CVE-2026-25960CRITICAL 7.1EPSS p42.6%

CVE-2026-25960CVE-2026-25960

vllm / vllm

Description

vLLM is an inference and serving engine for large language models (LLMs). The SSRF protection fix for CVE-2026-24779 add in 0.15.1 can be bypassed in the load_from_url_async method due to inconsistent URL parsing behavior between the validation layer and the actual HTTP client. The SSRF fix uses urllib3.util.parse_url() to validate and extract the hostname from user-provided URLs. However, load_from_url_async uses aiohttp for making the actual HTTP requests, and aiohttp internally uses the yarl library for URL parsing. This vulnerability in 0.17.0.

Scoring

CVSS 3.17.1 (CRITICAL)
VectorCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L
EPSS0.54% probability of exploitation · percentile 42.6% · 2026-08-03T12:00:16Z
Published2026-03-09
Last modified2026-07-21

Underlying weaknesses· 1

CWE-918

References

  1. https://github.com/vllm-project/vllm/commit/6f3b2047abd4a748e3db4a68543f8221358002c0
  2. https://github.com/vllm-project/vllm/pull/34743
  3. https://github.com/vllm-project/vllm/security/advisories/GHSA-qh4c-xf7m-gxfc
  4. https://github.com/vllm-project/vllm/security/advisories/GHSA-v359-jj2v-j536

1

TypeTargetConfidenceTier
WeaknessServer-Side Request Forgery (SSRF)cwe-9180%live

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Nearest entities by semantic similarity across the cs-graph corpus.

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Sourced from NVD + FIRST.org EPSS. Curated for EU compliance use cases by Adam Lundqvist, Founder at SQUR.