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.
In binary-husky/gpt_academic version <= 3.83, the plugin `CodeInterpreter` is vulnerable to code injection caused by prompt injection. The root cause is the execution of user-provided prompts that generate untrusted code without a sandbox, allowing the execution of parts of the LLM-generated code. This vulnerability can be exploited by an attacker to achieve remote code execution (RCE) on the application backend server, potentially gaining full control of the server.
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.
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.