PatchFirst · Свежие уязвимости
За последние 30 дней в Python опубликовано уязвимостей: 318; в каталоге KEV: 1.
Уязвимостей: 7143
Deserialization of untrusted data can occur in versions 3.7.0 or newer of Ydata's ydata-profiling open-source library, enabling a malicously crafted report to run arbitrary code on an end user's system when loaded.
Remote Code Execution can occur in versions of the MLflow platform running version 1.11.0 or newer, enabling a maliciously crafted MLproject to execute arbitrary code on an end user’s system when run.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.27.0 or newer, enabling a maliciously crafted Recipe to execute arbitrary code on an end user’s system when run.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.5.0 or newer, enabling a maliciously uploaded PyTorch model to run arbitrary code on an end user’s system when interacted with.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.5.0 or newer, enabling a maliciously uploaded Langchain AgentExecutor model to run arbitrary code on an end user’s system when interacted with.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 2.0.0rc0 or newer, enabling a maliciously uploaded Tensorflow model to run arbitrary code on an end user’s system when interacted with.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.23.0 or newer, enabling a maliciously uploaded LightGBM scikit-learn model to run arbitrary code on an end user’s system when interacted with.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.24.0 or newer, enabling a maliciously uploaded pmdarima model to run arbitrary code on an end user’s system when interacted with.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 0.9.0 or newer, enabling a maliciously uploaded PyFunc model to run arbitrary code on an end user’s system when interacted with.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.
Deserialization of untrusted data can occur in versions of the MLflow platform running version 1.1.0 or newer, enabling a maliciously uploaded scikit-learn model to run arbitrary code on an end user’s system when interacted with.
A command injection vulnerability exists in the gradio-app/gradio repository, specifically within the 'test-functional.yml' workflow. The vulnerability arises due to improper neutralization of special elements used in a command, allowing for unauthorized modification of the base repository or secrets exfiltration. The issue affects versions up to and including '@gradio/video@0.6.12'. The flaw is present in the workflow's handling of GitHub context information, where it echoes the full name of the head repository, the head branch, and the workflow reference without adequate sanitization. This could potentially lead to the exfiltration of sensi
qdrant/qdrant version 1.9.0-dev is vulnerable to arbitrary file read and write during the snapshot recovery process. Attackers can exploit this vulnerability by manipulating snapshot files to include symlinks, leading to arbitrary file read by adding a symlink that points to a desired file on the filesystem and arbitrary file write by including a symlink and a payload file in the snapshot's directory structure. This vulnerability allows for the reading and writing of arbitrary files on the server, which could potentially lead to a full takeover of the system. The issue is fixed in version v1.9.0.
Sentry is a developer-first error tracking and performance monitoring platform. Sentry's Slack integration incorrectly records the incoming request body in logs. This request data can contain sensitive information, including the deprecated Slack verification token. With this verification token, it is possible under specific configurations, an attacker can forge requests and act as the Slack integration. The request body is leaked in log entries matching `event == "slack.*" && name == "sentry.integrations.slack" && request_data == *`. The deprecated slack verification token, will be found in the `request_data.token` key. **SaaS users** do not
The Vanna library uses a prompt function to present the user with visualized results, it is possible to alter the prompt using prompt injection and run arbitrary Python code instead of the intended visualization code. Specifically - allowing external input to the library’s “ask” method with "visualize" set to True (default behavior) leads to remote code execution.
Fides is an open-source privacy engineering platform. The Fides webserver has a number of endpoints that retrieve `ConnectionConfiguration` records and their associated `secrets` which _can_ contain sensitive data (e.g. passwords, private keys, etc.). These `secrets` are stored encrypted at rest (in the application database), and the associated endpoints are not meant to expose that sensitive data in plaintext to API clients, as it could be compromising. Fides's developers have available to them a Pydantic field-attribute (`sensitive`) that they can annotate as `True` to indicate that a given secret field should not be exposed via the API. Th
Wagtail is an open source content management system built on Django. Due to an improperly applied permission check in the `wagtail.contrib.settings` module, a user with access to the Wagtail admin and knowledge of the URL of the edit view for a settings model can access and update that setting, even when they have not been granted permission over the model. The vulnerability is not exploitable by an ordinary site visitor without access to the Wagtail admin. Patched versions have been released as Wagtail 6.0.5 and 6.1.2. Wagtail releases prior to 6.0 are unaffected. Users are advised to upgrade. Site owners who are unable to upgrade to a patch
A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the `autodocs.yml` workflow file. The vulnerability arises from the insecure handling of the `github.head_ref` user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including
A path traversal vulnerability was identified in the parisneo/lollms-webui repository, specifically within version 9.6. The vulnerability arises due to improper handling of user-supplied input in the 'list_personalities' endpoint. By crafting a malicious HTTP request, an attacker can traverse the directory structure and view the contents of any folder, albeit limited to subfolder names only. This issue was demonstrated via a specific HTTP request that manipulated the 'category' parameter to access arbitrary directories. The vulnerability is present in the code located at the 'endpoints/lollms_advanced.py' file.
Fides is an open-source privacy engineering platform. The Fides webserver requires a connection to a hosted PostgreSQL database for persistent storage of application data. If the password used by the webserver for this database connection includes special characters such as `@` and `$`, webserver startup fails and the part of the password following the special character is exposed in webserver error logs. This is caused by improper escaping of the SQLAlchemy password string. As a result users are subject to a partial exposure of hosted database password in webserver logs. The vulnerability has been patched in Fides version `2.37.0`. Users are
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