A vulnerability in aimhubio/aim version 3.25.0 allows for a denial of service (DoS) attack. The issue arises when a large number of tracked metrics are retrieved simultaneously from the Aim web API, causing the web server to become unresponsive. The root cause is the lack of a limit on the number of metrics that can be requested per call, combined with the server's single-threaded nature, leading to excessive resource consumption and blocking of the server.
A Denial of Service (DoS) vulnerability exists in the brycedrennan/imaginairy repository, version 15.0.0. The vulnerability is present in the `/api/stablestudio/generate` endpoint, which can be exploited by sending an invalid request. This causes the server process to terminate abruptly, outputting `KILLED` in the terminal, and results in the unavailability of the server. This issue disrupts the server's functionality, affecting all users.
A vulnerability in the LangChainLLM class of the run-llama/llama_index repository, version v0.12.5, allows for a Denial of Service (DoS) attack. The stream_complete method executes the llm using a thread and retrieves the result via the get_response_gen method of the StreamingGeneratorCallbackHandler class. If the thread terminates abnormally before the _llm.predict is executed, there is no exception handling for this case, leading to an infinite loop in the get_response_gen function. This can be triggered by providing an input of an incorrect type, causing the thread to terminate and the process to continue running indefinitely.
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 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 local file inclusion vulnerability was identified in automatic1111/stable-diffusion-webui, affecting version git 82a973c. This vulnerability allows an attacker to read arbitrary files on the system by sending a specially crafted request to the application.
A stored cross-site scripting (XSS) vulnerability exists in automatic1111/stable-diffusion-webui version git 82a973c. An attacker can upload an HTML file, which the application interprets as content-type application/html. If a victim accesses the malicious link, it will execute arbitrary JavaScript in the victim's browser.
A vulnerability in the gradio-app/gradio repository, version git 67e4044, allows for path traversal on Windows OS. The implementation of the blocked_path functionality, which is intended to disallow users from reading certain files, is flawed. Specifically, while the application correctly blocks access to paths like 'C:/tmp/secret.txt', it fails to block access when using NTFS Alternate Data Streams (ADS) syntax, such as 'C:/tmp/secret.txt::$DATA'. This flaw can lead to unauthorized reading of blocked file paths.
A vulnerability in the `ImageClassificationDataset.from_csv()` API of the `dmlc/gluon-cv` repository, version 0.10.0, allows for arbitrary file write. The function downloads and extracts `tar.gz` files from URLs without proper sanitization, making it susceptible to a TarSlip vulnerability. Attackers can exploit this by crafting malicious tar files that, when extracted, can overwrite files on the victim's system via path traversal or faked symlinks.
In kedro-org/kedro version 0.19.8, the `pull_package()` API function allows users to download and extract micro packages from the Internet. However, the function `project_wheel_metadata()` within the code path can execute the `setup.py` file inside the tar file, leading to remote code execution (RCE) by running arbitrary commands on the victim's machine.
A Server-Side Request Forgery (SSRF) vulnerability was discovered in haotian-liu/llava, affecting version git c121f04. This vulnerability allows an attacker to make the server perform HTTP requests to arbitrary URLs, potentially accessing sensitive data that is only accessible from the server, such as AWS metadata credentials.
A local file inclusion vulnerability exists in haotian-liu/llava at commit c121f04. This vulnerability allows an attacker to access any file on the system by sending multiple crafted requests to the server. The issue is due to improper input validation in the gradio web UI component.
A remote code execution vulnerability exists in open-mmlab/mmdetection version v3.3.0. The vulnerability is due to the use of the `pickle.loads()` function in the `all_reduce_dict()` distributed training API without proper sanitization. This allows an attacker to execute arbitrary code by broadcasting a malicious payload to the distributed training network.
A remote code execution vulnerability exists in invoke-ai/invokeai versions 5.3.1 through 5.4.2 via the /api/v2/models/install API. The vulnerability arises from unsafe deserialization of model files using torch.load without proper validation. Attackers can exploit this by embedding malicious code in model files, which is executed upon loading. This issue is fixed in version 5.4.3.
A Cross-Origin Resource Sharing (CORS) vulnerability exists in feast-dev/feast version 0.40.0. The CORS configuration on the agentscope server does not properly restrict access to only trusted origins, allowing any external domain to make requests to the API. This can bypass intended security controls and potentially expose sensitive information.
A stored cross-site scripting (XSS) vulnerability exists in Serge version 0.9.0. The vulnerability is due to improper neutralization of input during web page generation in the chat prompt. An attacker can exploit this vulnerability by sending a crafted message containing malicious HTML/JavaScript code, which will be stored and executed whenever the chat is accessed, leading to unintended content being shown to the user and potential phishing attacks.
A missing check_access() function in the lollms_binding_infos module of the parisneo/lollms repository, version V14, allows attackers to add, modify, and remove bindings arbitrarily. This vulnerability affects the /install_binding and /reinstall_binding endpoints, among others, enabling unauthorized access and manipulation of binding settings without requiring the client_id value.
