Vulnerabilities (CVE)

Insufficient sanitization in MLflow leads to XSS when running a recipe that uses an untrusted dataset. This issue leads to a client-side RCE when running the recipe in Jupyter Notebook. The vulnerability stems from lack of sanitization over dataset table fields.

A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service (DoS) as an authenticated user might not be able to use the intended model, as it will open a different model each time. Additionally, an attacker can exploit this vulnerability to perform data model poisoning by creating a model with the same name, potentially causing an authenticated user to become a victim by using th ... A vulnerability in mlflow/mlflow version 2.11.1 allows attackers to create multiple models with the same name by exploiting URL encoding. This flaw can lead to Denial of Service (DoS) as an authenticated user might not be able to use the intended model, as it will open a different model each time. Additionally, an attacker can exploit this vulnerability to perform data model poisoning by creating a model with the same name, potentially causing an authenticated user to become a victim by using the poisoned model. The issue stems from inadequate validation of model names, allowing for the creation of models with URL-encoded names that are treated as distinct from their URL-decoded counterparts.

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A Local File Inclusion (LFI) vulnerability was identified in mlflow/mlflow, specifically in version 2.9.2, which was fixed in version 2.11.3. This vulnerability arises from the application's failure to properly validate URI fragments for directory traversal sequences such as '../'. An attacker can exploit this flaw by manipulating the fragment part of the URI to read arbitrary files on the local file system, including sensitive files like '/etc/passwd'. The vulnerability is a bypass to a previou ... A Local File Inclusion (LFI) vulnerability was identified in mlflow/mlflow, specifically in version 2.9.2, which was fixed in version 2.11.3. This vulnerability arises from the application's failure to properly validate URI fragments for directory traversal sequences such as '../'. An attacker can exploit this flaw by manipulating the fragment part of the URI to read arbitrary files on the local file system, including sensitive files like '/etc/passwd'. The vulnerability is a bypass to a previous patch that only addressed similar manipulation within the URI's query string, highlighting the need for comprehensive validation of all parts of a URI to prevent LFI attacks.

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cdo-local-uuid project provides a specialized UUID-generating function that can, on user request, cause a program to generate deterministic UUIDs. An information leakage vulnerability is present in `cdo-local-uuid` at version `0.4.0`, and in `case-utils` in unpatched versions (matching the pattern `0.x.0`) at and since `0.5.0`, before `0.15.0`. The vulnerability stems from a Python function, `cdo_local_uuid.local_uuid()`, and its original implementation `case_utils.local_uuid()`.

This vulnerability enables malicious users to read sensitive files on the server.

This vulnerability is capable of writing arbitrary files into arbitrary locations on the remote filesystem in the context of the server process.

A malicious user could use this issue to get command execution on the vulnerable machine and get access to data & models information.

A malicious user could use this issue to access internal HTTP(s) servers and in the worst case (ie: aws instance) it could be abuse to get a remote code execution on the victim machine.

with only one user interaction(download a malicious config), attackers can gain full command execution on the victim system.

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.

Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.9.2.

Path Traversal in GitHub repository mlflow/mlflow prior to 2.9.2.

Improper Neutralization of Special Elements Used in a Template Engine in GitHub repository mlflow/mlflow prior to 2.9.2.

A reflected Cross-Site Scripting (XSS) vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the Content-Type header in POST requests. An attacker can inject malicious JavaScript code into the Content-Type header, which is then improperly reflected back to the user without adequate sanitization or escaping, leading to arbitrary JavaScript execution in the context of the victim's browser. The vulnerability is present in the mlflow/server/auth/__init__.py file, ... A reflected Cross-Site Scripting (XSS) vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the Content-Type header in POST requests. An attacker can inject malicious JavaScript code into the Content-Type header, which is then improperly reflected back to the user without adequate sanitization or escaping, leading to arbitrary JavaScript execution in the context of the victim's browser. The vulnerability is present in the mlflow/server/auth/__init__.py file, where the user-supplied Content-Type header is directly injected into a Python formatted string and returned to the user, facilitating the XSS attack.

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An attacker can overwrite any file on the server hosting MLflow without any authentication.

