CVE-2026-65105 in NemoClaw
Summary
by MITRE • 08/26/2026
NVIDIA NemoClaw for Linux contains a vulnerability in its inference server setup, where a remote attacker may access the inference service without authentication. A successful exploit of this vulnerability may lead to information disclosure and denial of service.
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Analysis
by VulDB Data Team • 08/26/2026
The NVIDIA NemoClaw framework serves as an integrated platform designed to streamline the development, deployment, and management of large language models and generative AI applications on Linux systems. Within this ecosystem, the inference server component is responsible for serving model predictions to client requests over a network interface. A critical security flaw has been identified in the configuration or implementation of this inference service setup, specifically regarding its authentication mechanisms. The vulnerability stems from an improper access control configuration that fails to enforce mandatory identity verification before granting access to sensitive AI endpoints. This oversight allows any remote actor with network connectivity to interact directly with the underlying model serving infrastructure without providing valid credentials.
From a technical perspective, this flaw represents a classic case of broken object level authorization or missing authentication at the API layer. The inference server likely exposes an endpoint that is intended for internal use or requires administrative privileges but has been inadvertently configured to accept unauthenticated requests from external networks. This misconfiguration bypasses standard security controls such as token validation, session management, and role-based access control policies. As a result, the system operates in a state where trust boundaries are effectively dissolved between the public network interface and the internal model processing engine. The absence of authentication checks means that the server processes requests from any source without verifying the identity or permissions of the requester, creating an open door for unauthorized interaction with the AI models hosted within NemoClaw.
The operational impact of this vulnerability is severe due to the nature of generative AI systems and their integration into enterprise workflows. First, information disclosure occurs as attackers can query the model directly, potentially extracting proprietary training data patterns, sensitive prompt engineering techniques, or confidential outputs generated for other users if multi-tenancy features are enabled. In some configurations, this access may also allow retrieval of system metadata, version details, and internal architecture specifics that aid in further exploitation. Second, denial of service is a significant risk because unauthenticated actors can flood the inference endpoint with resource-intensive queries. Since large language models require substantial computational resources for token generation, malicious users can exhaust CPU, GPU, or memory resources by sending continuous high-load requests, thereby degrading performance for legitimate users or causing complete system unavailability.
This vulnerability aligns closely with CWE-287 Improper Authentication and CWE-306 Missing Authentication for Critical Function within the Common Weakness Enumeration framework. It also maps to MITRE ATT&CK techniques related to Initial Access via Unsecured Credentials and potentially Impact through Resource Exhaustion if exploited for denial of service purposes. The lack of authentication in a cloud-native or containerized AI deployment environment amplifies the risk, as these services are often exposed directly to user-facing networks without adequate gateway-level protections.
To mitigate this vulnerability, immediate remediation steps should focus on enforcing strict access controls at multiple layers. Administrators must configure the inference server to require valid authentication tokens for all incoming requests, ensuring that only authorized service accounts or users can interact with the model endpoints. Network segmentation strategies should be implemented to restrict direct external access to the inference ports, placing them behind a secure API gateway or reverse proxy that handles authentication and authorization before forwarding traffic to NemoClaw components. Additionally, enabling rate limiting on these endpoints will help mitigate potential denial of service attempts by unauthenticated actors while stronger identity management solutions are deployed. Regular security audits and configuration reviews should be conducted to ensure that no other services within the NemoClaw suite suffer from similar misconfigurations regarding access control policies.