binary-husky CVE Vulnerabilities & Metrics

A stored cross-site scripting (XSS) vulnerability exists in the Latex Proof-Reading Module of binary-husky/gpt_academic version 3.9.0. This vulnerability allows an attacker to inject malicious scripts into the `debug_log.html` file generated by the module. When an admin visits this debug report, the injected scripts can execute, potentially leading to unauthorized actions and data access.

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.

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 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 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 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 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 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.

A path traversal vulnerability exists in binary-husky/gpt_academic at commit 679352d, which allows an attacker to bypass the blocked_paths protection and read the config.py file containing sensitive information such as the OpenAI API key. This vulnerability is exploitable on Windows operating systems by accessing a specific URL that includes the absolute path of the project.

A Denial of Service (DoS) vulnerability exists in the file upload feature of binary-husky/gpt_academic version 3.83. The vulnerability is due to improper handling of form-data with a large filename in the file upload request. An attacker can exploit this vulnerability by sending a payload with an excessively large filename, causing the server to become overwhelmed and unavailable for legitimate users.

In version 3.83 of binary-husky/gpt_academic, a Server-Side Request Forgery (SSRF) vulnerability exists in the Markdown_Translate.get_files_from_everything() API. This vulnerability is exploited through the HotReload(Markdown翻译中) plugin function, which allows downloading arbitrary web hosts by only checking if the link starts with 'http'. Attackers can exploit this vulnerability to abuse the victim GPT Academic's Gradio Web server's credentials to access unauthorized web resources.

GPT Academic version 3.83 is vulnerable to a Server-Side Request Forgery (SSRF) vulnerability through its HotReload plugin function, which calls the crazy_utils.get_files_from_everything() API without proper sanitization. This allows attackers to exploit the vulnerability to abuse the victim GPT Academic's Gradio Web server's credentials to access unauthorized web resources.

GPT Academic version 3.83 is vulnerable to a Local File Read (LFI) vulnerability through its HotReload function. This function can download and extract tar.gz files from arxiv.org. Despite implementing protections against path traversal, the application overlooks the Tarslip triggered by symlinks. This oversight allows attackers to read arbitrary local files from the victim server.

GPT Academy version 3.83 in the binary-husky/gpt_academic repository is vulnerable to Cross-Site WebSocket Hijacking (CSWSH). This vulnerability allows an attacker to hijack an existing WebSocket connection between the victim's browser and the server, enabling unauthorized actions such as deleting conversation history without the victim's consent. The issue arises due to insufficient WebSocket authentication and lack of origin validation.

In the `manim` plugin of binary-husky/gpt_academic, versions prior to the fix, a vulnerability exists due to improper handling of user-provided prompts. The root cause is the execution of untrusted code generated by the LLM without a proper sandbox. This allows an attacker to perform remote code execution (RCE) on the app backend server by injecting malicious code through the prompt.

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.

A vulnerability in the upload function of binary-husky/gpt_academic allows any user to read arbitrary files on the system, including sensitive files such as `config.py`. This issue affects the latest version of the product. An attacker can exploit this vulnerability by intercepting the websocket request during file upload and replacing the file path with the path of the file they wish to read. The server then copies the file to the `private_upload` folder and provides the path to the copied file, which can be accessed via a GET request. This vulnerability can lead to the exposure of sensitive system files, potentially including credentials, configuration files, or sensitive user data.

A Cross-Site Request Forgery (CSRF) vulnerability in version 3.83 of binary-husky/gpt_academic allows an attacker to trick a user into uploading files without their consent, exploiting their session. This can lead to unauthorized file uploads and potential system compromise. The uploaded file can contain malicious scripts, leading to stored Cross-Site Scripting (XSS) attacks. Through stored XSS, an attacker can steal information about the victim and perform any action on their behalf.

An open redirect vulnerability exists in binary-husky/gpt_academic version 3.83. The vulnerability occurs when a user is redirected to a URL specified by user-controlled input in the 'file' parameter without proper validation or sanitization. This can be exploited by attackers to conduct phishing attacks, distribute malware, and steal user credentials.

A vulnerability in binary-husky/gpt_academic version 3.83 allows an attacker to cause a Denial of Service (DoS) by adding excessive characters to the end of a multipart boundary during file upload. This results in the server continuously processing each character and displaying warnings, rendering the application inaccessible. The issue occurs when the terminal shows a warning: 'multipart.multipart Consuming a byte '0x2d' in end state'.

A stored cross-site scripting (XSS) vulnerability exists in binary-husky/gpt_academic version 3.83. The vulnerability occurs at the /file endpoint, which renders HTML files. Malicious HTML files containing XSS payloads can be uploaded and stored in the backend, leading to the execution of the payload in the victim's browser when the file is accessed. This can result in the theft of session cookies or other sensitive information.

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 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.