Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-7456 is a critical-severity SQL Injection (CWE-89) vulnerability in Lunary Lunary. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 30% 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 LLM Application Platforms; in the Other ATLAS/OWASP Terms risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
A SQL injection vulnerability affects the /api/v1/external-users route in lunary-ai/lunary version 1.4.2. The flaw stems from an order-by clause that invokes sql.unsafe on an orderByClause variable built without server-side validation or sanitization, allowing arbitrary SQL commands to be executed.
An unauthenticated attacker with network access can supply a malicious order-by parameter to the endpoint and achieve full read, write, or destructive operations against the database, resulting in data exfiltration, modification, or complete loss.
The referenced commit 6a0bc201181e0f4a0cc375ccf4ef0d7ae65c8a8e in the lunary repository addresses the issue by removing the unsafe SQL construction; practitioners should apply the corresponding patch or upgrade to a fixed release. The associated huntr report documents the same root cause and remediation path.
The EPSS score has reached a peak of 0.2969 with a current value of 0.2925, indicating moderate and relatively stable exploitation interest since disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-48378
Vulnerability Data
A SQL injection vulnerability exists in the `/api/v1/external-users` route of lunary-ai/lunary version v1.4.2. The `order by` clause of the SQL query uses `sql.unsafe` without prior sanitization, allowing for SQL injection. The `orderByClause` variable is constructed without server-side validation or sanitization,…
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enabling an attacker to execute arbitrary SQL commands. Successful exploitation can lead to complete data loss, modification, or corruption.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application 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/AI applications, fitting 'Other Platforms' as it is an AI/ML-related platform not matching more specific categories like frameworks or libraries. The vulnerability is in its API, confirmed AI-related via AI/ML bug bounty context.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V6.2.5
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover SQLi flaws before deployment but does not stop their introduction.
Input validation directly stops untrusted data from reaching SQL query construction without neutralization.
Secure engineering principles require parameterized queries and input sanitization that structurally eliminate SQLi.
System monitoring can identify attempted SQLi exploitation via anomalous queries after the weakness exists.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly target injection flaws during coding and review so largely prevent CWE-89 introduction, yet the single broad outcome leaves residual risk from incomplete neutralization techniques or missed edge cases.
Training raises developer awareness of SQLi risks and can reduce introduction likelihood (partial) but removes none of the actual coding flaw's risk by itself since technical neutralization is still required.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
The same secure-coding and static-analysis activities surface missing neutralization of SQL metacharacters before the system is accepted.
Early warnings and shared best-practice information help organizations apply the latest remediation techniques against SQL-injection vulnerabilities.
Threat-intelligence feeds that surface new SQL-injection campaigns enable rapid updates to query-construction defenses and detection signatures before exploitation occurs.
Secure-coding rules and security testing phases mandate the use of parameterized queries or equivalent escaping, preventing the construction of dynamic SQL statements from untrusted input.
Language-specific secure coding rules, peer review and SAST together prevent the construction of SQL statements from untrusted data without proper parameterization or escaping.