A Server-Side Request Forgery (SSRF) vulnerability exists in the lunary-ai/lunary application, specifically within the endpoint '/auth/saml/tto/download-idp-xml'. The vulnerability arises due to the application's failure to validate user-supplied URLs before using them in server-side requests. An attacker can exploit this vulnerability by sending a specially crafted request to the affected endpoint, allowing them to make unauthorized requests to internal or external resources. This could lead to the disclosure of sensitive information, service disruption, or further attacks against the network infrastructure. The issue affects the latest version of the application as of the report.

Kofax Power PDF AcroForm Annotation Out-Of-Bounds Read Information Disclosure Vulnerability. This vulnerability allows remote attackers to disclose sensitive information on affected installations of Kofax Power PDF. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the handling of Annotation objects in AcroForms. The issue results from the lack of proper validation of user-supplied data, which can result in a read past the end of an allocated buffer. An attacker can leverage this in conjunction with other vulnerabilities to execute arbitrary code in the context of the current process. Was ZDI-CAN-22933.

Kofax Power PDF PDF File Parsing Memory Corruption Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Kofax Power PDF. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of PDF files. The issue results from the lack of proper validation of user-supplied data, which can result in a memory corruption condition. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-22930.

Kofax Power PDF PDF File Parsing Stack-based Buffer Overflow Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Kofax Power PDF. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of PDF files. The issue results from the lack of proper validation of the length of user-supplied data prior to copying it to a fixed-length stack-based buffer. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-22921.

Kofax Power PDF TGA File Parsing Out-Of-Bounds Write Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Kofax Power PDF. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of TGA files. The issue results from the lack of proper validation of user-supplied data, which can result in a write past the end of an allocated buffer. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-22920.

gaizhenbiao/chuanhuchatgpt is vulnerable to an unrestricted file upload vulnerability due to insufficient validation of uploaded file types in its `/upload` endpoint. Specifically, the `handle_file_upload` function does not sanitize or validate the file extension or content type of uploaded files, allowing attackers to upload files with arbitrary extensions, including HTML files containing XSS payloads and Python files. This vulnerability, present in the latest version as of 20240310, could lead to stored XSS attacks and potentially result in remote code execution (RCE) on the server hosting the application.

In lunary-ai/lunary version 1.2.5, an improper access control vulnerability exists due to a missing permission check in the `GET /v1/users/me/org` endpoint. The platform's role definitions restrict the `Prompt Editor` role to prompt management and project viewing/listing capabilities, explicitly excluding access to user information. However, the endpoint fails to enforce this restriction, allowing users with the `Prompt Editor` role to access the full list of users in the organization. This vulnerability allows unauthorized access to sensitive user information, violating the intended access controls.

An SQL Injection vulnerability exists in the berriai/litellm repository, specifically within the `/global/spend/logs` endpoint. The vulnerability arises due to improper neutralization of special elements used in an SQL command. The affected code constructs an SQL query by concatenating an unvalidated `api_key` parameter directly into the query, making it susceptible to SQL Injection if the `api_key` contains malicious data. This issue affects the latest version of the repository. Successful exploitation of this vulnerability could lead to unauthorized access, data manipulation, exposure of confidential information, and denial of service (DoS).

A sensitive data leakage vulnerability was identified in scikit-learn's TfidfVectorizer, specifically in versions up to and including 1.4.1.post1, which was fixed in version 1.5.0. The vulnerability arises from the unexpected storage of all tokens present in the training data within the `stop_words_` attribute, rather than only storing the subset of tokens required for the TF-IDF technique to function. This behavior leads to the potential leakage of sensitive information, as the `stop_words_` attribute could contain tokens that were meant to be discarded and not stored, such as passwords or keys. The impact of this vulnerability varies based on the nature of the data being processed by the vectorizer.

A vulnerability in the `download_model_with_test_data` function of the onnx/onnx framework, version 1.16.0, allows for arbitrary file overwrite due to inadequate prevention of path traversal attacks in malicious tar files. This vulnerability enables attackers to overwrite any file on the system, potentially leading to remote code execution, deletion of system, personal, or application files, thus impacting the integrity and availability of the system. The issue arises from the function's handling of tar file extraction without performing security checks on the paths within the tar file, as demonstrated by the ability to overwrite the `/home/kali/.ssh/authorized_keys` file by specifying an absolute path in the malicious tar file.

