CVE-2025-25185
Binary-Husky Gpt Academic
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2025-25185 is a high-severity Link Following (CWE-59) vulnerability in Binary-Husky Gpt Academic. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Path Interception (T1034); 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-3 (Access Enforcement) and AC-6 (Least Privilege) — 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-2025-25185 is a file access vulnerability affecting GPT Academic, an open-source tool that provides interactive interfaces for large language models, in versions 3.91 and earlier. The issue arises because the application fails to properly account for soft links (symlinks) during handling of uploaded tar.gz archives. Classified under CWE-59 (Improper Link Resolution Before File Access), it 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), indicating high confidentiality impact from network-accessible exploitation with low complexity and no privileges required.
An unauthenticated attacker can exploit this vulnerability remotely by crafting a tar.gz file containing a malicious soft link that points to a target file on the victim server. After uploading the archive, the server decompresses it, and subsequent access to the symlink resolves to the targeted server file, enabling arbitrary file reads across the entire filesystem.
The GitHub security advisory (GHSA-gqp5-wm97-qxcv) and a related commit (5dffe8627f681d7006cebcba27def038bb691949) in the binary-husky/gpt_academic repository address the issue, with the commit likely implementing the fix for symlink handling during archive processing.
As GPT Academic supports interactive access to large language models, this vulnerability holds relevance for AI/ML environments where such interfaces are deployed, potentially exposing sensitive model data or configurations. No public evidence of real-world exploitation is noted in available details.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-6017
Vulnerability Data
GPT Academic provides interactive interfaces for large language models. In 3.91 and earlier, GPT Academic does not properly account for soft links. An attacker can create a malicious file as a soft link pointing to a target file, then package…
more
this soft link file into a tar.gz file and upload it. Subsequently, when accessing the decompressed file from the server, the soft link will point to the target file on the victim server. The vulnerability allows attackers to read all files on the 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
—
—
—
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.