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Critical

vLLM: OpenAI auth bypass

  • Disclosed: 16.06.2026
  • Updated: 11.10.2026 15:17

Summary

A vulnerability in ASGI web servers and starlette's trust on those web servers enables an authentication bypass of the OpenAI API AuthenticationMiddleware, which was discovered during @x41sec's source code audit. It allows to use the API without providing the configured VLLM_API_KEY or --api-key.

Details

In https://github.com/vllm-project/vllm/blob/v0.14.0/vllm/entrypoints/openai/api_server.py#L689-L692 the url_path is taken from the URL, which is reconstructed by starlette based on the request scope.

python
from starlette.datastructures import URL, Headers, MutableHeaders, State

# ...

url_path = URL(scope=scope).path.removeprefix(root_path)
headers = Headers(scope=scope)
if url_path.startswith("/v1") and not self.verify_token(headers):
    response = JSONResponse(content={"error": "Unauthorized"}, status_code=401)
    return response(scope, receive, send)
return self.app(scope, receive, send)

The request scope includes the request's Host: header and reconstructs the URL as shown below:

python
f"{scheme}://{host_header}{path}"

Neither starlette nor any of the ASGI servers (including uvicorn, which vllm uses) properly filter the Host: header for invalid characters. This allows an attacker to include special URL characters such as / or ? in the Host: header and thereby control the reconstructed URL and it's .path attribute.

FastAPI/starlette's routing uses the HTTP path and does not depend on the parsed url.path attribute, allowing attackers to reach an endpoint via a certain path while providing a different value in the .path.

Impact

  • Instances of vllm that use an API Key for the OpenAI API and expose the API to attackers.
  • Instances behind an RFC-conforming web server (such as nginx) are not affected.

Affected software

Click a technology to see all vulnerabilities affecting it.

PlatformPackageVulnerable versionsFixed in
Python (PyPI) vllm >= 0.3.0, < 0.22.0 0.22.0

References

CVSS score 9.1/10
CVSS vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:H
Weakness (CWE)
CWE-444, CWE-501
Platform
Python (PyPI)
Package
vllm
Disclosed
16.06.2026
Updated
11.10.2026 15:17

Sources and licenses

OSV / GitHub Advisory Database - PYSEC-2026-226 - PyPA Advisory Database; CC BY 4.0

OSV / GitHub Advisory Database - GHSA-94f4-hr76-p5j6 - 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 this entry
    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
Show more (12) Showing 20 of 32

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