CVE-2026-24779

Описание

vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.14.1, a Server-Side Request Forgery (SSRF) vulnerability exists in the MediaConnector class within the vLLM project's multimodal feature set. The load_from_url and load_from_url_async methods obtain and process media from URLs provided by users, using different Python parsing libraries when restricting the target host. These two parsing libraries have different interpretations of backslashes, which allows the host name restriction to be bypassed. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources. This vulnerability is particularly critical in containerized environments like llm-d, where a compromised vLLM pod could be used to scan the internal network, interact with other pods, and potentially cause denial of service or access sensitive data. For example, an attacker could make the vLLM pod send malicious requests to an internal llm-d management endpoint, leading to system instability by falsely reporting metrics like the KV cache state. Version 0.14.1 contains a patch for the issue.

A flaw was found in vLLM, an inference and serving engine for large language models (LLMs). A Server-Side Request Forgery (SSRF) vulnerability exists in the MediaConnector class, specifically within the load_from_url and load_from_url_async methods. An attacker can exploit differing interpretations of backslashes by Python parsing libraries used for host restrictions to bypass these restrictions. This allows the attacker to force the vLLM server to make arbitrary requests to internal network resources, potentially leading to information disclosure, denial of service, or unauthorized access within containerized environments.