CVE-2024-12392
Published: 20 March 2025
Summary
CVE-2024-12392 is a medium-severity SSRF (CWE-918) vulnerability in Binary-Husky Gpt Academic. Its CVSS base score is 6.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 48.4% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7017
Vulnerability details
A Server-Side Request Forgery (SSRF) vulnerability exists in binary-husky/gpt_academic version git 310122f. The application has a functionality to download papers from arxiv.org, but the URL validation is incomplete. An attacker can exploit this vulnerability to make the application access any…
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URL, including internal services, and read the response. This can be used to access data that are only accessible from the server, such as AWS metadata credentials, and can escalate local exploits to network-based attacks.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
SSRF enables exploitation of public-facing application (T1190) and facilitates access to internal cloud instance metadata API for discovery (T1522) and unsecured credential retrieval (T1552.005), such as AWS metadata credentials.
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.
Penetration testing attempts server-side requests to internal resources, identifying SSRF weaknesses for remediation.
Outbound connections to external resources can be monitored and limited at the boundary, reducing SSRF impact.
Validates server-side URLs and resource references to block SSRF attempts.
Detects server-side request forgery through monitoring of unexpected outbound connections.