vLLM MediaConnector SSRF via load_from_url
CVE-2025-6242 Published on October 7, 2025
Vllm: server side request forgery (ssrf) in mediaconnector
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 fetch and process media from user-provided URLs without adequate restrictions on the target hosts. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources.
Vulnerability Analysis
CVE-2025-6242 is exploitable with network access, and requires small amount of user privileges. This vulnerability is consided to have a high level of attack complexity. The potential impact of an exploit of this vulnerability is considered to have a high impact on confidentiality, with no impact on integrity, and a high impact on availability.
Timeline
Reported to Red Hat.
Made public. 111 days later.
Weakness Type
What is a SSRF Vulnerability?
The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination. By providing URLs to unexpected hosts or ports, attackers can make it appear that the server is sending the request, possibly bypassing access controls such as firewalls that prevent the attackers from accessing the URLs directly. The server can be used as a proxy to conduct port scanning of hosts in internal networks, use other URLs such as that can access documents on the system (using file://), or use other protocols such as gopher:// or tftp://, which may provide greater control over the contents of requests.
CVE-2025-6242 has been classified to as a SSRF vulnerability or weakness.
Products Associated with CVE-2025-6242
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Affected Versions
Red Hat AI Inference Server: Red Hat AI Inference Server: Red Hat AI Inference Server: Red Hat AI Inference Server: Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI): Red Hat Enterprise Linux AI (RHEL AI):Vulnerable Packages
The following package name and versions may be associated with CVE-2025-6242
| Package Manager | Vulnerable Package | Versions | Fixed In |
|---|---|---|---|
| pip | vllm | >= 0.5.0, < 0.11.0 | 0.11.0 |
Exploit Probability
EPSS (Exploit Prediction Scoring System) scores estimate the probability that a vulnerability will be exploited in the wild within the next 30 days. The percentile shows you how this score compares to all other vulnerabilities.