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Medium

vLLM through 0.29.0 fails to properly validate bad_words token indices against the model's generation output width in SamplingParams.update_from_tokenizer(). Attackers can supply out-of-bounds token i

  • Disclosed: 19.09.2026
  • Updated: 11.10.2026 15:18

vLLM through 0.29.0 fails to properly validate bad_words token indices against the model's generation output width in SamplingParams.update_from_tokenizer(). Attackers can supply out-of-bounds token indices that corrupt logits memory of concurrent requests, causing different in-flight HTTP requests to return incorrect tokens.

Affected software

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PlatformPackageVulnerable versionsFixed in
Python (PyPI) vllm < 0.30.0 0.30.0

References

CVSS score 4.3/10
CVSS vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:N
Weakness (CWE)
-
Platform
Python (PyPI)
Package
vllm
Disclosed
19.09.2026
Updated
11.10.2026 15:18

Sources and licenses

OSV / GitHub Advisory Database - PYSEC-2026-4000 - PyPA Advisory Database; 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 through 0.29.0 fails to properly validate bad_words token indices against the model's generation output width in SamplingParams.update_from_tokenizer(). Attackers can supply out-of-bounds token i this entry
    Medium · 4.3 CVE-2026-93989 < 0.30.0 0.30.0
  4. vLLM has Remote DoS via Invalid Recovered Token Reinjection
    High · 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
    High · 7.5 CVE-2026-55574 < 0.24.0 0.24.0
  6. vLLM denial of service via prompt embeds on M-RoPE models
    High 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.
    High · 8.8 CVE-2026-54232 < 0.22.1 0.22.1
  8. vLLM: OpenAI auth bypass
    Critical · 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
    High · 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
    High · 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
    High · 8.8 CVE-2026-27893 >= 0.10.1, < 0.18.0 0.18.0
  12. vLLM has RCE In Video Processing
    Critical · 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
    High · 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
    High · 8.8 CVE-2026-22807 >= 0.10.1, < 0.14.0 0.14.0
  15. vLLM introduced enhanced protection for CVE-2025-62164
    High · 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
    High · 7.1 CVE-2025-66448 < 0.11.1 0.11.1
  17. 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
  18. vLLM deserialization vulnerability leading to DoS and potential RCE
    High · 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
    High · 7.1 CVE-2025-6242 >= 0.5.0, < 0.11.0 0.11.0
  20. vLLM is vulnerable to timing attack at bearer auth
    High · 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
    High · 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
    High · 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
    Critical · 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
    High · 8.0 CVE-2025-30165 >= 0.5.2, < 0.10.0 0.10.0
  25. vLLM Vulnerable to Remote Code Execution via Mooncake Integration
    Critical · 10.0 CVE-2025-32444 >= 0.6.5, < 0.8.5 0.8.5
  26. Data exposure via ZeroMQ on multi-node vLLM deployment
    High · 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
    Critical · 9.8 ghsa-ggpf-24jw-3fcw < 0.8.0 0.8.0
  28. vLLM deserialization vulnerability in vllm.distributed.GroupCoordinator.recv_object
    Critical · 9.8 CVE-2024-9052 <= 0.8.1 no fix
  29. vLLM Deserialization of Untrusted Data vulnerability
    Critical · 9.8 CVE-2024-11041 <= 0.6.2 no fix
  30. vLLM allows Remote Code Execution by Pickle Deserialization via AsyncEngineRPCServer() RPC server entrypoints
    Critical · 9.8 CVE-2024-9053 <= 0.6.0 no fix
  31. vLLM Allows Remote Code Execution via Mooncake Integration
    Critical · 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
    High · 7.5 CVE-2025-24357 < 0.7.0 0.7.0
  33. vLLM denial of service vulnerability
    High · 7.5 CVE-2024-8768 < 0.5.5 0.5.5

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