CVE-2026-85668 in Xinference
Summary
by MITRE • 09/04/2026
Xinference (affected commit 4a94832, v3.x) contains an unauthenticated arbitrary-path file read vulnerability in the POST /v1/models/llm/auto-register endpoint, which accepts a caller-supplied model_path parameter without authentication or path confinement. The endpoint reads and parses config.json, tokenizer_config.json, and chat_template.jinja files at the supplied path and reflects the parsed content back to the caller, allowing an unauthenticated attacker to probe the server filesystem and extract content of files with those names in any directory.
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Analysis
by VulDB Data Team • 09/04/2026
The vulnerability identified in Xinference versions prior to commit 4a94832 represents a critical security flaw within the model registration subsystem, specifically affecting the POST /v1/models/llm/auto-register endpoint. This component is designed to facilitate the automatic discovery and configuration of large language models by parsing essential metadata files such as config.json, tokenizer_config.json, and chat_template.jinja. However, the implementation fails to enforce proper access controls or input validation on the model_path parameter provided by the client. The absence of authentication mechanisms allows any network-accessible actor to interact with this endpoint without credentials, while the lack of path confinement means that user-supplied paths are not sanitized against directory traversal sequences. This combination creates a scenario where an attacker can manipulate the file system navigation logic to bypass intended boundaries and access arbitrary locations on the host operating system.
From a technical perspective, the core issue lies in how the application resolves the model_path argument before attempting to read the associated configuration files. When a request is received, the server directly incorporates the supplied path into its internal file operations without verifying that the resulting absolute path remains within an allowed directory structure or checking for symbolic links that might redirect access outside of expected scopes. Consequently, by injecting sequences such as ../ in the model_path parameter, an attacker can traverse up the directory hierarchy to reach sensitive areas of the filesystem. The vulnerability is particularly severe because the endpoint does not merely return a success or failure status; it actively reads and parses these specific JSON and template files from the targeted location and reflects their contents back to the caller. This behavior transforms what might otherwise be an information disclosure into a powerful data exfiltration vector, as the raw content of any file matching those names is returned in the HTTP response body.
The operational impact of this vulnerability extends beyond simple directory probing. An unauthenticated attacker can systematically scan common directories where sensitive configuration files, environment variables stored in text format, or private keys might reside under filenames like config.json or tokenizer_config.json. If such files exist at accessible paths, their contents are fully exposed to the remote adversary. This could lead to the leakage of internal infrastructure details, database credentials embedded in configuration scripts, proprietary model weights metadata, or other sensitive intellectual property stored on the server. In a production environment where Xinference is deployed as part of a larger AI inference pipeline, this exposure compromises the confidentiality and integrity of the underlying system components. The ability to read arbitrary files effectively neutralizes any security boundaries established by file permissions if those files are readable by the user account running the Xinference service, which is often the case in containerized or local development environments.
This vulnerability aligns with CWE-22: Improper Limitation of a Pathname to a Restricted Directory and CWE-732: Owner Privileges Required for Critical Function, as it involves an unauthenticated actor performing actions that should be restricted based on path constraints and privilege levels. In the context of the MITRE ATT&CK framework, this behavior corresponds to T1083: File and Directory Discovery, where adversaries use techniques to enumerate files and directories to gather information necessary for further exploitation or lateral movement within a compromised network. The reflection of file contents also touches upon aspects of data exfiltration, although it is primarily an initial reconnaissance step that enables deeper compromise depending on the sensitivity of the discovered files.
Mitigation strategies must prioritize immediate patching to version 4a94832 or later, where these path validation and authentication checks have been addressed. In environments where upgrading is not immediately feasible, network-level controls should be implemented to restrict access to the /v1/models/llm/auto-register endpoint exclusively from trusted internal networks or specific IP ranges using firewall rules or reverse proxy configurations. Additionally, deploying a Web Application Firewall with rules capable of detecting and blocking directory traversal patterns in POST body parameters can provide an additional layer of defense against exploitation attempts. It is also critical to ensure that the service account running Xinference operates under strict least-privilege principles, limiting its ability to read sensitive system files even if path confinement fails. Regular security audits focusing on input validation for file operations and enforcing authentication on all administrative endpoints are essential practices to prevent similar vulnerabilities in AI infrastructure components.