Nixpkgs security tracker
7.1 HIGH
- CVSS version: 3.1
- Attack vector (AV): NETWORK
- Attack complexity (AC): LOW
- Privileges required (PR): LOW
- User interaction (UI): NONE
- Scope (S): UNCHANGED
- Confidentiality impact (C): HIGH
- Integrity impact (I): NONE
- Availability impact (A): LOW
SSRF Protection Bypass in vLLM
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/security/advisories/GHSA-v359-jj2v-j536 x_refsource_CONFIRM
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https://github.com/vllm-project/vllm/pull/34743 x_refsource_MISC
Affected products
- ==>= 0.15.1, < 0.17.0
Matching in nixpkgs
pkgs.vllm
High-throughput and memory-efficient inference and serving engine for LLMs
pkgs.pkgsRocm.vllm
High-throughput and memory-efficient inference and serving engine for LLMs
pkgs.python312Packages.vllm
High-throughput and memory-efficient inference and serving engine for LLMs
pkgs.python313Packages.vllm
High-throughput and memory-efficient inference and serving engine for LLMs
Package maintainers
-
@happysalada Raphael Megzari <raphael@megzari.com>
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@CertainLach Yaroslav Bolyukin <iam@lach.pw>
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@daniel-fahey Daniel Fahey <daniel.fahey+nixpkgs@pm.me>