CVE-2026-25960

Deeper analysis

CVE-2026-25960 is a server-side request forgery (SSRF) protection bypass vulnerability in vLLM, an inference and serving engine for large language models (LLMs). The issue affects vLLM version 0.17.0 and stems from a fix for the prior CVE-2026-24779 that was introduced in version 0.15.1. Specifically, the SSRF protection in the load_from_url_async method validates user-provided URLs using urllib3.util.parse_url() to extract the hostname. However, the method performs actual HTTP requests via aiohttp, which uses the yarl library for URL parsing, leading to inconsistent behavior that allows bypasses. The vulnerability carries a CVSS v3.1 base score of 7.1 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L) and maps to CWE-918.

An attacker requires low privileges (PR:L) to exploit this vulnerability over the network (AV:N) with low attack complexity (AC:L) and no user interaction (UI:N). By supplying a malicious URL to the load_from_url_async method, the attacker can evade hostname validation, tricking the vLLM server into issuing requests to unauthorized destinations, such as internal network resources. Successful exploitation results in high confidentiality impact (C:H) with low availability impact (A:L) and no integrity impact (I:N), potentially exposing sensitive data.

Mitigation details are outlined in vLLM security advisories GHSA-qh4c-xf7m-gxfc and GHSA-v359-jj2v-j536, along with the fixing commit 6f3b2047abd4a748e3db4a68543f8221358002c0 and pull request #34743. Security practitioners should apply these updates to eliminate the parsing discrepancy and restore effective SSRF protection.

This vulnerability is notable in AI/ML contexts, as vLLM powers LLM inference and serving deployments that may handle remote model loading, increasing exposure in production environments.