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Not in CISA KEV
HIGH severity

CVE-2026-24779 — vllm

HIGHFix: vllm-project/vllm@f46d576

CVE-2026-24779 is a high-severity (CVSS 7.1) Server-Side Request Forgery (SSRF) vulnerability in vllm. A fix is available for vllm — see the affected versions and patch details below.

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

Also known asGHSA-qh4c-xf7m-gxfcPYSEC-2026-2020
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Nov 8, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

Proof-of-concept exploit code exists

  • CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.

Exploitation and automatability from CISA’s SSVC triage for CVE-2026-24779.

EPSS Exploitation Probability

via FIRST.org ↗
0.6%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs48th percentile — riskier than 48% of all scored CVEsHighest risk
0.00%0.36%0.73%1.09%0.0%0.6%May 26Sep 26Nov 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

Where this sits among everything scored

Real-World Exposure

1 pkg affected
🐍vllm

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Summary

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.

Details

The core of the vulnerability lies in the MediaConnector.load_from_url method and its asynchronous counterpart. These methods accept a URL string to fetch media content (images, audio, video).

def load_from_url(
    self,
    url: str,
    media_io: MediaIO[_M],
    *,
    fetch_timeout: int | None = None,
) -> _M:  # type: ignore[type-var]
    url_spec = urlparse(url)

    if url_spec.scheme.startswith("http"):
        self._assert_url_in_allowed_media_domains(url_spec)

        connection = self.connection
        data = connection.get_bytes(
            url,
            timeout=fetch_timeout,
            allow_redirects=envs.VLLM_MEDIA_URL_ALLOW_REDIRECTS,
        )

        return media_io.load_bytes(data)

The URL validation uses the urlparse function from Python's urllib module, while the request is made using the request function from Python's requests module. The requests module's underlying URL parsing is implemented using the parse_url function from Python's urllib3. These two parsing functions follow different URL specifications; one is implemented according to the RFC 3986 specification, and the other is implemented according to the WHATWG Living Standard. There is a difference in how the two functions handle backslashes (\) in URLs, which allows the hostname restriction to be bypassed.

Fix

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIvllmall versions0.14.1pip install --upgrade 'vllm==0.14.1'

Affected Products

1 product · 1 configurations
Application
vllmvllm
< 0.14.1
range

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for vllm, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update vllm to 0.14.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2026-24779 is resolved across your whole dependency graph.

  3. Workarounds

    Restrict outbound requests from the affected component to an allowlist of hosts, block access to link-local and internal address ranges at the network layer, and require authentication on internal services so a forged request cannot reach them unauthenticated.

Fixing This On Your OS

If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.

Red HatImportant

This IMPORTANT Server-Side Request Forgery (SSRF) vulnerability in vLLM's `MediaConnector` allows an attacker to bypass host restrictions when processing user-provided URLs. This enables the vLLM server to be coerced into making arbitrary requests to internal network resources. This is critical in containerized…

Workaround published by Red Hat
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.
Source: Red Hat security advisory for CVE-2026-24779 (CC BY 4.0)
ProductFixed inAdvisory
Red Hat AI Inference Server 3.2rhaiis/vllm-cuda-rhel9:1772160593RHSA-2026:3461
Red Hat AI Inference Server 3.2rhaiis/vllm-rocm-rhel9:1772160625RHSA-2026:3462
Red Hat AI Inference Server 3.3rhaiis/vllm-spyre-rhel9:1782352919RHSA-2026:30087
Red Hat AI Inference Server 3.3rhaiis/vllm-rocm-rhel9:1782353093RHSA-2026:30088
Red Hat AI Inference Server 3.3rhaiis/vllm-cuda-rhel9:1782352847RHSA-2026:30089
Red Hat OpenShift AI 2.25rhoai/odh-vllm-cpu-rhel9:1776259063RHSA-2026:10184
Red Hat OpenShift AI 2.25rhoai/odh-vllm-gaudi-rhel9:1772093278RHSA-2026:3782
Red Hat OpenShift AI 2.25rhoai/odh-vllm-cuda-rhel9:1783998774RHSA-2026:42644

Frequently Asked Questions

### Summary 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 partic
O3 Security · Impact-Aware SCA

Is CVE-2026-24779 in your dependencies?

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CVE-2026-24779: vllm SSRF — Fixed in 0.14.1