CVE-2024-7048 identifies an improper authorization vulnerability (CWE-863) in open-webui version v0.3.8, specifically affecting the API endpoints GET /api/v1/documents/ and POST /rag/api/v1/doc. This vulnerability arises from insufficient privilege checks, allowing users with lower privileges to access metadata of files uploaded by administrators and to overwrite these files. The affected files are related to Retrieval-Augmented Generation (RAG) models, which are critical for AI-driven document retrieval and processing. By exploiting this flaw, an attacker compromises the integrity and availability of these models, potentially leading to corrupted data, denial of service, or manipulation of AI outputs. The vulnerability requires network access and low privileges but does not require user interaction, making it easier to exploit remotely. The CVSS 3.0 base score is 6.3 (medium severity), reflecting the moderate confidentiality, integrity, and availability impacts combined with low attack complexity and no user interaction. No patches or mitigations have been officially released yet, and no known exploits are reported in the wild. The vulnerability highlights a critical gap in access control mechanisms within open-webui's API design, emphasizing the need for robust authorization enforcement in AI model management platforms.

For European organizations leveraging open-webui for AI document management or RAG model deployment, this vulnerability poses a significant risk to data integrity and service availability. Attackers exploiting this flaw can overwrite critical AI model files, potentially causing incorrect AI outputs, service disruptions, or data loss. This can undermine trust in AI-driven processes and lead to operational downtime. Confidentiality impact is limited to metadata exposure, but integrity and availability impacts are more severe. Organizations in sectors relying heavily on AI for decision-making, such as finance, healthcare, and research institutions, may face operational and reputational damage. Additionally, the ease of exploitation without user interaction increases the risk of automated attacks. The absence of patches means organizations must rely on compensating controls until a fix is available. Given the growing adoption of AI tools in Europe, the vulnerability could affect a broad range of entities, especially those using open-source AI frameworks.

1. Immediately restrict access to the vulnerable API endpoints (GET /api/v1/documents/ and POST /rag/api/v1/doc) to trusted and authenticated users only, preferably via network segmentation or firewall rules. 2. Implement strict role-based access control (RBAC) and verify that authorization checks are enforced server-side for all API requests, ensuring that lower-privileged users cannot access or modify admin-managed files. 3. Monitor API usage logs for unusual access patterns or unauthorized attempts to access or overwrite files. 4. If possible, disable or isolate the RAG model management features until a patch is available. 5. Engage with the open-webui community or vendor to track patch releases and apply updates promptly once available. 6. Conduct internal audits of AI model file permissions and integrity to detect any unauthorized changes. 7. Consider deploying Web Application Firewalls (WAF) with custom rules to detect and block suspicious API calls targeting these endpoints. 8. Educate administrators and developers about the importance of secure API design and privilege separation in AI platforms.