Flowise: Password Reset Link Sent Over Unsecured HTTP
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the password reset functionality on cloud.flowiseai.com sends a reset password link over the unsecured HTTP protocol instead of HTTPS. This behavior introduces the risk of a man-in-the-middle (MITM) attack, where an attacker on the same network as the user (e.g., public Wi-Fi) can intercept the reset link and gain unauthorized access to the victim’s account. This vulnerability is fixed in 3.1.0.
Flowise: Unauthenticated OAuth 2.0 Access Token Disclosure via Public Chatflow
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, Flowise contains an authentication bypass vulnerability that allows an unauthenticated attacker to obtain OAuth 2.0 access tokens associated with a public chatflow. By accessing a public chatflow configuration endpoint, an attacker can retrieve internal workflow data, including OAuth credential identifiers, which can then be used to refresh and obtain valid OAuth 2.0 access tokens without authentication. This vulnerability is fixed in 3.1.0.
Flowise: APIChain Prompt Injection SSRF in GET/POST API Chains
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, a Server-Side Request Forgery (SSRF) vulnerability exists in FlowiseAI's POST/GET API Chain components that allows unauthenticated attackers to force the server to make arbitrary HTTP requests to internal and external systems. By injecting malicious prompt templates, attackers can bypass the intended API documentation constraints and redirect requests to sensitive internal services, potentially leading to internal network reconnaissance and data exfiltration. 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 core security wrappers (secureAxiosRequest and secureFetch) intended to prevent Server-Side Request Forgery (SSRF) contain multiple logic flaws. These flaws allow attackers to bypass the allow/deny lists via DNS Rebinding (Time-of-Check Time-of-Use) or by exploiting the default configuration which fails to enforce any deny list. This vulnerability is fixed in 3.1.0.
Flowise: SSRF Protection Bypass via Unprotected Built-in HTTP Modules in Custom Function Sandbox
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, a Server-Side Request Forgery (SSRF) protection bypass vulnerability exists in the Custom Function feature. While the application implements SSRF protection via HTTP_DENY_LIST for axios and node-fetch libraries, the built-in Node.js http, https, and net modules are allowed in the NodeVM sandbox without equivalent protection. This allows authenticated users to bypass SSRF controls and access internal network resources (e.g., cloud provider metadata services) This vulnerability is fixed in 3.1.0.
Flowise: File Upload Validation Bypass in createAttachment
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the Chatflow configuration file upload settings can be modified to allow the application/javascript MIME type. This lets an attacker upload .js files even though the frontend doesn’t normally allow JavaScript uploads. This enables attackers to persistently store malicious Node.js web shells on the server, potentially leading to Remote Code Execution (RCE). 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, Flowise is vulnerable to a critical unauthenticated remote command execution (RCE) vulnerability. It can be exploited via a parameter override bypass using the FILE-STORAGE:: keyword combined with a NODE_OPTIONS environment variable injection. This allows for the execution of arbitrary system commands with root privileges within the containerized Flowise instance, requiring only a single HTTP request and no authentication or knowledge of the instance. This vulnerability is fixed in 3.1.0.
Flowise: Improper Mass Assignment in Account Registration Enables Unauthorized Organization Association
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, an improper mass assignment (JSON injection) vulnerability in the account registration endpoint of Flowise Cloud allows unauthenticated attackers to inject server-managed fields and nested objects during account creation. This enables client-controlled manipulation of ownership metadata, timestamps, organization association, and role mappings, breaking trust boundaries in a multi-tenant environment. This vulnerability is fixed in 3.1.0.
Flowise: Sensitive Data Leak in public-chatbotConfig
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, /api/v1/public-chatbotConfig/:id ep exposes sensitive data including API keys, HTTP authorization headers and internal configuration without any authentication. An attacker with knowledge just of a chatflow UUID can retrieve credentials stored in password type fields and HTTP headers, leading to credential theft and more. This vulnerability is fixed in 3.1.0.
Flowise: Code Injection in CSVAgent leads to Authenticated RCE
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, The CSVAgent allows providing a custom Pandas CSV read code. Due to lack of sanitization, an attacker can provide a command injection payload that will get interpolated and executed by the server. This vulnerability is fixed in 3.1.0.
Flowise: Remote code execution vulnerability in AirtableAgent.ts caused by lack of input verification when using Pandas.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, there is a remote code execution vulnerability in AirtableAgent.ts caused by lack of input verification when using Pandas. The user’s input is directly applied to the question parameter within the prompt template and it is reflected to the Python code without any sanitization. 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, due to unsafe serialization of stdio commands in the MCP adapter, an authenticated attacker can add an MCP stdio server with an arbitrary command, achieving command execution. The vulnerability lies in a bug in the input sanitization from the “Custom MCP” configuration in http://localhost:3000/canvas - where any user can add a new MCP, when doing so - adding a new MCP using stdio, the user can add any command, even though your code have input sanitization checks such as validateCommandInjection and validateArgsForLocalFileAccess, and a lis