Vulnerabilities

GPT Academic upload Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of GPT Academic. Authentication is not required to exploit this vulnerability. The specific flaw exists within the upload endpoint. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of root. Was ZDI-CAN-27957.

A vulnerability has been found in binary-husky gpt_academic up to 3.91. Impacted is the function merge_tex_files_ of the file crazy_functions/latex_fns/latex_toolbox.py of the component LaTeX File Handler. Such manipulation of the argument \input{} leads to path traversal. The attack may be launched remotely. The exploit has been disclosed to the public and may be used. The vendor was contacted early about this disclosure but did not respond in any way.

A pickle deserialization vulnerability exists in the Latex English error correction plug-in function of binary-husky/gpt_academic versions up to and including 3.83. This vulnerability allows attackers to achieve remote command execution by deserializing untrusted data. The issue arises from the inclusion of numpy in the deserialization whitelist, which can be exploited by constructing a malicious compressed package containing a merge_result.pkl file and a merge_proofread_en.tex file. The vulnerability is fixed in commit 91f5e6b.

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 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.

A path traversal vulnerability exists in binary-husky/gpt_academic version 3.83. The vulnerability is due to improper handling of the file parameter, which is open to path traversal through URL encoding. This allows attackers to view any file on the host system, including sensitive files such as critical application files, SSH keys, API keys, and configuration values.

GPT Academic provides interactive interfaces for large language models. A vulnerability was found in gpt_academic versions 3.64 through 3.73. The server deserializes untrustworthy data from the client, which may risk remote code execution. Any device that exposes the GPT Academic service to the Internet is vulnerable. Version 3.74 contains a patch for the issue. There are no known workarounds aside from upgrading to a patched version.

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.