CVE-2024-6862
Published: 13 September 2024
Summary
CVE-2024-6862 is a high-severity CSRF (CWE-352) vulnerability in Lunary Lunary. Its CVSS base score is 8.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked in the top 43.5% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Other Platforms; in the Other ATLAS/OWASP Terms risk domain; MITRE ATLAS techniques in scope: AI Model Inference API Access (AML.T0040), External Harms (AML.T0048).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-2876
Vulnerability details
A Cross-Site Request Forgery (CSRF) vulnerability exists in lunary-ai/lunary version 1.2.34 due to overly permissive CORS settings. This vulnerability allows an attacker to sign up for and create projects or use the instance as if they were a user with…
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local access. The main attack vector is for instances hosted locally on personal machines, which are not publicly accessible. The CORS settings in the backend permit all origins, exposing unauthenticated endpoints to CSRF attacks.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Lunary.ai is an open-source observability platform for LLM applications, fitting 'Other Platforms' as it provides monitoring, tracing, and evaluation tools for AI/LLM deployments, not matching narrower categories like frameworks or libraries.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
CSRF due to permissive CORS enables unauthorized signup (local account creation, T1136.001) and usage as local user via exploitation for privilege escalation (T1068).
MITRE ATLAS TechniquesAI
MITRE ATLAS techniques
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Awareness training educates users on avoiding untrusted links and actions that can be exploited via CSRF.
Requiring user re-entry of credentials for sensitive actions prevents automated forgery of requests without active user participation.
Security testing regimens explicitly include checks for missing or ineffective anti-CSRF protections in web applications.
Detects anomalous request patterns consistent with cross-site request forgery.