CVE-2024-10272 is a vulnerability classified under CWE-862 (Missing Authorization) affecting the lunary-ai/lunary software. The vulnerability arises because the /v1/datasets API endpoint does not enforce authorization checks, allowing any attacker to send a GET request and retrieve the contents of any dataset stored within the system. This means that an attacker does not need to authenticate or possess any valid token to access potentially sensitive or proprietary datasets. The vulnerability affects unspecified versions of lunary-ai/lunary, and no official patch has been linked yet. The CVSS 3.0 score of 7.5 indicates a high-severity issue, with an attack vector of network (AV:N), low attack complexity (AC:L), no privileges required (PR:N), and no user interaction needed (UI:N). The impact is primarily on confidentiality (C:H), with no impact on integrity or availability. This vulnerability could lead to unauthorized data disclosure, potentially exposing sensitive business or personal information. Although there are no known exploits in the wild, the simplicity of exploitation and the critical nature of the data involved make this a serious threat. Organizations using lunary-ai/lunary for AI or data analytics workloads should urgently assess exposure and implement access controls or network restrictions to mitigate risk.

For European organizations, the primary impact is unauthorized disclosure of sensitive datasets managed by lunary-ai/lunary. This could include intellectual property, personal data subject to GDPR, or confidential business information. Exposure of such data can lead to regulatory penalties, loss of competitive advantage, reputational damage, and potential legal liabilities. Since the vulnerability requires no authentication and can be exploited remotely, attackers can easily access data from anywhere, increasing the risk of widespread data breaches. Organizations in sectors such as finance, healthcare, research, and government that rely on lunary-ai/lunary for data processing are particularly vulnerable. The breach of confidentiality could also undermine trust in AI-driven services and data platforms. Additionally, the lack of integrity or availability impact means the system remains operational but compromised in terms of data privacy. The absence of known exploits currently provides a window for proactive mitigation before active attacks emerge.

1. Immediately implement strict authorization checks on the /v1/datasets endpoint to ensure only authenticated and authorized users can access datasets. 2. If a patch is not yet available, deploy network-level access controls such as IP whitelisting or VPN requirements to restrict access to the API endpoint. 3. Conduct a thorough audit of dataset access logs to detect any unauthorized access attempts. 4. Employ API gateways or web application firewalls (WAFs) with rules to block unauthenticated requests to sensitive endpoints. 5. Review and enforce role-based access control (RBAC) policies within lunary-ai/lunary configurations. 6. Educate development and security teams about secure API design principles to prevent missing authorization flaws. 7. Monitor vendor communications for patches or updates and apply them promptly once available. 8. Consider data encryption at rest and in transit to add an additional layer of protection. 9. Perform regular penetration testing focused on access control mechanisms to detect similar issues early.