The vulnerability CVE-2026-24779 affects the vLLM project, an inference and serving engine for large language models, specifically versions prior to 0.14.1. The issue lies in the MediaConnector class, which provides methods load_from_url and load_from_url_async to fetch media from user-supplied URLs. These methods use two different Python parsing libraries to enforce hostname restrictions. However, these libraries interpret backslashes differently, allowing attackers to craft URLs that bypass hostname restrictions and cause the server to make arbitrary HTTP requests to internal network resources. This SSRF vulnerability enables attackers to interact with internal services that are otherwise inaccessible externally. In containerized environments such as llm-d, where vLLM runs in pods, exploitation could allow an attacker to scan internal networks, communicate with other pods, or send malicious requests to internal management endpoints. For example, sending false metrics to the llm-d management interface could destabilize the system or cause denial of service. The vulnerability has a CVSS 3.1 score of 7.1, indicating high severity due to network attack vector, low attack complexity, and high confidentiality impact, though integrity and availability impacts are limited. No authentication or user interaction is required, but some privileges on the vLLM instance are necessary (PR:L). The vulnerability was published on January 27, 2026, and fixed in version 0.14.1. No known exploits have been reported in the wild to date.

For European organizations deploying vLLM versions prior to 0.14.1, especially in containerized AI inference environments, this SSRF vulnerability poses significant risks. Attackers could leverage the flaw to access internal network resources that are not exposed externally, potentially extracting sensitive data or reconnaissance information. In multi-tenant or microservices architectures common in European cloud and enterprise environments, compromised vLLM pods could interact with other pods or services, leading to lateral movement or privilege escalation. The ability to send malicious requests to internal management endpoints could disrupt AI service availability, causing denial of service or system instability. Confidentiality is the primary concern, as internal data or metadata could be exposed. Given the increasing adoption of AI and container orchestration platforms in Europe, this vulnerability could impact sectors such as finance, healthcare, and government, where sensitive data and AI workloads coexist. The lack of known exploits reduces immediate risk but does not eliminate the threat, especially as attackers often target AI infrastructure. Organizations failing to patch or isolate vulnerable vLLM instances risk operational disruption and data breaches.

European organizations should immediately upgrade all vLLM deployments to version 0.14.1 or later to apply the official patch. For environments where immediate upgrade is not feasible, implement strict network segmentation and firewall rules to restrict outbound requests from vLLM pods to only trusted endpoints. Employ egress filtering at the container or orchestration platform level to prevent unauthorized internal network scanning or access. Monitor network traffic from vLLM instances for unusual or unexpected requests, especially to internal management endpoints. Review and harden internal management interfaces to require strong authentication and limit exposure. Conduct regular security audits of containerized AI workloads and apply runtime security tools to detect anomalous behavior. Additionally, validate and sanitize all user-supplied URLs before processing to prevent malformed inputs exploiting parsing inconsistencies. Finally, maintain an incident response plan tailored to AI infrastructure compromise scenarios.