CVE-2025-66448

CVE-2025-66448 is a remote code execution vulnerability in vLLM, an inference and serving engine for large language models, affecting versions prior to 0.11.1. The issue resides in the Nemotron_Nano_VL_Config class, where loading a model configuration containing an auto_map entry triggers resolution via get_class_from_dynamic_module, which fetches and instantiates Python code from a remote repository specified in the auto_map string. This execution occurs even when the caller explicitly sets trust_remote_code=False in vllm.transformers_utils.config.get_config, bypassing intended security controls.

An attacker can exploit this by publishing a seemingly benign frontend repository with a config.json file that includes an auto_map pointing to a separate malicious backend repository. A victim loading the frontend model config will silently fetch and execute the backend's arbitrary Python code on their host. Per the CVSS v3.1 score of 7.1 (AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H), exploitation requires network access, high attack complexity, low privileges, and user interaction, but yields high impacts on confidentiality, integrity, and availability, classified under CWE-94 (code injection).

The vulnerability is addressed in vLLM version 0.11.1. Official mitigation details are available in the project's security advisory at GHSA-8fr4-5q9j-m8gm, the fixing pull request at github.com/vllm-project/vllm/pull/28126, and the commit ffb08379d8870a1a81ba82b72797f196838d0c86, which practitioners should review for patch implementation guidance.

This flaw highlights risks in AI/ML inference engines handling untrusted model configurations from remote sources, with no reported real-world exploitation as of the CVE publication on 2025-12-01.