In MLflow version 3.9.0, the MLflow Assistant feature introduced improper origin validation in its /ajax-api endpoints. This vulnerability allows a remote attacker to exploit cross-origin requests from a malicious webpage to interact with the MLflow Assistant running on a victim's local machine. By bypassing the loopback-only restriction, the attacker can modify the Assistant's configuration to enable full access, which in turn allows the execution of arbitrary commands via the Claude Code sub-agent. This issue is resolved in version 3.10.0.

In mlflow/mlflow versions prior to 3.11.0, the `get_or_create_nfs_tmp_dir()` function in `mlflow/utils/file_utils.py` creates temporary directories with world-writable permissions (0o777), and the `_create_model_downloading_tmp_dir()` function in `mlflow/pyfunc/__init__.py` creates directories with group-writable permissions (0o770). These insecure permissions allow local attackers to tamper with model artifacts, such as cloudpickle-serialized Python objects, and achieve arbitrary code execution when the tampered artifacts are deserialized via `cloudpickle.load()`. This vulnerability is particularly critical in environments with shared NFS mounts, such as Databricks, where NFS is enabled by default. The issue is a continuation of the vulnerability class addressed in CVE-2025-10279, which was only partially fixed.

A vulnerability in mlflow/mlflow versions 3.9.0 and earlier allows unauthenticated access to certain FastAPI routes when the server is started with authentication enabled (`--app-name basic-auth`) and served via uvicorn (ASGI). The FastAPI permission middleware only enforces authentication on `/gateway/` routes, leaving other routes such as the Job API (`/ajax-api/3.0/jobs/*`) and the OpenTelemetry trace ingestion API (`/v1/traces`) unprotected. This allows unauthenticated remote attackers to submit jobs, read job results, cancel running jobs, and inject arbitrary trace data into experiments. The issue arises from an architectural mismatch between Flask and FastAPI authentication mechanisms, where the `_find_fastapi_validator()` function fails to handle non-`/gateway/` paths, resulting in a complete authentication bypass. This vulnerability is fixed in version 3.10.0.

The MCP Registry provides MCP clients with a list of MCP servers, like an app store for MCP servers. Prior to 1.7.6, the client-side and server-side GitHub OIDC flow is bound only to a global audience string, not to the specific registry instance being targeted. On the client side, the publisher always appends audience=mcp-registry when requesting the GitHub Actions ID token, regardless of the selected --registry URL. On the server side, the exchange endpoint validates only that same fixed audience and then derives publish permissions directly from repository_owner. As a result, a token legitimately obtained while interacting with one registry deployment remains acceptable to any other deployment that shares the same code and audience string. This vulnerability is fixed in 1.7.6.

The MCP Registry provides MCP clients with a list of MCP servers, like an app store for MCP servers. Prior to 1.7.7, the Registry's HTTP-based namespace verification (POST /v0/auth/http, POST /v0.1/auth/http) uses safeDialContext (internal/api/handlers/v0/auth/http.go:67-110) to refuse dialling private/internal addresses when fetching the well-known public-key file from a publisher-supplied domain. The blocklist (isBlockedIP, lines 125-133) relies entirely on Go stdlib's IsLoopback / IsPrivate / IsLinkLocalUnicast / IsMulticast / IsUnspecified plus a manual CGNAT range. None of these cover IPv6 6to4 (2002::/16), NAT64 (64:ff9b::/96 and 64:ff9b:1::/48 per RFC 8215), or deprecated site-local (fec0::/10) — all of which encode arbitrary IPv4 in the address bits and tunnel to RFC1918 / cloud-metadata services on dual-stack / NAT64-enabled hosts. This vulnerability is fixed in 1.7.7.

