CVE-2026-111668 in AIHelms
Summary
by MITRE • 10/16/2026
AIHelms through 0.1.25 contains a missing authorization vulnerability in apps/api/v1/efficiency.py that allows authenticated low-privileged users to bypass efficiency:read and efficiency:write permission checks. Attackers can query routes like /cost/detail, /top-users and /budget to read organization-wide AI spend, and can modify suggestion statuses or generate reports.
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Analysis
by VulDB Data Team • 10/16/2026
The vulnerability identified in AIHelms versions through 0.1.25 represents a critical failure in access control mechanisms within the application's backend API infrastructure. Specifically located in the efficiency module at apps/api/v1/efficiency.py, this flaw allows authenticated users with low-privilege roles to bypass intended permission checks for both reading and writing efficiency-related data. The core technical issue stems from an improper enforcement of authorization policies where the system fails to validate whether a requesting user possesses the necessary scope or role-based permissions before processing requests directed at sensitive endpoints. This type of flaw is classically categorized under CWE-285, which describes Improper Authorization, and aligns with MITRE ATT&CK technique T1078, Valid Accounts, as it involves an attacker leveraging legitimate but insufficiently restricted credentials to access resources beyond their authorized level.
The operational impact of this vulnerability is significant due to the sensitivity of the data accessible through the affected endpoints. Attackers can exploit routes such as /cost/detail, /top-users, and /budget to extract organization-wide AI spend information. This exposure reveals financial metrics that are typically restricted to administrative or finance-level personnel, potentially leading to competitive intelligence leaks or internal security assessments being compromised by lower-tier employees or external actors who have obtained valid credentials through phishing or credential stuffing attacks. Furthermore, the ability to modify suggestion statuses and generate reports introduces a risk of data integrity compromise. An attacker could alter system recommendations or fabricate usage reports, which may lead to misguided decision-making regarding resource allocation or mask malicious activity within the AI infrastructure.
From a technical perspective, this vulnerability likely arises from missing middleware checks or incorrect logic in the route handlers that do not properly associate the user's session token with their assigned role permissions before executing business logic. The absence of these checks means that any authenticated request to these endpoints is processed without verifying if the actor has the efficiency:read or efficiency:write privileges mandated by the application's security model. This lack of server-side validation allows low-privileged users to perform actions reserved for higher-level administrators, effectively elevating their capabilities within the system context.
To mitigate this vulnerability, immediate patching to version 0.1.26 or later is required as it addresses these authorization gaps. In environments where upgrading is not immediately feasible, network-level controls such as Web Application Firewalls can be configured to restrict access to efficiency-related API endpoints based on user role headers if available. Additionally, developers should implement strict server-side authorization checks for all sensitive routes, ensuring that every request validates the caller's permissions against a centralized policy engine before processing data retrieval or modification operations. Regular security audits and penetration testing focused on broken object level authorization are recommended to detect similar flaws in other parts of the application architecture.