CVE-2024-10986
Binary-Husky Gpt Academic 3.83
Raw vector
CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-10986 is a high-severity Link Following (CWE-59) vulnerability in Binary-Husky Gpt Academic. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Path Interception (T1034); ranked in the top 48% 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 Privacy and Disclosure risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and AC-6 (Least Privilege) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7061
Vulnerability Data
GPT Academic version 3.83 is vulnerable to a Local File Read (LFI) vulnerability through its HotReload function. This function can download and extract tar.gz files from arxiv.org. Despite implementing protections against path traversal, the application overlooks the Tarslip triggered by…
more
symlinks. This oversight allows attackers to read arbitrary local files from the victim server.
- 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
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V15.4.2
Mitigating Controls (NIST 800-53 r5) AI
Proper enforcement of access authorizations on the resolved target resource stops a link from reaching an unintended object.
Least-privilege limits the damage an attacker can cause after following an unintended link.
Validating file-name inputs can reject or canonicalize names that resolve to links before access 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 SDLC practices directly require code to validate paths and avoid unsafe link following.
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.
Security testing can detect link-following flaws before release.
Secure SDLC practices can mandate link-resolution checks and canonicalization before file access.
Application security requirements can explicitly require safe handling of symbolic links and path traversal.
Secure architecture principles include input validation and safe file-access design patterns.
Secure coding standards directly address canonicalization and symlink attacks during implementation.
Access-control rules can limit which files are reachable, reducing exposure to malicious links.