Vulnerabilidades de Flowiseai

O Flowise anterior à versão 3.1.2 contém múltiplas vulnerabilidades de injeção de comando do sistema operacional (OS command injection) no recurso Custom MCP Server, devido a uma validação incompleta de flags de comando e a um bypass por expressão regular nas restrições de acesso a arquivos locais. Um atacante com uma conta no Flowise de qualquer papel, ou com acesso à API com permissões de visualização/atualização para chatflows, pode configurar um servidor MCP malicioso para contornar a lista de bloqueio validateCommandFlags (por exemplo, 'docker build' não é bloqueado, e 'npx --yes' não é bloqueado enquanto apenas '-y' é) e as verificações validateArgsForLocalFileAccess, resultando na execução de comandos arbitrários no host do Flowise.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, POST /api/v1/node-custom-function lacks route-level authorization, allowing any authenticated user or API key to submit arbitrary JavaScript to the Custom JS Function node. When E2B_APIKEY is not configured — the common deployment case — Flowise executes this code inside a NodeVM sandbox. This sandbox can be escaped, allowing an attacker to reach the host process object and execute system commands via child_process. The result is authenticated remote code execution on the Flowise server host. This issue has been patched in version 3.1.2.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, a mass assignment vulnerability exists in the assistant update endpoint of FlowiseAI. The endpoint allows authenticated users to modify server-controlled properties such as workspaceId, createdDate, and updatedDate when updating an assistant resource. Due to missing server-side validation and authorization checks, an attacker can manipulate the workspaceId field and reassign assistants to arbitrary workspaces. This breaks tenant isolation in multi-workspace environments. This issue has been patched in version 3.1.2.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, the checkBasicAuth endpoint validates credentials in plaintext without rate limiting and with direct comparison. This issue has been patched in version 3.1.2.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, a mass assignment vulnerability exists in the variable update endpoint of FlowiseAI. The endpoint allows authenticated users to modify server-controlled properties such as workspaceId, createdDate, and updatedDate when updating a variable resource. Due to missing server-side validation and authorization checks, an attacker can manipulate the workspaceId field and reassign variables to arbitrary workspaces. This behavior may break tenant isolation in multi-workspace environments. This issue has been patched in version 3.1.2.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, multiple tool implementations directly import and invoke raw HTTP clients (node-fetch, axios) instead of using the secured wrapper. These tools include (1) OpenAPIToolkit/OpenAPIToolkit.ts, (2) WebScraperTool/WebScraperTool.ts, (3) MCP/core.ts, and (4) Arxiv/core.ts. This vulnerability is fixed in 3.1.0.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the GraphCypherQAChain node forwards user-provided input directly into the Cypher query execution pipeline without proper sanitization. An attacker can inject arbitrary Cypher commands that are executed on the underlying Neo4j database, enabling data exfiltration, modification, or deletion. This vulnerability is fixed in 3.1.0.