A vulnerability in the binary-husky/gpt_academic repository, as of commit git 3890467, allows an attacker to crash the server by uploading a specially crafted zip bomb. The server decompresses the uploaded file and attempts to load it into memory, which can lead to an out-of-memory crash. This issue arises due to improper input validation when handling compressed file uploads.
A vulnerability in binary-husky/gpt_academic version 310122f allows for a Regular Expression Denial of Service (ReDoS) attack. The application uses a regular expression to parse user input, which can take polynomial time to match certain crafted inputs. This allows an attacker to send a small malicious payload to the server, causing it to become unresponsive and unable to handle any requests from other users.
A path traversal vulnerability exists in binary-husky/gpt_academic version git 310122f. The application supports the extraction of user-provided 7z files without proper validation. The Python py7zr package used for extraction does not guarantee that files will remain within the intended extraction directory. An attacker can exploit this vulnerability to perform arbitrary file writes, which can lead to remote code execution.
A vulnerability in binary-husky/gpt_academic version git 310122f allows for remote code execution. The application supports the extraction of user-provided RAR files without proper validation. The Python rarfile module, which supports symlinks, can be exploited to perform arbitrary file writes. This can lead to remote code execution by writing to sensitive files such as SSH keys, crontab files, or the application's own code.
A vulnerability in binary-husky/gpt_academic, as of commit 310122f, allows for a Regular Expression Denial of Service (ReDoS) attack. The function '解析项目源码(手动指定和筛选源码文件类型)' permits the execution of user-provided regular expressions. Certain regular expressions can cause the Python RE engine to take exponential time to execute, leading to a Denial of Service (DoS) condition. An attacker who controls both the regular expression and the search string can exploit this vulnerability to hang the server for an arbitrary amount of time.
A Server-Side Request Forgery (SSRF) vulnerability exists in binary-husky/gpt_academic version git 310122f. The application has a functionality to download papers from arxiv.org, but the URL validation is incomplete. An attacker can exploit this vulnerability to make the application access any URL, including internal services, and read the response. This can be used to access data that are only accessible from the server, such as AWS metadata credentials, and can escalate local exploits to network-based attacks.
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.