In lunary-ai/lunary before version 1.6.3, an improper access control vulnerability exists where a user can access prompt data of another user. This issue affects version 1.6.2 and the main branch. The vulnerability allows unauthorized users to view sensitive prompt data by accessing specific URLs, leading to potential exposure of critical information.
A vulnerability in danny-avila/librechat version git a1647d7 allows an unauthenticated attacker to cause a denial of service by sending a crafted payload to the server. The middleware `checkBan` is not surrounded by a try-catch block, and an unhandled exception will cause the server to crash. This issue is fixed in version 0.7.6.
In danny-avila/librechat version git 0c2a583, there is an improper input validation vulnerability. The application uses multer middleware for handling multipart file uploads. When using in-memory storage (the default setting for multer), there is no limit on the upload file size. This can lead to a server crash due to out-of-memory errors when handling large files. An attacker without any privileges can exploit this vulnerability to cause a complete denial of service. The issue is fixed in version 0.7.6.
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.
A Denial of Service (DoS) vulnerability was discovered in the /api/v1/boards/{board_id} endpoint of invoke-ai/invokeai version v5.0.2. This vulnerability occurs when an excessively large payload is sent in the board_name field during a PATCH request. By sending a large payload, the UI becomes unresponsive, rendering it impossible for users to interact with or manage the affected board. Additionally, the option to delete the board becomes inaccessible, amplifying the severity of the issue.
In invoke-ai/invokeai version v5.0.2, the web API `POST /api/v1/images/delete` is vulnerable to Arbitrary File Deletion. This vulnerability allows unauthorized attackers to delete arbitrary files on the server, potentially including critical or sensitive system files such as SSH keys, SQLite databases, and configuration files. This can impact the integrity and availability of applications relying on these files.
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.
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.
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 vulnerability in langchain-core versions >=0.1.17,<0.1.53, >=0.2.0,<0.2.43, and >=0.3.0,<0.3.15 allows unauthorized users to read arbitrary files from the host file system. The issue arises from the ability to create langchain_core.prompts.ImagePromptTemplate's (and by extension langchain_core.prompts.ChatPromptTemplate's) with input variables that can read any user-specified path from the server file system. If the outputs of these prompt templates are exposed to the user, either directly or through downstream model outputs, it can lead to the exposure of sensitive information.
automatic1111/stable-diffusion-webui version 1.10.0 contains a vulnerability where the server fails to handle excessive characters appended to the end of multipart boundaries. This flaw can be exploited by sending malformed multipart requests with arbitrary characters at the end of the boundary, leading to excessive resource consumption and a complete denial of service (DoS) for all users. The vulnerability is unauthenticated, meaning no user login or interaction is required for an attacker to exploit this issue.
In lm-sys/fastchat Release v0.2.36, the server fails to handle excessive characters appended to the end of multipart boundaries. This flaw can be exploited by sending malformed multipart requests with arbitrary characters at the end of the boundary. Each extra character is processed in an infinite loop, leading to excessive resource consumption and a complete denial of service (DoS) for all users. The vulnerability is unauthenticated, meaning no user login or interaction is required for an attacker to exploit this issue.
In eosphoros-ai/db-gpt version v0.6.0, the web API `POST /v1/personal/agent/upload` is vulnerable to Arbitrary File Upload with Path Traversal. This vulnerability allows unauthorized attackers to upload arbitrary files to the victim's file system at any location. The impact of this vulnerability includes the potential for remote code execution (RCE) by writing malicious files, such as a malicious `__init__.py` in the Python's `/site-packages/` directory.
eosphoros-ai/db-gpt version 0.6.0 is vulnerable to an arbitrary file write through the knowledge API. The endpoint for uploading files as 'knowledge' is susceptible to absolute path traversal, allowing attackers to write files to arbitrary locations on the target server. This vulnerability arises because the 'doc_file.filename' parameter is user-controllable, enabling the construction of absolute paths.
A Denial of Service (DoS) vulnerability in the multipart request boundary processing mechanism of eosphoros-ai/db-gpt v0.6.0 allows unauthenticated attackers to cause excessive resource consumption. The server fails to handle excessive characters appended to the end of multipart boundaries, leading to an infinite loop and complete denial of service for all users. This vulnerability affects all endpoints processing multipart/form-data requests.
A Denial of Service (DoS) vulnerability in the multipart request boundary processing mechanism of the Invoke-AI server (version v5.0.1) allows unauthenticated attackers to cause excessive resource consumption. The server fails to handle excessive characters appended to the end of multipart boundaries, leading to an infinite loop and a complete denial of service for all users. The affected endpoint is `/api/v1/images/upload`.
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 vulnerability in szad670401/hyperlpr v3.0 allows for a Denial of Service (DoS) attack. The server fails to handle excessive characters appended to the end of multipart boundaries, regardless of the character used. This flaw can be exploited by sending malformed multipart requests with arbitrary characters at the end of the boundary, leading to excessive resource consumption and a complete denial of service for all users. The vulnerability is unauthenticated, meaning no user login or interaction is required for an attacker to exploit this issue.
gaizhenbiao/chuanhuchatgpt version git d4ec6a3 is affected by a local file inclusion vulnerability due to the use of the gradio component gr.JSON, which has a known issue (CVE-2024-4941). This vulnerability allows unauthenticated users to access arbitrary files on the server by uploading a specially crafted JSON file and exploiting the improper input validation in the handle_dataset_selection function.
