gpt_academic provides a graphical interface for ChatGPT/GLM. A vulnerability was found in gpt_academic 3.37 and prior. This issue affects some unknown processing of the component Configuration File Handler. The manipulation of the argument file leads to information disclosure. Since no sensitive files are configured to be off-limits, sensitive information files in some working directories can be read through the `/file` route, leading to sensitive information leakage. This affects users that uses file configurations via `config.py`, `config_private.py`, `Dockerfile`. A patch is available at commit 1dcc2873d2168ad2d3d70afcb453ac1695fbdf02. As a workaround, one may use environment variables instead of `config*.py` files to configure this project, or use docker-compose installation to configure this project.
This vulnerability carries a MEDIUM severity rating with a CVSS v3.1 score of 6.5, indicating it can be exploited remotely over the network with relatively low complexity without requiring user interaction requiring only low-level privileges . The vulnerability impacts confidentiality (data exposure), for affected systems. Impacting 1 product from binary-husky organizations running these solutions should prioritize assessment and patching.
Reported in 2023, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2023-05-31T19:15:27.163
2025-03-07T15:30:57.390
Modified
CVSSv3.1: 6.5 (MEDIUM)
| Type | Vendor | Product | Version/Range | Vulnerable? |
|---|---|---|---|---|
| Application | binary-husky | gpt_academic | < 3.37 | Yes |
SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For binary-husky's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.