CVE-2026-65090 in NemoClaw
Summary
by MITRE • 08/25/2026
NVIDIA NemoClaw for Linux contains a vulnerability in its NIM management component, where an attacker could cause OS command injection. A successful exploit of this vulnerability might lead to code execution, data tampering, information disclosure, and denial of service.
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Analysis
by VulDB Data Team • 08/25/2026
The NVIDIA NemoClay platform serves as a comprehensive framework for building, customizing, and deploying generative AI models at scale within enterprise environments. Within the Linux distribution of this software suite, the Network Interface Manager (NIM) component plays a critical role in orchestrating model inference services and managing resource allocation across distributed systems. A significant security flaw has been identified in this management interface that allows for OS command injection attacks. This vulnerability stems from insufficient input validation within the NIM API endpoints or configuration handlers, where user-supplied data is improperly sanitized before being passed to underlying system shell commands. Because the NIM component typically operates with elevated privileges to manage hardware resources and network configurations, any successful exploitation of this flaw grants an attacker a high degree of control over the host operating system.
The technical nature of this vulnerability involves the injection of arbitrary shell metacharacters into input fields that are subsequently interpreted by the OS command interpreter. An attacker who can interact with the NIM management interface, whether through authenticated API calls or potentially unauthenticated endpoints depending on the specific deployment configuration and version, can craft malicious payloads that bypass intended logic. These payloads allow for the execution of arbitrary commands under the context of the service account running the NIM process. This mechanism effectively transforms a local application-level flaw into a remote code execution vector if the management interface is exposed to untrusted networks or compromised internal segments. The lack of strict whitelisting or parameterized command execution in certain legacy paths within the component facilitates this bypass, enabling attackers to chain multiple commands for complex attack scenarios.
The operational impact of exploiting this vulnerability is severe and multifaceted. Primary among these risks is full remote code execution on the affected host system. This allows an attacker to install backdoors, establish persistent access, or pivot further into the internal network infrastructure. Data tampering becomes a significant concern as attackers can modify model weights, configuration files, or inference logs to degrade service quality or inject malicious logic into AI outputs. Information disclosure is also highly probable, as command execution enables the reading of sensitive system files, environment variables containing credentials, and proprietary model artifacts stored on disk. Furthermore, denial of service conditions can be induced by consuming excessive system resources through spawned processes or by disrupting critical NIM services, thereby halting AI inference capabilities for dependent applications.
Mitigation strategies must address both immediate remediation and long-term architectural hardening. The most effective countermeasure is to apply the vendor-provided security patches that update the NemoClay software suite to a version where input validation in the NIM component has been rigorously strengthened. Organizations should ensure that their CI/CD pipelines integrate these updates automatically to maintain compliance with security baselines. In addition to patching, network segmentation is crucial; the management interfaces for AI infrastructure components like NIM should be isolated within dedicated administrative VLANs and protected by strict firewall rules that limit access to authorized IP addresses only. Implementing multi-factor authentication for all management portals adds an essential layer of defense against credential theft or brute-force attempts that might precede exploitation.
From a classification perspective, this vulnerability aligns with CWE-78 Improper Neutralization of Special Elements used in an OS Command and CWE-94 Improper Control of Generation of Code. In terms of adversary behavior mapping to the MITRE ATT&CK framework, this flaw facilitates techniques associated with Remote Service Exploitation (T1210) and Execution via Command and Scripting Interpreter (T1059). Security teams should monitor for anomalous process creation events originating from NIM service accounts, particularly those involving shell interpreters or network connections to external destinations. Regular vulnerability scanning focused on AI infrastructure components is recommended to detect unpatched instances before they can be leveraged by threat actors seeking to compromise high-value generative AI deployments.