A Server-Side Request Forgery (SSRF) vulnerability exists in the file upload section of imartinez/privategpt version 0.5.0. This vulnerability allows attackers to send crafted requests that could result in unauthorized access to the local network and potentially sensitive information. Specifically, by manipulating the 'path' parameter in a file upload request, an attacker can cause the application to make arbitrary requests to internal services, including the AWS metadata endpoint. This issue could lead to the exposure of internal servers and sensitive data.

In lunary-ai/lunary version 1.2.4, an account takeover vulnerability exists due to the exposure of password recovery tokens in API responses. Specifically, when a user initiates the password reset process, the recovery token is included in the response of the `GET /v1/users/me/org` endpoint, which lists all users in a team. This allows any authenticated user to capture the recovery token of another user and subsequently change that user's password without consent, effectively taking over the account. The issue lies in the inclusion of the `recovery_token` attribute in the users object returned by the API.

In lunary-ai/lunary version 1.2.2, a business logic error allows users to bypass the intended limitations on team member invitations and additions, regardless of their subscription plan. The vulnerability arises due to the lack of validation against the predefined member limits in the SEAT_ALLOWANCE constants during the invitation and joining processes. This issue enables users on any plan, including the free plan, to invite and add more members to a team than allowed, effectively circumventing the system's subscription model. The flaw is located in the backend's handling of user invitations and additions, specifically in the /api/v1/auth/index.ts and /api/v1/users.ts endpoints, where the system fails to check the current number of team members against the allowed limits before proceeding with the invitation and addition operations.

An Improper Access Control vulnerability exists in the lunary-ai/lunary repository, affecting versions up to and including 1.2.2. The vulnerability allows unauthorized users to view any prompts in any projects by supplying a specific prompt ID to an endpoint that does not adequately verify the ownership of the prompt ID. This issue was fixed in version 1.2.25.

An Incorrect Authorization vulnerability exists in lunary-ai/lunary versions up to and including 1.2.2, which allows unauthenticated users to delete any dataset. The vulnerability is due to the lack of proper authorization checks in the dataset deletion endpoint. Specifically, the endpoint does not verify if the provided project ID belongs to the current user, thereby allowing any dataset to be deleted without proper authentication. This issue was fixed in version 1.2.8.

A Privilege Escalation Vulnerability exists in lunary-ai/lunary version 1.2.2, where any user can delete any datasets due to missing authorization checks. The vulnerability is present in the dataset deletion functionality, where the application fails to verify if the user requesting the deletion has the appropriate permissions. This allows unauthorized users to send a DELETE request to the server and delete any dataset by specifying its ID. The issue is located in the datasets.delete function within the datasets index file.

An Insecure Direct Object Reference (IDOR) vulnerability was identified in lunary-ai/lunary, affecting versions up to and including 1.2.2. This vulnerability allows unauthorized users to view, update, or delete any dataset_prompt or dataset_prompt_variation within any dataset or project. The issue stems from improper access control checks in the dataset management endpoints, where direct references to object IDs are not adequately secured against unauthorized access. This vulnerability was fixed in version 1.2.25.

An improper access control vulnerability exists in the lunary-ai/lunary repository, specifically within the versions.patch functionality for updating prompts. Affected versions include 1.2.2 up to but not including 1.2.25. The vulnerability allows unauthorized users to update prompt details due to insufficient access control checks. This issue was addressed and fixed in version 1.2.25.

A timing attack vulnerability exists in the gaizhenbiao/chuanhuchatgpt repository, specifically within the password comparison logic. The vulnerability is present in version 20240310 of the software, where passwords are compared using the '=' operator in Python. This method of comparison allows an attacker to guess passwords based on the timing of each character's comparison. The issue arises from the code segment that checks a password for a particular username, which can lead to the exposure of sensitive information to an unauthorized actor. An attacker exploiting this vulnerability could potentially guess user passwords, compromising the security of the system.

A blind SQL injection vulnerability exists in the berriai/litellm application, specifically within the '/team/update' process. The vulnerability arises due to the improper handling of the 'user_id' parameter in the raw SQL query used for deleting users. An attacker can exploit this vulnerability by injecting malicious SQL commands through the 'user_id' parameter, leading to potential unauthorized access to sensitive information such as API keys, user information, and tokens stored in the database. The affected version is 1.27.14.