CVE-2026-24779vLLM is an inference and serving engine for large language models (LLMs).

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.

CVSS

7.1 HIGH (v3.1, NVD)CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L

Weakness (CWE)

CWE-918

Known exploited

Not in CISA's KEV catalog

EPSS (exploitation in the next 30 days)

Not loaded for this CVE

Assigned by

Not given

NVD analysis

Modified

Record last updated

Jul 21, 2026

First seen by InfoSec Signals

Oct 9, 2026

The CVE record on cve.org · NVD's entry

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