CVE-2025-6242 is a Server-Side Request Forgery (SSRF) vulnerability identified in the MediaConnector class of the vLLM project's multimodal feature set within the Red Hat AI Inference Server. The vulnerability arises from the load_from_url and load_from_url_async methods, which fetch and process media content from user-supplied URLs without enforcing adequate restrictions on the destination hosts. This lack of validation enables an attacker to coerce the server into initiating arbitrary HTTP requests to internal or otherwise protected network resources that the attacker cannot access directly. SSRF vulnerabilities are particularly dangerous because they can be used to bypass network access controls, potentially exposing sensitive internal services, metadata endpoints, or administrative interfaces. In this case, the vulnerability requires only low privileges on the server and does not require user interaction, increasing the risk of automated exploitation. The CVSS 3.1 score of 7.1 reflects a high severity, with a network attack vector, high confidentiality impact, low integrity impact, and high availability impact. The vulnerability was reserved in June 2025 and published in October 2025, with no known exploits in the wild at the time of publication. The absence of affected versions listed suggests that the vulnerability may affect multiple or all versions of the Red Hat AI Inference Server that include the vulnerable vLLM multimodal feature. Given the increasing adoption of AI inference servers in enterprise environments, this vulnerability poses a significant risk if left unmitigated.

For European organizations, the impact of CVE-2025-6242 can be substantial. Exploitation could allow attackers to access internal network resources that are otherwise inaccessible from the internet, potentially exposing sensitive data, internal APIs, or administrative interfaces. This could lead to unauthorized data disclosure (high confidentiality impact), partial data manipulation or service disruption (low integrity impact), and denial of service or degraded performance of AI inference services (high availability impact). Organizations relying on Red Hat AI Inference Server for critical AI workloads, especially those processing sensitive or regulated data, face increased risk of compliance violations and operational disruption. The ability to pivot into internal networks via SSRF can also facilitate further lateral movement and escalation of privileges, compounding the threat. Given the growing importance of AI infrastructure in sectors such as finance, healthcare, and manufacturing across Europe, the vulnerability could have cascading effects on business continuity and data protection obligations under regulations like GDPR.

To mitigate CVE-2025-6242, European organizations should implement the following specific measures: 1) Apply vendor-provided patches or updates for the Red Hat AI Inference Server as soon as they become available to address the SSRF vulnerability directly. 2) Implement strict network segmentation and firewall rules to restrict the AI server's outbound HTTP requests, limiting them to only trusted external endpoints and blocking access to internal IP ranges and metadata services. 3) Employ input validation and URL allowlisting at the application level to prevent the server from processing untrusted or arbitrary URLs. 4) Monitor network traffic originating from the AI inference server for unusual or unauthorized internal requests indicative of SSRF exploitation attempts. 5) Conduct regular security assessments and penetration tests focusing on SSRF and related vulnerabilities in AI infrastructure components. 6) Restrict user privileges on the AI server to the minimum necessary to reduce the risk of low-privilege exploitation. 7) Maintain up-to-date inventory and visibility of AI infrastructure components to rapidly respond to emerging threats. These measures go beyond generic advice by focusing on network-level controls and application-specific input restrictions tailored to the AI inference server environment.