MLflow allowed arbitrary files to be PUT onto the server.

An attacker is able to arbitrarily create an account in MLflow bypassing any authentication requirment.

OS Command Injection in GitHub repository mlflow/mlflow prior to 2.6.0.

An issue in MLFlow versions 2.8.1 and before allows a remote attacker to obtain sensitive information via a crafted request to REST API.

Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.5.0.

Apptainer is an open source container platform. Version 1.2.0-rc.2 introduced an ineffective privilege drop when requesting container network setup, therefore subsequent functions are called with root privileges, the attack surface is rather limited for users but an attacker could possibly craft a starter config to delete any directory on the host filesystems. A security fix has been included in Apptainer 1.2.1. There is no known workaround outside of upgrading to Apptainer 1.2.1.

Apptainer is an open source container platform for Linux. There is an ext4 use-after-free flaw that is exploitable through versions of Apptainer < 1.1.0 and installations that include apptainer-suid < 1.1.8 on older operating systems where that CVE has not been patched. That includes Red Hat Enterprise Linux 7, Debian 10 buster (unless the linux-5.10 package is installed), Ubuntu 18.04 bionic and Ubuntu 20.04 focal. Use-after-free flaws in the kernel can be used to attack the kernel for denial o ... Apptainer is an open source container platform for Linux. There is an ext4 use-after-free flaw that is exploitable through versions of Apptainer < 1.1.0 and installations that include apptainer-suid < 1.1.8 on older operating systems where that CVE has not been patched. That includes Red Hat Enterprise Linux 7, Debian 10 buster (unless the linux-5.10 package is installed), Ubuntu 18.04 bionic and Ubuntu 20.04 focal. Use-after-free flaws in the kernel can be used to attack the kernel for denial of service and potentially for privilege escalation. Apptainer 1.1.8 includes a patch that by default disables mounting of extfs filesystem types in setuid-root mode, while continuing to allow mounting of extfs filesystems in non-setuid "rootless" mode using fuse2fs. Some workarounds are possible. Either do not install apptainer-suid (for versions 1.1.0 through 1.1.7) or set `allow setuid = no` in apptainer.conf. This requires having unprivileged user namespaces enabled and except for apptainer 1.1.x versions will disallow mounting of sif files, extfs files, and squashfs files in addition to other, less significant impacts. (Encrypted sif files are also not supported unprivileged in apptainer 1.1.x.). Alternatively, use the `limit containers` options in apptainer.conf/singularity.conf to limit sif files to trusted users, groups, and/or paths, and set `allow container extfs = no` to disallow mounting of extfs overlay files. The latter option by itself does not disallow mounting of extfs overlay partitions inside SIF files, so that's why the former options are also needed.

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Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.3.1.

Relative Path Traversal in GitHub repository mlflow/mlflow prior to 2.3.1.

Modelina is a library for generating data models based on inputs such as AsyncAPI, OpenAPI, or JSON Schema documents. Versions prior to 1.0.0 are vulnerable to Code injection. This issue affects anyone who is using the default presets and/or does not handle the functionality themself. This issue has been partially mitigated in version 1.0.0, with the maintainer's GitHub Security Advisory (GHSA) noting "It is impossible to fully guard against this, because users have access to the original raw in ... Modelina is a library for generating data models based on inputs such as AsyncAPI, OpenAPI, or JSON Schema documents. Versions prior to 1.0.0 are vulnerable to Code injection. This issue affects anyone who is using the default presets and/or does not handle the functionality themself. This issue has been partially mitigated in version 1.0.0, with the maintainer's GitHub Security Advisory (GHSA) noting "It is impossible to fully guard against this, because users have access to the original raw information. However, as of version 1, if you only access the constrained models, you will not encounter this issue. Further similar situations are NOT seen as a security issue, but intended behavior." The suggested workaround from the maintainers is "Fully custom presets that change the entire rendering process which can then escape the user input."

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Path Traversal: '\..\filename' in GitHub repository mlflow/mlflow prior to 2.2.1.

Absolute Path Traversal in GitHub repository mlflow/mlflow prior to 2.2.2.

Insecure Temporary File in GitHub repository mlflow/mlflow prior to 1.23.1.