A AegiFlow
HIGHCVSS 7.1EPSS 0.5%

CVE-2026-24779

CVE-2026-24779 updated by NVD

Published
2026-01-28
Modified
2026-07-23
EPSS percentile
41%
Sources
github-advisory, nvd

Summary

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.

Affected packages

EcosystemPackageAffected versionsFixed versions
PyPIvllm0.14.1

Remediation: Upgrade to 0.14.1 or later.

References

Includes data from the GitHub Advisory Database, licensed under CC-BY 4.0.

Includes data from the National Vulnerability Database (NIST). NVD data is in the public domain; this page is not endorsed by NIST.

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EPSS scores provided by the FIRST.org Exploit Prediction Scoring System.