Security Vulnerabilities
- CVEs Published In March 2025
A Cross-Site WebSocket Hijacking (CSWSH) vulnerability in automatic1111/stable-diffusion-webui version 1.10.0 allows an attacker to clone a malicious server extension from a GitHub repository. The vulnerability arises from the lack of proper validation on WebSocket connections at ws://127.0.0.1:7860/queue/join, enabling unauthorized actions on the server. This can lead to unauthorized cloning of server extensions, execution of malicious scripts, data exfiltration, and potential denial of service (DoS).
An Insecure Direct Object Reference (IDOR) vulnerability exists in the `PATCH /v1/runs/:id/score` endpoint of lunary-ai/lunary version 1.6.0. This vulnerability allows an attacker to update the score data of any run by manipulating the id parameter in the request URL, which corresponds to the `runId_score` in the database. The endpoint does not sufficiently validate whether the authenticated user has permission to modify the specified runId, enabling an attacker with a valid account to modify other users' runId scores by specifying different id values. This issue was fixed in version 1.6.1.
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.
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.
A Regular Expression Denial of Service (ReDoS) vulnerability exists in gaizhenbiao/chuanhuchatgpt, as of commit 20b2e02. The server uses the regex pattern `r'<[^>]+>'` to parse user input. In Python's default regex engine, this pattern can take polynomial time to match certain crafted inputs. An attacker can exploit this by uploading a malicious JSON payload, causing the server to consume 100% CPU for an extended period. This can lead to a Denial of Service (DoS) condition, potentially affecting the entire 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.
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 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.
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.
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.