- CVSS
- HIGH · 7.1v3.1CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L
- Published
- 2026-03-09
- Weakness
- CWE-918, CWE-474
- Source
- nvd.nist.gov/vuln/detail/CVE-2026-25960
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.
References
- https://github.com/vllm-project/vllm/commit/6f3b2047abd4a748e3db4a68543f8221358002c0
- 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
- https://access.redhat.com/errata/RHSA-2026:24977
- https://access.redhat.com/security/cve/CVE-2026-25960
How GTK Cyber trains on this
AI security training at GTK Cyber covers the LLM and ML-pipeline vulnerability classes that vulnerabilities like CVE-2026-25960 fall into. Our hands-on courses are taught by Charles Givre and other practitioners who break and defend production AI systems.