CVE-2026-25960
HIGH
7,1
Source: [email protected]
Attack Vector: network
Attack Complexity: low
Privileges Required: low
User Interaction: none
Scope: unchanged
Confidentiality: high
Integrity: none
Availability: low
Description
AI Translation Available
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.
EPSS (Exploit Prediction Scoring System)
Trend Analysis
EPSS (Exploit Prediction Scoring System)
Prevede la probabilità di sfruttamento basata su intelligence sulle minacce e sulle caratteristiche della vulnerabilità.
EPSS Score
0,0002
Percentile
0,0th
Updated
EPSS Score Trend (Last 7 Days)
918
Server-Side Request Forgery (SSRF)
IncompleteCommon Consequences
Security Scopes Affected:
Confidentiality
Integrity
Access Control
Potential Impacts:
Read Application Data
Execute Unauthorized Code Or Commands
Bypass Protection Mechanism
Applicable Platforms
Technologies:
AI/ML, Web Based, Web Server
https://github.com/vllm-project/vllm/commit/6f3b2047abd4a748e3db4a68543f8221358…
https://github.com/vllm-project/vllm/pull/34743
https://github.com/vllm-project/vllm/security/advisories/GHSA-qh4c-xf7m-gxfc
https://github.com/vllm-project/vllm/security/advisories/GHSA-v359-jj2v-j536