The MCP Registry provides MCP clients with a list of MCP servers, like an app store for MCP servers. Prior to 1.7.7, the public catalogue UI served at GET / (file internal/api/handlers/v0/ui_index.html) is vulnerable to stored cross-site scripting via the server.websiteUrl field of any published server.json. Server-side validation in internal/validators/validators.go (validateWebsiteURL) only checks that the URL parses, is absolute, and uses the https scheme; it does not reject quote characters. Client-side, the value is interpolated into a double-quoted href attribute via innerHTML, using a homegrown escapeHtml helper that performs the standard textContent → innerHTML round-trip. Per the HTML serialisation algorithm, that round-trip encodes only &, <, > and U+00A0 inside text nodes — it does not encode " or '. A literal " in websiteUrl therefore breaks out of the href attribute, allowing arbitrary on* event handlers to be appended to the same <a> element. The Content-Security-Policy on / is script-src 'self' 'unsafe-inline' https://cdn.tailwindcss.com, so the injected event handlers execute. Any user able to obtain a publish token (e.g. via POST /v0/auth/github-at with their own GitHub account, or POST /v0/auth/none on a deployment that has anonymous auth enabled) can plant a poisoned record visible to every visitor of the registry homepage. This vulnerability is fixed in 1.7.7.

A vulnerability in the `_create_model_version()` handler of `mlflow/server/handlers.py` in mlflow/mlflow versions 3.9.0 and earlier allows an unauthenticated remote attacker to read arbitrary files from the server's filesystem. The issue arises when a `CreateModelVersion` request includes the tag `mlflow.prompt.is_prompt`, which bypasses source path validation. This enables an attacker to store an arbitrary local filesystem path as the model version source. The `get_model_version_artifact_handler()` function later uses this source to serve files without verifying the model version's prompt status, leading to a complete confidentiality compromise. This issue is fixed in version 3.10.0.

A Server-Side Request Forgery (SSRF) vulnerability exists in MLflow versions prior to 3.9.0. The `_create_webhook()` function in `mlflow/server/handlers.py` accepts a user-controlled `url` parameter without validation, and the `_send_webhook_request()` function in `mlflow/webhooks/delivery.py` sends HTTP POST requests to this attacker-controlled URL. This allows an authenticated attacker to force the MLflow backend to send HTTP requests to internal services, cloud metadata endpoints, or arbitrary external servers. The lack of input sanitization, URL scheme filtering, or allowlist validation on the webhook URL enables exploitation, potentially leading to cloud credential theft, internal network access, and data exfiltration.

Zarf is an Airgap Native Packager Manager for Kubernetes. Versions 0.23.0 through 0.74.1 contain an arbitrary file write vulnerability in the zarf package inspect sbom and zarf package inspect documentation subcommands. These subcommands output file paths are constructed by joining a user-controlled output directory with the package's Metadata.Name field read directly from the untrusted package's zarf.yaml manifest. Although Metadata.Name is validated against a regex on package creation, an attacker can unarchive a package to modify the Metadata.Name field to contain path traversal sequences such as ../../etc/cron.d/malicious or absolute paths like /home/user/.ssh/authorized_keys, along with the corresponding files inside SBOMS.tar. This allows writing attacker-controlled content to arbitrary filesystem locations within the permissions of the user running the inspect command. This issue has been fixed in version 0.74.2.

MCP Java SDK is the official Java SDK for Model Context Protocol servers and clients. Prior to 1.0.0, the java-sdk contains a DNS rebinding vulnerability. This vulnerability allows an attacker to access a locally or network-private java-sdk MCP server via a victims browser that is either local, or network adjacent. This allows an attacker to make any tool call to the server as if they were a locally running MCP connected AI agent. This vulnerability is fixed in 1.0.0.

