CVE-2024-11030
SSRF in Binary-Husky Gpt Academic 3.83
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2024-11030 is a high-severity SSRF (CWE-918) vulnerability in Binary-Husky Gpt Academic. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 46th 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 LLM Application Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) 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.
GPT Academic version 3.83 is affected by CVE-2024-11030, a Server-Side Request Forgery (SSRF) vulnerability classified under CWE-918. The issue resides in the HotReload plugin function, which invokes the crazy_utils.get_files_from_everything() API without adequate input sanitization. This flaw carries a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N), highlighting its potential for high confidentiality impact from remote exploitation.
Unauthenticated remote attackers can exploit this vulnerability over the network with low attack complexity and no user interaction required. By crafting malicious requests to the HotReload function, attackers can force the server to make unauthorized outbound requests, abusing the credentials of the victim's GPT Academic Gradio Web server to access restricted web resources.
The primary advisory is available via the Huntr bounty report at https://huntr.com/bounties/729d9928-c28a-40fd-8a86-bb4ca2984bba, which details the vulnerability discovery but does not specify patches or mitigations in the provided information. Security practitioners should review the full report for any recommended remediation steps.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7055
Vulnerability Data
GPT Academic version 3.83 is vulnerable to a Server-Side Request Forgery (SSRF) vulnerability through its HotReload plugin function, which calls the crazy_utils.get_files_from_everything() API without proper sanitization. This allows attackers to exploit the vulnerability to abuse the victim GPT Academic's Gradio…
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Web server's credentials to access unauthorized web resources.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: gpt, gradio
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
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 development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
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.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.