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Flowise, a tool for building AI workflows using a drag-and-drop interface, had a security flaw in versions before 3.1.0 where password reset links were sent over HTTP (unencrypted internet connection) instead of HTTPS (encrypted connection). This allowed attackers on the same network, such as on public Wi-Fi, to intercept these reset links through a MITM attack (man-in-the-middle attack, where someone secretly reads messages between two parties) and take over user accounts.

Flowise, a tool for building customized AI workflows with a drag-and-drop interface, had a security flaw in versions before 3.1.0 that let attackers bypass authentication (skip the login process) and steal OAuth 2.0 access tokens (credentials that grant permission to access other services). Attackers could access public chatflow configuration endpoints (URLs that show workflow settings) to find OAuth credential identifiers and use them to obtain valid access tokens without needing to log in.

Flowise, a tool with a drag-and-drop interface for building customized AI workflows, had security flaws in its request-blocking system before version 3.1.0. These flaws allowed attackers to bypass security protections through DNS Rebinding (a technique where a domain name's IP address changes between security checks) or by exploiting a default configuration that didn't enforce any blocklist, potentially enabling SSRF attacks (Server-Side Request Forgery, where an attacker tricks a server into making unwanted requests).

Flowise, a tool with a drag-and-drop interface for building AI workflows, had a Server-Side Request Forgery vulnerability (SSRF, where an attacker tricks a server into making requests to unintended locations) in versions before 3.1.0 that let unauthenticated attackers force the server to send requests to internal or external systems by injecting malicious instructions into prompt templates. This could allow attackers to explore internal networks and steal data.

Flowise, a tool for building custom AI workflows through a visual interface, had a vulnerability in versions before 3.1.0 where authenticated users could bypass SSRF protection (a security control that prevents the application from making requests to internal networks). The issue occurred because the Custom Function feature blocked some ways of making network requests but left others unprotected, allowing attackers to potentially access sensitive internal resources like cloud provider metadata services.

Flowise, a tool with a drag-and-drop interface for building customized AI workflows, had a vulnerability before version 3.1.0 where attackers could upload malicious JavaScript files by changing file type settings, even though the user interface normally blocks such uploads. These uploaded files could act as web shells (programs that give attackers control over the server), potentially allowing remote code execution (RCE, where an attacker runs commands on a system they don't own).

Flowise, a tool for building customized AI workflows through a drag-and-drop interface, had a security flaw in versions before 3.1.0 where attackers could inject malicious data during account registration. This JSON injection (inserting unauthorized code into data fields) vulnerability allowed unauthenticated users to manipulate important metadata like ownership and user roles, potentially breaking security boundaries in systems that host multiple separate organizations.

Flowise, a tool for building customized LLM (large language model) flows through a visual drag-and-drop interface, has a vulnerability in versions before 3.1.0 where an API endpoint exposes sensitive data like API keys and authorization headers without requiring authentication. An attacker who knows only a chatflow UUID (a unique identifier) can steal credentials and other sensitive information from the system.

Flowise is a tool with a drag-and-drop interface for building customized large language model flows. Before version 3.1.0, it had a remote code execution vulnerability (RCE, where an attacker can run commands on a system they don't own) in AirtableAgent.ts because user input was directly inserted into Python code without sanitization (cleaning to remove harmful content), allowing attackers to inject malicious code through the question parameter.