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MEDIUM · 5.4

CVE-2026-34753

vLLM is an inference and serving engine for large language models (LLMs). From 0.16.0 to before 0.19.0, a server-side request forgery (SSRF) vulnerability in download_bytes_from_url allows any actor w...

Vulnerability Description

vLLM is an inference and serving engine for large language models (LLMs). From 0.16.0 to before 0.19.0, a server-side request forgery (SSRF) vulnerability in download_bytes_from_url allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions. This can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. This vulnerability is fixed in 0.19.0.

CVSS Score

5.4

MEDIUM

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:L
Attack Vector
NETWORK
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality
LOW
Integrity
NONE
Availability
LOW

Affected Products

VendorProductVersions
VllmVllm>= 0.16.0, < 0.19.0

Related Weaknesses (CWE)

CWE-918

References

FAQ

What is CVE-2026-34753?

CVE-2026-34753 is a vulnerability with a CVSS score of 5.4 (MEDIUM). vLLM is an inference and serving engine for large language models (LLMs). From 0.16.0 to before 0.19.0, a server-side request forgery (SSRF) vulnerability in download_bytes_from_url allows any actor w...

How severe is CVE-2026-34753?

CVE-2026-34753 has been rated MEDIUM with a CVSS base score of 5.4/10. Review the CVSS metrics above for detailed severity breakdown.

Is there a patch for CVE-2026-34753?

Check the references section above for vendor advisories and patch information. Affected products include: Vllm Vllm.