CVE-2026-24779 is a high-severity Server-Side Request Forgery (SSRF) vulnerability identified in the vLLM project, an inference and serving engine for large language models. The vulnerability resides in the MediaConnector class, specifically in the load_from_url and load_from_url_async methods that fetch and process media from user-supplied URLs. These methods attempt to restrict requests to allowed hosts by parsing URLs using two different Python libraries, each interpreting backslashes differently. This discrepancy allows attackers to craft URLs that bypass hostname restrictions, coercing the vLLM server into making arbitrary HTTP requests to internal network resources. In containerized environments such as llm-d, where vLLM pods operate, this can be exploited to scan internal networks, interact with other pods, or send malicious requests to internal management endpoints. For example, attackers might send falsified metrics to destabilize the system or cause denial of service. The vulnerability requires low privileges (PR:L) but no user interaction, and the attack vector is network-based, making exploitation feasible remotely. The impact primarily affects confidentiality by exposing internal resources, with limited availability impact. The vendor patched the vulnerability in version 0.14.1 by addressing the inconsistent URL parsing and enforcing proper hostname restrictions. No public exploits have been reported yet, but the nature of SSRF in containerized AI serving environments makes this a significant risk.

For European organizations deploying vLLM versions prior to 0.14.1, especially within containerized or cloud-native AI infrastructures, this SSRF vulnerability poses a substantial risk. Attackers could leverage it to pivot within internal networks, accessing sensitive internal services or data not intended to be exposed externally. This could lead to unauthorized data disclosure, internal reconnaissance, or denial of service conditions affecting AI service availability. Given the increasing adoption of AI inference engines in sectors like finance, healthcare, and critical infrastructure across Europe, exploitation could disrupt essential services or compromise confidential information. Container orchestration platforms commonly used in Europe, such as Kubernetes, often run pods with network segmentation assumptions that SSRF can undermine. The vulnerability's ability to bypass hostname restrictions means traditional network controls might be insufficient. Although no known exploits exist yet, the high severity and ease of exploitation warrant immediate attention to prevent potential lateral movement and internal attacks within European enterprise environments.

European organizations should immediately upgrade all vLLM deployments to version 0.14.1 or later to apply the official patch. Until upgrades are completed, implement strict network egress filtering at the container and host levels to restrict outbound HTTP requests from vLLM pods to only trusted external endpoints. Employ runtime security tools to monitor and alert on unusual outbound network activity from AI inference containers. Review and harden internal service authentication and authorization to mitigate risks if SSRF is exploited to reach internal endpoints. Consider deploying web application firewalls (WAFs) or API gateways that can detect and block SSRF patterns in incoming requests. Conduct thorough security assessments of containerized AI environments, focusing on inter-pod communication and internal API exposure. Finally, establish incident response procedures to quickly isolate affected pods and networks if suspicious activity is detected.