CVE-2026-84452 in winml-cli
Summary
by MITRE • 09/02/2026
Windows ML CLI is a command line tool for building portable, performant, and high-quality AI models for Windows ML. Prior to 0.4.0, the src/winml/modelkit/serve/cli_api.py component exposes WinML CLI commands through a localhost HTTP API without authentication and configures the allow_origins setting as a wildcard in both src/winml/modelkit/serve/cli_api.py and src/winml/modelkit/serve/app.py. A malicious website loaded by a user can send cross-origin requests to /v1/cli/build or /v1/cli/config and set the trust_remote_code parameter to true, which is converted to the --trust-remote-code command-line flag without validation. This reaches AutoConfig.from_pretrained with trust_remote_code=True in src/winml/modelkit/loader/_autoconfig.py and imports Python code from an attacker-controlled model repository, resulting in arbitrary code execution as the server user. This issue is fixed in version 0.4.0.
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Analysis
by VulDB Data Team • 09/02/2026
The vulnerability resides within the Windows ML CLI tool, specifically affecting versions prior to 0.4.0, and involves a critical combination of insecure default configurations and insufficient input validation that leads to remote code execution. The core technical flaw stems from two distinct but compounding issues: an unauthenticated HTTP API exposed on localhost and permissive Cross-Origin Resource Sharing (CORS) settings. By configuring the allow_origins setting as a wildcard, the application permits requests from any origin, including malicious web pages hosted by attackers. This configuration effectively removes browser-based protections that would normally prevent cross-site scripting or data theft via AJAX requests, allowing an attacker to programmatically interact with the CLI tool's backend services directly from a victim's browser context without triggering standard security warnings.
The operational impact is severe because it enables arbitrary code execution as the user running the Windows ML server process. The attack vector begins when a malicious website loads in a victim's browser and sends cross-origin requests to specific endpoints, namely /v1/cli/build or /v1/cli/config. These endpoints accept a trust_remote_code parameter which, if set to true, is passed directly into the underlying Python library as the --trust-remote-code command-line flag without any form of validation or sanitization by the CLI wrapper. This lack of input verification allows an attacker to dictate how external model repositories are handled by the application's loader components.
Once this parameter reaches the AutoConfig.from_pretrained function in src/winml/modelkit/loader/_autoconfig.py with trust_remote_code set to true, it instructs the library to dynamically import and execute Python code contained within a remote model repository hosted on an attacker-controlled server. This mechanism is designed for legitimate use cases where developers need to load custom architectures not natively supported by the framework, but in this vulnerable state, it becomes a direct path for exploitation. The imported code runs with the privileges of the user executing the Windows ML CLI service, potentially granting the attacker full control over the system depending on the privilege level of that account. This aligns closely with CWE-94 Improper Control of Generation of Code (Code Injection) and CWE-200 Exposure of Sensitive Information to an Unauthorized Actor due to the lack of authentication on the API endpoint.
From a threat intelligence perspective, this vulnerability maps to MITRE ATT&CK techniques involving Remote Services and Command Line Interface exploitation. The attacker leverages the local service as a pivot point for code execution, effectively bypassing network-level security controls by exploiting the trust relationship between localhost services and web browsers. To mitigate this risk, organizations must immediately upgrade to version 0.4.0 or later where these issues have been addressed. In environments where upgrading is not immediately feasible, it is critical to restrict access to the localhost API endpoints using firewall rules or network segmentation to prevent external origins from reaching them. Additionally, implementing strict CORS policies that whitelist only specific trusted domains rather than using wildcards can significantly reduce the attack surface for cross-origin attacks.