CVE-2026-65088 in NemoClaw
Summary
by MITRE • 08/26/2026
NVIDIA NemoClaw contains a vulnerability where an attacker could cause invocation of process using visible sensitive information. A successful exploit of this vulnerability might lead to information disclosure.
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Analysis
by VulDB Data Team • 08/26/2026
The NVIDIA NemoClaw framework, designed to facilitate the development and deployment of generative AI applications on GPU-accelerated infrastructure, has been identified with a security flaw related to improper handling of sensitive data during process invocation. This vulnerability stems from an implementation error where critical configuration parameters or authentication credentials are exposed in plain text within visible system interfaces or logs when launching computational processes. In complex machine learning workflows involving multiple microservices and containerized components, such as those managed by NemoClaw, the visibility of these secrets can occur through command-line arguments passed to child processes, environment variable dumps, or verbose logging mechanisms that are inadvertently enabled during debugging or standard operational modes. The core technical flaw lies in the failure to sanitize or mask sensitive inputs before they are rendered accessible to users with access to process status information or system logs, creating a direct pathway for unauthorized data retrieval.
From an operational perspective, this vulnerability poses a significant risk of information disclosure within enterprise AI environments. Attackers who gain local access to the host machine or compromise a user account running NemoClaw services can inspect active processes and their associated arguments to extract API keys, database connection strings, cloud provider credentials, or internal network topology details embedded in command lines. This exposure undermines the principle of least privilege and compromises the confidentiality of the underlying infrastructure. The disclosed information can be leveraged for further attacks, including lateral movement within the cluster, unauthorized access to external data stores, or exfiltration of proprietary model weights and training datasets that are protected by these credentials.
This issue aligns with CWE-209, which describes the generation of an error message that includes sensitive information about the environment from which the error was generated, as well as CWE-798, concerning the use of hard-coded or visible default credentials in command-line arguments. Furthermore, within the MITRE ATT&CK framework, this vulnerability facilitates techniques associated with Credential Access and Discovery, specifically allowing adversaries to gather authentication data through local process inspection without needing elevated privileges initially. The impact is compounded by the fact that AI infrastructure often handles highly sensitive intellectual property and personal data, making even minor leaks potentially catastrophic for organizational security posture and regulatory compliance.
To mitigate this risk, it is imperative to implement strict input sanitization protocols within NemoClaw’s process management modules. Developers should ensure that sensitive parameters are never passed via command-line arguments but instead injected through secure environment variables or secret management systems like HashiCorp Vault or AWS Secrets Manager, which do not appear in standard process listings. Additionally, logging mechanisms must be configured to redact any fields identified as containing secrets before they are written to disk or displayed in console output. Organizations should also enforce strict access controls on the nodes running NemoClaw services and regularly audit logs for signs of credential exposure. Updating to patched versions of NVIDIA software that address this specific handling logic is essential, alongside adopting a zero-trust architecture where even internal service-to-service communications are encrypted and authenticated rigorously.