vLLM vulnerable to Server-Side Request Forgery (SSRF) in `MediaConnector`

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Summary

CVECVE-2026-24779
StatePUBLISHED
AssignerGitHub_M
Source PriorityCVE Program / NVD first with legacy fallback
Published2026-01-27 22:15:57 UTC
Updated2026-06-30 03:17:39 UTC
DescriptionvLLM 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.

Risk And Classification

Primary CVSS: v3.1 7.1 HIGH from ADP

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L

EPSS: 0.005280000 probability, percentile 0.407210000 (date 2026-07-01)

Problem Types: CWE-918 | CWE-918 CWE-918: Server-Side Request Forgery (SSRF) | CWE-918 Server-Side Request Forgery (SSRF)

VersionSourceTypeScoreSeverityVector
3.1ADPCVSS7.1HIGHCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L
3.1[email protected]Secondary7.1HIGHCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L
3.10b0ca135-0b70-47e7-9f44-1890c2a1c46cSecondary7.1HIGHCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L
3.1CNADECLARED7.1HIGHCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L

CVSS v3.1 Breakdown

Attack Vector
Network
Attack Complexity
Low
Privileges Required
Low
User Interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
None
Availability
Low

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L

NVD Known Affected Configurations (CPE 2.3)

TypeVendorProductVersionUpdateEditionLanguage
Application Vllm Vllm All All All All

Vendor Declared Affected Products

SourceVendorProductVersionPlatforms
CNA Vllm-project Vllm affected < 0.14.1 Not specified
ADP Red Hat Red Hat AI Inference Server 3.2 Not specified Not specified
ADP Red Hat Red Hat AI Inference Server 3.3 Not specified Not specified
ADP Red Hat Red Hat OpenShift AI 2.25 Not specified Not specified
ADP Red Hat Red Hat OpenShift AI 3.3 Not specified Not specified
ADP Red Hat Red Hat AI Inference Server Not specified Not specified
ADP Red Hat Red Hat Enterprise Linux AI RHEL AI 3 Not specified Not specified
ADP Red Hat Red Hat OpenShift AI RHOAI Not specified Not specified

References

ReferenceSourceLinkTags
access.redhat.com/errata/RHSA-2026:3462 0b0ca135-0b70-47e7-9f44-1890c2a1c46c access.redhat.com
access.redhat.com/errata/RHSA-2026:3782 0b0ca135-0b70-47e7-9f44-1890c2a1c46c access.redhat.com
bugzilla.redhat.com/show_bug.cgi 0b0ca135-0b70-47e7-9f44-1890c2a1c46c bugzilla.redhat.com
access.redhat.com/errata/RHSA-2026:10184 0b0ca135-0b70-47e7-9f44-1890c2a1c46c access.redhat.com
github.com/vllm-project/vllm/pull/32746 [email protected] github.com Issue Tracking, Patch
access.redhat.com/errata/RHSA-2026:30089 0b0ca135-0b70-47e7-9f44-1890c2a1c46c access.redhat.com
github.com/vllm-project/vllm/security/advisories/GHSA-qh4c-xf7m-gxfc [email protected] github.com Exploit, Patch, Vendor Advisory
access.redhat.com/errata/RHSA-2026:30087 0b0ca135-0b70-47e7-9f44-1890c2a1c46c access.redhat.com
access.redhat.com/errata/RHSA-2026:30088 0b0ca135-0b70-47e7-9f44-1890c2a1c46c access.redhat.com
github.com/vllm-project/vllm/commit/f46d576c54fb8aeec5fc70560e850bed38ef... [email protected] github.com Patch
security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-24779.json 0b0ca135-0b70-47e7-9f44-1890c2a1c46c security.access.redhat.com
access.redhat.com/errata/RHSA-2026:19712 0b0ca135-0b70-47e7-9f44-1890c2a1c46c access.redhat.com
access.redhat.com/security/cve/CVE-2026-24779 0b0ca135-0b70-47e7-9f44-1890c2a1c46c access.redhat.com
access.redhat.com/errata/RHSA-2026:3461 0b0ca135-0b70-47e7-9f44-1890c2a1c46c access.redhat.com
CVE Program record CVE.ORG www.cve.org canonical
NVD vulnerability detail NVD nvd.nist.gov canonical, analysis

Additional Advisory Data

SourceTimeEvent
ADP2026-01-27T23:00:53.998ZReported to Red Hat.
ADP2026-01-27T22:01:13.808ZMade public.

Solutions

ADP: RHSA-2026:3461: Red Hat AI Inference Server 3.2

ADP: RHSA-2026:3462: Red Hat AI Inference Server 3.2

ADP: RHSA-2026:30089: Red Hat AI Inference Server 3.3

ADP: RHSA-2026:30088: Red Hat AI Inference Server 3.3

ADP: RHSA-2026:30087: Red Hat AI Inference Server 3.3

ADP: RHSA-2026:10184: Red Hat OpenShift AI 2.25

ADP: RHSA-2026:3782: Red Hat OpenShift AI 2.25

ADP: RHSA-2026:19712: Red Hat OpenShift AI 3.3

Workarounds

ADP: To mitigate this issue, restrict network access to the vLLM service to only trusted clients. Implement strict network segmentation for vLLM pods in containerized environments to limit potential lateral movement. Ensure that vLLM instances are not exposed to untrusted external networks without proper access controls and input validation at the perimeter.

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