Insecure permissions in Netgear WNR614 JNR1010V2/N300-V1.1.0.54_1.0.1 allows attackers to access URLs and directories embedded within the firmware via unspecified vectors.

Under certain circumstances communications between the ICU tool and an iSTAR Pro door controller is susceptible to Machine-in-the-Middle attacks which could impact door control and configuration.

Dynamsoft Service 1.8.1025 through 1.8.2013, 1.7.0330 through 1.7.2531, 1.6.0428 through 1.6.1112, 1.5.0625 through 1.5.3116, 1.4.0618 through 1.4.1230, and 1.0.516 through 1.3.0115 has Incorrect Access Control. This is fixed in 1.8.2014, 1.7.4212, 1.6.3212, 1.5.31212, 1.4.3212, and 1.3.3212.

kubeflow/kubeflow is vulnerable to a Regular Expression Denial of Service (ReDoS) attack due to inefficient regular expression complexity in its email validation mechanism. An attacker can remotely exploit this vulnerability without authentication by providing specially crafted input that causes the application to consume an excessive amount of CPU resources. This vulnerability affects the latest version of kubeflow/kubeflow, specifically within the centraldashboard-angular backend component. The impact of exploiting this vulnerability includes resource exhaustion, and service disruption.

In h2oai/h2o-3 version 3.40.0.4, an exposure of sensitive information vulnerability exists due to an arbitrary system path lookup feature. This vulnerability allows any remote user to view full paths in the entire file system where h2o-3 is hosted. Specifically, the issue resides in the Typeahead API call, which when requested with a typeahead lookup of '/', exposes the root filesystem including directories such as /home, /usr, /bin, among others. This vulnerability could allow attackers to explore the entire filesystem, and when combined with a Local File Inclusion (LFI) vulnerability, could make exploitation of the server trivial.

A vulnerability in the PyTorch's torch.distributed.rpc framework, specifically in versions prior to 2.2.2, allows for remote code execution (RCE). The framework, which is used in distributed training scenarios, does not properly verify the functions being called during RPC (Remote Procedure Call) operations. This oversight permits attackers to execute arbitrary commands by leveraging built-in Python functions such as eval during multi-cpu RPC communication. The vulnerability arises from the lack of restriction on function calls when a worker node serializes and sends a PythonUDF (User Defined Function) to the master node, which then deserializes and executes the function without validation. This flaw can be exploited to compromise master nodes initiating distributed training, potentially leading to the theft of sensitive AI-related data.

A Cross-site Scripting (XSS) vulnerability exists in the SAML metadata endpoint `/auth/saml/${org?.id}/metadata` of lunary-ai/lunary version 1.2.7. The vulnerability arises due to the application's failure to escape or validate the `orgId` parameter supplied by the user before incorporating it into the generated response. Specifically, the endpoint generates XML responses for SAML metadata, where the `orgId` parameter is directly embedded into the XML structure without proper sanitization or validation. This flaw allows an attacker to inject arbitrary JavaScript code into the generated SAML metadata page, leading to potential theft of user cookies or authentication tokens.

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.