Lfprojects vulnerabilities

114 vulnerabilities

Known vulnerabilities in Lfprojects products, newest first, with CVSS severity, EPSS, KEV listings and ransomware use, across 18 products.

Products

18
7.5CVE-2025-21605alpineredis+54
EPSS score
0.86%
Redis is an open source, in-memory database that persists on disk. In versions starting at 2.6 and prior to 7.4.3, An unauthenticated client can cause unlimited growth of output buffers, until the server runs out of memory or is killed. By default, the Redis configuration does not limit the output buffer of normal clients (see client-output-buffer-limit). Therefore, the output buffer can grow unlimitedly over time. As a result, the service is exhausted and the memory is unavailable. When password authentication is enabled on the Redis server, but no password is provided, the client can still cause the output buffer to grow from "NOAUTH" responses until the system will run out of memory. This issue has been patched in version 7.4.3. An additional workaround to mitigate this problem without patching the redis-server executable is to block access to prevent unauthenticated users from connecting to Redis. This can be done in different ways. Either using network access control tools like firewalls, iptables, security groups, etc, or enabling TLS and requiring users to authenticate using client side certificates.5.8CVE-2025-52967lfprojectsmlflow+1
EPSS score
0.44%
gateway_proxy_handler in MLflow before 3.1.0 lacks gateway_path validation.7.1CVE-2025-1473lfprojectsmlflow+2
EPSS score
0.22%
A Cross-Site Request Forgery (CSRF) vulnerability exists in the Signup feature of mlflow/mlflow versions 2.17.0 to 2.20.1. This vulnerability allows an attacker to create a new account, which may be used to perform unauthorized actions on behalf of the malicious user.5.5CVE-2025-1474lfprojectsmlflow+2
EPSS score
0.37%
In mlflow/mlflow version 2.18, an admin is able to create a new user account without setting a password. This vulnerability could lead to security risks, as accounts without passwords may be susceptible to unauthorized access. Additionally, this issue violates best practices for secure user account management. The issue is fixed in version 2.19.0.5.3CVE-2025-65105apptainerapptainer+4
EPSS score
0.23%
Apptainer is an open source container platform. In Apptainer versions less than 1.4.5, a container can disable two of the forms of the little used --security option, in particular the forms --security=apparmor:<profile> and --security=selinux:<label> which otherwise put restrictions on operations that containers can do. The --security option has always been mentioned in Apptainer documentation as being a feature for the root user, although these forms do also work for unprivileged users on systems where the corresponding feature is enabled. Apparmor is enabled by default on Debian-based distributions and SElinux is enabled by default on RHEL-based distributions, but on SUSE it depends on the distribution version. This vulnerability is fixed in 1.4.5.9.8CVE-2025-11201lfprojectsmlflow+2
EPSS score
26.26%
MLflow Tracking Server Model Creation Directory Traversal Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of MLflow Tracking Server. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of model file paths. The issue results from the lack of proper validation of a user-supplied path prior to using it in file operations. An attacker can leverage this vulnerability to execute code in the context of the service account. Was ZDI-CAN-26921.9.8CVE-2025-11200lfprojectsmlflow+2
EPSS score
1.44%
MLflow Weak Password Requirements Authentication Bypass Vulnerability. This vulnerability allows remote attackers to bypass authentication on affected installations of MLflow. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of passwords. The issue results from weak password requirements. An attacker can leverage this vulnerability to bypass authentication on the system. Was ZDI-CAN-26916.9.1CVE-2025-68145lfprojectsmodel_context_protocol_servers+2
EPSS score
7.03%
In mcp-server-git versions prior to 2025.12.17, when the server is started with the --repository flag to restrict operations to a specific repository path, it did not validate that repo_path arguments in subsequent tool calls were actually within that configured path. This could allow tool calls to operate on other repositories accessible to the server process. The fix adds path validation that resolves both the configured repository and the requested path (following symlinks) and verifies the requested path is within the allowed repository before executing any git operations. Users are advised to upgrade to 2025.12.17 upon release to remediate this issue.7.1CVE-2025-68144lfprojectsmodel_context_protocol_servers+2
EPSS score
7.21%
In mcp-server-git versions prior to 2025.12.17, the git_diff and git_checkout functions passed user-controlled arguments directly to git CLI commands without sanitization. Flag-like values (e.g., `--output=/path/to/file` for `git_diff`) would be interpreted as command-line options rather than git refs, enabling arbitrary file overwrites. The fix adds validation that rejects arguments starting with - and verifies the argument resolves to a valid git ref via rev_parse before execution. Users are advised to update to 2025.12.17 resolve this issue when it is released.8.8CVE-2025-68143lfprojectsmodel_context_protocol_servers+2
EPSS score
8.13%
Model Context Protocol Servers is a collection of reference implementations for the model context protocol (MCP). In mcp-server-git versions prior to 2025.9.25, the git_init tool accepted arbitrary filesystem paths and created Git repositories without validating the target location. Unlike other tools which required an existing repository, git_init could operate on any directory accessible to the server process, making those directories eligible for subsequent git operations. The tool was removed entirely, as the server is intended to operate on existing repositories only. Users are advised to upgrade to 2025.9.25 or newer to remediate this issue.7.5CVE-2025-0453lfprojectsmlflow+2
EPSS score
11.03%
