CVE-2025-6920 in ai-inference-server
Summary
by MITRE • 07/01/2025
A flaw was found in the authentication enforcement mechanism of a model inference API in ai-inference-server. All /v1/* endpoints are expected to enforce API key validation. However, the POST /invocations endpoint failed to do so, resulting in an authentication bypass. This vulnerability allows unauthorized users to access the same inference features available on protected endpoints, potentially exposing sensitive functionality or allowing unintended access to backend resources.
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Analysis
by VulDB Data Team • 07/03/2025
The vulnerability described in CVE-2025-6920 represents a critical authentication bypass flaw within the ai-inference-server model inference API framework. This issue specifically targets the authentication enforcement mechanism that governs access to various API endpoints, creating a dangerous gap in the security architecture that could be exploited by malicious actors. The flaw manifests in the server's inconsistent implementation of authentication controls across different endpoint paths, where the POST /invocations endpoint fails to properly validate API keys despite other endpoints in the /v1/* namespace correctly enforcing this security measure. This inconsistency creates an unintended access vector that undermines the overall security posture of the inference server.
The technical nature of this vulnerability stems from the improper implementation of authentication controls within the API gateway layer of the ai-inference-server. According to CWE-306, this represents a failure to implement proper access control mechanisms, specifically in the area of API key validation. The flaw occurs at the endpoint level where the server's authentication enforcement logic is selectively applied, creating a scenario where certain high-privilege operations remain unprotected. The POST /invocations endpoint serves as a critical pathway for model inference requests, making it a prime target for unauthorized access. This vulnerability aligns with ATT&CK technique T1078.004 which describes valid accounts being used to access systems, but in this case the access bypass occurs through a misconfiguration rather than compromised credentials.
The operational impact of CVE-2025-6920 extends beyond simple unauthorized access to encompass potential data exposure, resource abuse, and service disruption. Unauthorized users who exploit this vulnerability can leverage the same inference capabilities available to legitimate authenticated users, potentially accessing sensitive model outputs, consuming excessive computational resources, or performing operations that could compromise the integrity of the inference system. The implications are particularly severe in enterprise environments where inference servers may process sensitive data or provide access to proprietary models. This vulnerability could enable attackers to perform unauthorized inference requests at scale, potentially leading to computational resource exhaustion or data leakage through model outputs. The attack surface is further expanded because the bypass affects the core functionality of the inference server, making it difficult to contain the impact.
Mitigation strategies for this vulnerability should focus on implementing comprehensive authentication enforcement across all API endpoints within the ai-inference-server. The immediate fix involves ensuring that the POST /invocations endpoint properly validates API keys and implements the same authentication controls as other /v1/* endpoints. Organizations should conduct thorough security reviews of all API endpoints to identify similar authentication bypass vulnerabilities and implement consistent access control policies. The solution should incorporate proper input validation and authentication checks at the API gateway level, ensuring that no endpoint can be accessed without proper authorization. Additionally, implementing rate limiting and monitoring for the /invocations endpoint can help detect and prevent abuse of the vulnerable functionality. Security teams should also consider implementing automated testing procedures that specifically target authentication controls to prevent similar issues from emerging in future deployments, aligning with security best practices outlined in NIST SP 800-53 and ISO/IEC 27001 standards for access control management.