CVE-2026-91933 in Flowise
Summary
by MITRE • 09/15/2026
Flowise before 3.1.4 fails to enforce workspace-level authorization checks in openai-realtime endpoints, allowing authenticated users to access tools from ChatFlows in other workspaces by supplying an unscoped chatflowid. Attackers can invoke GET and POST requests to retrieve tool definitions and execute tools from victim workspaces, triggering external side effects and accessing sensitive tool outputs.
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Analysis
by VulDB Data Team • 09/15/2026
The vulnerability identified in Flowise versions prior to 3.1.4 represents a critical failure in access control mechanisms within the openai-realtime endpoints. This flaw stems from an insufficient enforcement of workspace-level authorization checks when processing requests for ChatFlow tools. In a multi-tenant architecture, workspaces are designed to provide logical isolation between different user groups or projects, ensuring that resources such as chat flows and their associated tools remain accessible only to authorized entities within the designated boundary. However, in this specific implementation, the system fails to validate whether the requesting authenticated user possesses the necessary permissions for the workspace containing the target ChatFlow. Instead of verifying the relationship between the user's session context and the resource's ownership or group membership, the application relies solely on the presence of a valid authentication token and the structural validity of the provided chatflowid parameter. This oversight allows any authenticated user to bypass isolation boundaries by simply supplying an unscoped or arbitrary chatflow identifier that belongs to another workspace.
From a technical perspective, this vulnerability enables unauthorized actors to interact with tools defined in victim workspaces through standard HTTP GET and POST requests. By manipulating the input parameters sent to the openai-realtime endpoints, attackers can retrieve detailed definitions of these external tools, which may include sensitive configuration data, API keys, or internal logic descriptions that were not intended for public consumption. More critically, the attacker can execute these tools remotely. Since many AI agent frameworks like Flowise are designed to integrate with external services and APIs, executing a tool often results in side effects such as sending emails, querying databases, modifying cloud resources, or triggering webhooks. The ability to invoke these actions without proper authorization transforms what might otherwise be an information disclosure issue into a severe integrity and availability risk, potentially leading to data exfiltration, unauthorized transactions, or disruption of services belonging to other tenants within the same Flowise instance.
The operational impact of this vulnerability is significant, particularly in environments where multiple teams or clients share a single deployment of Flowise for AI application development. The lack of workspace-level isolation means that trust boundaries are effectively nullified at the API layer. An attacker with low-privileged access can escalate their capabilities by leveraging tools from high-security workspaces, such as those handling financial data or proprietary intellectual property. This could result in direct compromise of downstream systems connected to these tools, leading to broader network infiltration if the compromised tool interacts with internal infrastructure. Furthermore, because the vulnerability affects both retrieval and execution phases, it facilitates reconnaissance followed by exploitation in a single attack chain, reducing the complexity required for an adversary to achieve their objectives.
This issue aligns closely with CWE-284 Improper Access Control, specifically reflecting failures in enforcing proper authorization checks on resources based on user identity or role. It also maps directly to MITRE ATT&CK technique T1530 Data from Cloud Storage Object Misconfiguration if the tools expose sensitive data, and more critically to T1657 Exfiltration over C2 Channel when used for data theft via tool outputs. To mitigate this risk, organizations must immediately upgrade Flowise to version 3.1.4 or later where these authorization checks have been corrected. In cases where immediate upgrading is not feasible, administrators should implement strict network-level controls such as WAF rules that restrict access to the openai-realtime endpoints based on IP reputation or known malicious patterns, although this is a less robust defense than fixing the underlying code logic. Additionally, reviewing and tightening IAM policies within any integrated identity providers can help ensure that only explicitly authorized users have API access, adding an additional layer of verification before requests reach the vulnerable endpoint.