In mlflow/mlflow version 2.17.2, the `/graphql` endpoint is vulnerable to a denial of service attack. An attacker can create large batches of queries that repeatedly request all runs from a given experiment. This can tie up all the workers allocated by MLFlow, rendering the application unable to respond to other requests. This vulnerability is due to uncontrolled resource consumption.8.8CVE-2025-14287lfprojectsmlflow+2
EPSS score
1.48%
A command injection vulnerability exists in mlflow/mlflow versions before v3.7.0, specifically in the `mlflow/sagemaker/__init__.py` file at lines 161-167. The vulnerability arises from the direct interpolation of user-supplied container image names into shell commands without proper sanitization, which are then executed using `os.system()`. This allows attackers to execute arbitrary commands by supplying malicious input through the `--container` parameter of the CLI. The issue affects environments where MLflow is used, including development setups, CI/CD pipelines, and cloud deployments.8.1CVE-2025-14279lfprojectsmlflow+2
EPSS score
0.22%
MLFlow versions up to and including 3.4.0 are vulnerable to DNS rebinding attacks due to a lack of Origin header validation in the MLFlow REST server. This vulnerability allows malicious websites to bypass Same-Origin Policy protections and execute unauthorized calls against REST endpoints. An attacker can query, update, and delete experiments via the affected endpoints, leading to potential data exfiltration, destruction, or manipulation. The issue is resolved in version 3.5.0.8.1CVE-2025-66416chainguardopen-webui-compat+8
EPSS score
0.51%
The MCP Python SDK, called `mcp` on PyPI, is a Python implementation of the Model Context Protocol (MCP). Prior to version 1.23.0, tThe Model Context Protocol (MCP) Python SDK does not enable DNS rebinding protection by default for HTTP-based servers. When an HTTP-based MCP server is run on localhost without authentication using FastMCP with streamable HTTP or SSE transport, and has not configured TransportSecuritySettings, a malicious website could exploit DNS rebinding to bypass same-origin policy restrictions and send requests to the local MCP server. This could allow an attacker to invoke tools or access resources exposed by the MCP server on behalf of the user in those limited circumstances. Note that running HTTP-based MCP servers locally without authentication is not recommended per MCP security best practices. This issue does not affect servers using stdio transport. This vulnerability is fixed in 1.23.0.8.1CVE-2025-66414chainguardgemini-cli+46
EPSS score
0.51%
MCP TypeScript SDK is the official TypeScript SDK for Model Context Protocol servers and clients. Prior to 1.24.0, The Model Context Protocol (MCP) TypeScript SDK does not enable DNS rebinding protection by default for HTTP-based servers. When an HTTP-based MCP server is run on localhost without authentication with StreamableHTTPServerTransport or SSEServerTransport and has not enabled enableDnsRebindingProtection, a malicious website could exploit DNS rebinding to bypass same-origin policy restrictions and send requests to the local MCP server. This could allow an attacker to invoke tools or access resources exposed by the MCP server on behalf of the user in those limited circumstances. Note that running HTTP-based MCP servers locally without authentication is not recommended per MCP security best practices. This issue does not affect servers using stdio transport. This vulnerability is fixed in 1.24.0.8.5CVE-2025-67733alpinevalkey+6
EPSS score
0.61%
Valkey is a distributed key-value database. Prior to versions 9.0.2, 8.1.6, 8.0.7, and 7.2.12, a malicious user can use scripting commands to inject arbitrary information into the response stream for the given client, potentially corrupting or returning tampered data to other users on the same connection. The error handling code for lua scripts does not properly handle null characters. Versions 9.0.2, 8.1.6, 8.0.7, and 7.2.12 fix the issue.7CVE-2025-10279lfprojectsmlflow+2
EPSS score
0.24%
In mlflow version 2.20.3, the temporary directory used for creating Python virtual environments is assigned insecure world-writable permissions (0o777). This vulnerability allows an attacker with write access to the `/tmp` directory to exploit a race condition and overwrite `.py` files in the virtual environment, leading to arbitrary code execution. The issue is resolved in version 3.4.0.9.1CVE-2025-15031lfprojectsmlflow+2
EPSS score
0.89%
A vulnerability in MLflow's pyfunc extraction process allows for arbitrary file writes due to improper handling of tar archive entries. Specifically, the use of `tarfile.extractall` without path validation enables crafted tar.gz files containing `..` or absolute paths to escape the intended extraction directory. This issue affects the latest version of MLflow and poses a high/critical risk in scenarios involving multi-tenant environments or ingestion of untrusted artifacts, as it can lead to arbitrary file overwrites and potential remote code execution.8.1CVE-2025-15381lfprojectsmlflow+2
EPSS score
0.33%
In the latest version of mlflow/mlflow, when the `basic-auth` app is enabled, tracing and assessment endpoints are not protected by permission validators. This allows any authenticated user, including those with `NO_PERMISSIONS` on the experiment, to read trace information and create assessments for traces they should not have access to. This vulnerability impacts confidentiality by exposing trace metadata and integrity by allowing unauthorized creation of assessments. Deployments using `mlflow server --app-name=basic-auth` are affected.10CVE-2025-15379lfprojectsmlflow+2
EPSS score
2.36%
A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the `_install_model_dependencies_to_env()` function. When deploying a model with `env_manager=LOCAL`, MLflow reads dependency specifications from the model artifact's `python_env.yaml` file and directly interpolates them into a shell command without sanitization. This allows an attacker to supply a malicious model artifact and achieve arbitrary command execution on systems that deploy the model. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.