MLflow is vulnerable to an authorization bypass affecting the AJAX endpoint used to download saved model artifacts. Due to missing access‑control validation, a user without permissions to a given experiment can directly query this endpoint and retrieve model artifacts they are not authorized to access. This issue affects MLflow version through 3.10.1

MLflow is vulnerable to Stored Cross-Site Scripting (XSS) caused by unsafe parsing of YAML-based MLmodel artifacts in its web interface. An authenticated attacker can upload a malicious MLmodel file containing a payload that executes when another user views the artifact in the UI. This allows actions such as session hijacking or performing operations on behalf of the victim. This issue affects MLflow version through 3.10.1

In mlflow/mlflow, the FastAPI job endpoints under `/ajax-api/3.0/jobs/*` are not protected by authentication or authorization when the `basic-auth` app is enabled. This vulnerability affects the latest version of the repository. If job execution is enabled (`MLFLOW_SERVER_ENABLE_JOB_EXECUTION=true`) and any job function is allowlisted, any network client can submit, read, search, and cancel jobs without credentials, bypassing basic-auth entirely. This can lead to unauthenticated remote code execution if allowed jobs perform privileged actions such as shell execution or filesystem changes. Even if jobs are deemed safe, this still constitutes an authentication bypass, potentially resulting in job spam, denial of service (DoS), or data exposure in job results.

The Go MCP SDK used Go's standard encoding/json. Prior to version 1.4.0, the Model Context Protocol (MCP) Go 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 StreamableHTTPHandler or SSEHandler, 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. This issue has been patched in version 1.4.0.

MCP Java SDK is the official Java SDK for Model Context Protocol servers and clients. Prior to versions 0.83.0, 1.0.1, and 1.1.1, there is a hardcoded wildcard CORS vulnerability. This issue has been patched in versions 0.83.0, 1.0.1, and 1.1.1.

A command injection vulnerability exists in mlflow/mlflow when serving a model with `enable_mlserver=True`. The `model_uri` is embedded directly into a shell command executed via `bash -c` without proper sanitization. If the `model_uri` contains shell metacharacters, such as `$()` or backticks, it allows for command substitution and execution of attacker-controlled commands. This vulnerability affects the latest version of mlflow/mlflow and can lead to privilege escalation if a higher-privileged service serves models from a directory writable by lower-privileged users.

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.

A path traversal vulnerability exists in the `extract_archive_to_dir` function within the `mlflow/pyfunc/dbconnect_artifact_cache.py` file of the mlflow/mlflow repository. This vulnerability, present in versions before v3.7.0, arises due to the lack of validation of tar member paths during extraction. An attacker with control over the tar.gz file can exploit this issue to overwrite arbitrary files or gain elevated privileges, potentially escaping the sandbox directory in multi-tenant or shared cluster environments.

MCP Ruby SDK is the official Ruby SDK for Model Context Protocol servers and clients. Prior to version 0.9.2, the Ruby SDK's streamable_http_transport.rb implementation contains a session hijacking vulnerability. An attacker who obtains a valid session ID can completely hijack the victim's Server-Sent Events (SSE) stream and intercept all real-time data. Version 0.9.2 contains a patch.

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.

The Go MCP SDK used Go's standard encoding/json. Prior to version 1.4.1, the Go SDK's Streamable HTTP transport accepted browser-generated cross-site `POST` requests without validating the `Origin` header and without requiring `Content-Type: application/json`. In deployments without Authorization, especially stateless or sessionless configurations, this allows an arbitrary website to send MCP requests to a local server and potentially trigger tool execution. Version 1.4.1 contains a patch for the issue.

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.

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.

Agentgateway is an open source data plane for agentic AI connectivity within or across any agent framework or environment. Prior to version 0.12.0, when converting MCP tools/call request to OpenAPI request, input path, query, and header values are not sanitized. This issue has been patched in version 0.12.0.

Zarf is an Airgap Native Packager Manager for Kubernetes. From version 0.54.0 to before version 0.73.1, a path traversal vulnerability in archive extraction allows a specifically crafted Zarf package to create symlinks pointing outside the destination directory, enabling arbitrary file read or write on the system processing the package. This issue has been patched in version 0.73.1.