CVE-2026-30824 in Flowise
Summary
by MITRE • 03/07/2026
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.0.13, the NVIDIA NIM router (/api/v1/nvidia-nim/*) is whitelisted in the global authentication middleware, allowing unauthenticated access to privileged container management and token generation endpoints. This issue has been patched in version 3.0.13.
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Analysis
by VulDB Data Team • 03/12/2026
The vulnerability identified as CVE-2026-30824 affects Flowise, a drag and drop interface for building customized large language model flows that has been widely adopted in enterprise environments for developing AI-powered applications. This security flaw resides in the authentication middleware implementation within the NVIDIA NIM router endpoints, specifically under the API path /api/v1/nvidia-nim/*. The issue stems from improper access control configuration where these critical endpoints are inadvertently whitelisted without proper authentication requirements, creating a significant security gap in the application's authorization framework. This vulnerability represents a classic case of insufficient authorization controls that allows attackers to bypass authentication mechanisms and gain access to privileged functionality.
The technical flaw manifests as a lack of proper authentication checks on the NVIDIA NIM router endpoints, which are designed to handle container management operations and token generation functions. These endpoints typically require elevated privileges to perform administrative tasks such as managing containerized AI models, generating access tokens for API consumption, and controlling resource allocation within the NVIDIA NIM environment. The whitelisting of these paths in the global authentication middleware means that any attacker who can reach the Flowise application can access these endpoints without providing valid credentials, effectively granting them unauthorized administrative access to the underlying container infrastructure. This vulnerability directly maps to CWE-285, which addresses insufficient authorization issues in software applications.
The operational impact of this vulnerability is substantial, particularly in enterprise environments where Flowise is deployed to manage sensitive AI workloads and proprietary data processing pipelines. An attacker exploiting this vulnerability could potentially compromise containerized AI models, steal or manipulate access tokens, and gain unauthorized access to underlying infrastructure resources. This could lead to data breaches, unauthorized model manipulation, and potential disruption of AI services. The vulnerability affects organizations that rely on Flowise for building AI applications and specifically use NVIDIA NIM integration, creating a risk vector that could be exploited to gain access to sensitive AI workloads and associated data. The exposure of container management capabilities also poses risks to the overall security posture of the AI infrastructure.
Organizations utilizing Flowise should immediately upgrade to version 3.0.13 or later to address this vulnerability, as the patch implements proper authentication requirements for the affected endpoints. Security teams should conduct comprehensive audits of their Flowise deployments to identify any potential unauthorized access that may have occurred prior to the patch deployment. Additional mitigations include implementing network-level access controls to restrict access to the affected API endpoints, monitoring for unauthorized access attempts, and ensuring that authentication mechanisms are properly configured across all API paths. This vulnerability aligns with ATT&CK technique T1078 which covers valid accounts and privilege escalation, as attackers can leverage the unauthorized access to perform administrative operations without detection. Organizations should also consider implementing zero-trust network principles to limit access to sensitive API endpoints and maintain detailed audit logs of all access attempts to these critical paths.