CVE-2026-17557

Published: Ott 21, 2026 Last Modified: Ott 21, 2026
ExploitDB:
Other exploit source:
Google Dorks:
MEDIUM 6,5
Source: [email protected]
Attack Vector: network
Attack Complexity: low
Privileges Required: low
User Interaction: none
Scope: unchanged
Confidentiality: high
Integrity: none
Availability: none

Description

AI Translation Available

A flaw was found in the `lm-evaluation-harness` component of Red Hat OpenShift AI. An authenticated user, able to submit an evaluation job, could exploit this by providing a malicious model URL in the `JobSpec`. This vulnerability allows for the disclosure of the tenant's model authentication secret to an attacker-controlled external listener. Additionally, it enables Server-Side Request Forgery (SSRF) attacks against internal network endpoints reachable from the evaluation job pod.

918

Server-Side Request Forgery (SSRF)

Incomplete
Abstraction: Base Structure: Simple
Common Consequences
Security Scopes Affected:
Confidentiality Integrity Access Control
Potential Impacts:
Read Application Data Execute Unauthorized Code Or Commands Bypass Protection Mechanism
Applicable Platforms
Technologies: Web Based, AI/ML, Web Server
View CWE Details
https://access.redhat.com/security/cve/CVE-2026-17557
https://access.redhat.com/security/cve/CVE-2026-17557
https://bugzilla.redhat.com/show_bug.cgi?id=2507452
https://bugzilla.redhat.com/show_bug.cgi?id=2507452