component versions against known vulnerabilities and notifies you about confirmed matches. Coverage depends on the supported technology and available advisory data.

Connect your application
DockRay newsletter

Application security in your inbox

Important vulnerabilities, actionable guidance and practical advice for your applications. Choose your technologies and follow what matters to your project.

  • A daily digest of new vulnerabilities, critical ones included.
  • Notifications about important updates.
  • Practical advice on application security and monitoring.

Leave empty to receive updates for every technology.

Privacy policy

Free to subscribe. Unsubscribe with one click in any email.