CVE-2024-11031
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-2024-11031 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 Privacy and Disclosure 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.
CVE-2024-11031 is a Server-Side Request Forgery (SSRF) vulnerability, classified as CWE-918, affecting version 3.83 of binary-husky/gpt_academic. The flaw exists in the Markdown_Translate.get_files_from_everything() API and is exploited through the HotReload(Markdown翻译中) plugin function, which performs insufficient validation by only checking if provided links start with 'http'. This allows the plugin to download content from arbitrary web hosts via the affected GPT Academic Gradio Web server.
Attackers require only network access with no authentication or user interaction, as indicated by the CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N). Exploitation occurs when victims use the plugin with a malicious link, enabling attackers to leverage the Gradio Web server's credentials to access unauthorized web resources and potentially exfiltrate sensitive data.
Mitigation details are available in the Huntr advisory at https://huntr.com/bounties/d27d89a7-7d54-45b9-a9eb-66c00bc56e02.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7071
Vulnerability Data
In version 3.83 of binary-husky/gpt_academic, a Server-Side Request Forgery (SSRF) vulnerability exists in the Markdown_Translate.get_files_from_everything() API. This vulnerability is exploited through the HotReload(Markdown翻译中) plugin function, which allows downloading arbitrary web hosts by only checking if the link starts with 'http'.…
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Attackers can exploit this vulnerability to abuse the victim GPT Academic's Gradio Web server's credentials to access unauthorized web resources.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Privacy and Disclosure
- 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.