LocalAI Unauthenticated SSRF via Gallery URL Parameter

Publication date: 2026-07-07

Last updated on: 2026-07-07

Assigner: VulnCheck

Description

LocalAI contains an unauthenticated server-side request forgery vulnerability in the POST /models/apply endpoint that allows attackers to fetch arbitrary internal URLs. The endpoint passes unsanitized gallery URL fields directly to gallery.GetGalleryConfigFromURLWithContext without proper validation, enabling attackers to force the server to issue HTTP GET requests to private and loopback ranges with partial response content leaked through error messages.

CVSS Scores

EPSS Scores

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Meta Information

Published

2026-07-07

Last Modified

2026-07-07

Generated

2026-07-08

AI Q&A

2026-07-08

EPSS Evaluated

N/A

NVD

CVE-2026-59707

Affected Vendors & Products

Currently, no data is known.

Helpful Resources

Exploitability

CWE

KEV

CWE ID	Description
CWE-918	The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.

Attack-Flow Graph

Executive Summary

This vulnerability is an unauthenticated server-side request forgery (SSRF) in LocalAI's POST /models/apply endpoint. It occurs because the endpoint accepts gallery URL fields without proper validation and passes them directly to a function that fetches gallery configurations. As a result, attackers can make the server send HTTP GET requests to internal or private URLs, including loopback addresses, potentially accessing internal resources.

Additionally, partial response content is leaked through error messages, which can provide attackers with information about the internal network.

Impact Analysis

This vulnerability can allow attackers to access internal network resources that are normally inaccessible from outside the server. By exploiting the SSRF, attackers might gather sensitive information about internal systems or services.

The leakage of partial response content through error messages can further aid attackers in mapping the internal network or discovering sensitive data.

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