An unauthenticated Denial of Service (DoS) vulnerability was identified in ChuanhuChatGPT version 20240918, which could be exploited by sending large data payloads using a multipart boundary. Although a patch was applied for CVE-2024-7807, the issue can still be exploited by sending data in groups with 10 characters in a line, with multiple lines. This can cause the system to continuously process these characters, resulting in prolonged unavailability of the service. The exploitation now requires low privilege if authentication is enabled due to a version upgrade in Gradio.
A path traversal vulnerability exists in the Gradio Audio component of gradio-app/gradio, as of version git 98cbcae. This vulnerability allows an attacker to control the format of the audio file, leading to arbitrary file content deletion. By manipulating the output format, an attacker can reset any file to an empty file, causing a denial of service (DOS) on the server.
A Regular Expression Denial of Service (ReDoS) vulnerability exists in the gradio-app/gradio repository, affecting the gr.Datetime component. The affected version is git commit 98cbcae. The vulnerability arises from the use of a regular expression `^(?:\s*now\s*(?:-\s*(\d+)\s*([dmhs]))?)?\s*$` to process user input. In Python's default regex engine, this regular expression can take polynomial time to match certain crafted inputs. An attacker can exploit this by sending a crafted HTTP request, causing the gradio process to consume 100% CPU and potentially leading to a Denial of Service (DoS) condition on the server.
In h2oai/h2o-3 version 3.46.0.1, the `run_tool` command exposes classes in the `water.tools` package through the `ast` parser. This includes the `XGBoostLibExtractTool` class, which can be exploited to shut down the server and write large files to arbitrary directories, leading to a denial of service.
A vulnerability in the dataframe component of gradio-app/gradio (version git 98cbcae) allows for a zip bomb attack. The component uses pd.read_csv to process input values, which can accept compressed files. An attacker can exploit this by uploading a maliciously crafted zip bomb, leading to a server crash and causing a denial of service.
A vulnerability in the `/3/Parse` endpoint of h2oai/h2o-3 version 3.46.0.1 allows for a denial of service (DoS) attack. The endpoint uses a user-specified string to construct a regular expression, which is then applied to another user-specified string. By sending multiple simultaneous requests, an attacker can exhaust all available threads, leading to a complete denial of service.
Multiple Server-Side Request Forgery (SSRF) vulnerabilities were identified in the significant-gravitas/autogpt repository, specifically in the GitHub Integration and Web Search blocks. These vulnerabilities affect version agpt-platform-beta-v0.1.1. The issues arise when block inputs are controlled by untrusted sources, leading to potential credential leakage, internal network scanning, and unauthorized access to internal services, APIs, or data stores. The affected blocks include GithubListPullRequestsBlock, GithubReadPullRequestBlock, GithubAssignPRReviewerBlock, GithubListPRReviewersBlock, GithubUnassignPRReviewerBlock, GithubCommentBlock, GithubMakeIssueBlock, GithubReadIssueBlock, GithubListIssuesBlock, GithubAddLabelBlock, GithubRemoveLabelBlock, GithubListBranchesBlock, and ExtractWebsiteContentBlock.
In version 0.7.5 of danny-avila/LibreChat, there is an improper access control vulnerability. Users can share, use, and create prompts without being granted permission by the admin. This can break application logic and permissions, allowing unauthorized actions.
An arbitrary file deletion vulnerability exists in danny-avila/librechat version v0.7.5-rc2, specifically within the /api/files endpoint. This vulnerability arises from improper input validation, allowing path traversal techniques to delete arbitrary files on the server. Attackers can exploit this to bypass security mechanisms and delete files outside the intended directory, including critical system files, user data, or application resources. This vulnerability impacts the integrity and availability of the system.
In lunary-ai/lunary version 1.5.6, the `/v1/evaluators/` endpoint lacks proper access control, allowing any user associated with a project to fetch all evaluator data regardless of their role. This vulnerability permits low-privilege users to access potentially sensitive evaluation data.
In version 1.5.5 of lunary-ai/lunary, a vulnerability exists where admins, who do not have direct permissions to access billing resources, can change the permissions of existing users to include billing permissions. This can lead to a privilege escalation scenario where an administrator can manage billing, effectively bypassing the intended role-based access control. Only users with the 'owner' role should be allowed to invite members with billing permissions. This flaw allows admins to circumvent those restrictions, gaining unauthorized access and control over billing information, posing a risk to the organization’s financial resources.
An improper authorization vulnerability exists in lunary-ai/lunary version 1.5.5. The /users/me/org endpoint lacks adequate access control mechanisms, allowing unauthorized users to access sensitive information about all team members in the current organization. This vulnerability can lead to the disclosure of sensitive information such as names, roles, or emails to users without sufficient privileges, resulting in privacy violations and potential reconnaissance for targeted attacks.