CVE-2024-7472
Published: 29 October 2024
Summary
CVE-2024-7472 is a medium-severity CRLF Injection (CWE-93) vulnerability in Lunary Lunary. Its CVSS base score is 6.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 33.4th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Enterprise AI Assistants; in the Other ATLAS/OWASP Terms risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-48392
Vulnerability details
lunary-ai/lunary v1.2.26 contains an email injection vulnerability in the Send email verification API (/v1/users/send-verification) and Sign up API (/auth/signup). An unauthenticated attacker can inject data into outgoing emails by bypassing the extractFirstName function using a different whitespace character (e.g., \xa0).…
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This vulnerability can be exploited to conduct phishing attacks, damage the application's brand, cause legal and compliance issues, and result in financial impact due to unauthorized email usage.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Enterprise AI Assistants
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Lunary.ai is an open-source LLM observability and management platform used for monitoring and improving AI/LLM applications in enterprise settings, fitting the Enterprise AI Assistants category.
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Unauthenticated exploitation of public-facing API (T1190) enables email injection for phishing attacks via the application's email service (T1566.003).
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.
Developer assessments and testing (including injection-focused techniques) identify improper neutralization of special elements, and the verifiable flaw remediation corrects them pre-deployment.
Identifies indicators of injection attacks (command, SQL, LDAP, etc.) via anomaly and attack monitoring.