GitHub Security Advisory: GHSA-v38x-c887-992f
Package: flowise-components (NPM)
Severity: CRITICAL (CVSS 9.8)
CWE: CWE-77: Improper Neutralization of Special Elements used in a Command ('Command Injection'), CWE-184: Incomplete List of Disallowed Inputs
Vulnerable versions: <= 3.0.13
Patched in: 3.1.0

Summary: Flowise: Airtable_Agent Code Injection Remote Code Execution Vulnerability

Details:
ZDI-CAN-29412: FlowiseAI Flowise Airtable_Agent Code Injection Remote Code Execution Vulnerability

Trend Micro's Zero Day Initiative has identified a vulnerability affecting the following products:
Flowise - Flowise

-- VULNERABILITY DETAILS ------------------------
* Version tested:   3.0.13
* Installer file:   hxxps://github.com/FlowiseAI/Flowise
* Platform tested:  Ubuntu 25.10

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### Analysis

# FlowiseAI Flowise Airtable Agent pythonCode Prompt Injection Remote Code Execution Vulnerability

This vulnerability allows remote attackers to execute arbitrary code on affected installations of FlowiseAI Flowise. Authentication is not required to exploit this vulnerability.

The specific flaw exists within the run method of the Airtable_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. An attacker can leverage this vulnerability to execute code in the context of the user running the server.

### Product information

FlowiseAI Flowise version 3.0.13 (https://github.com/FlowiseAI/Flowise)

### Setup Instructions

npm install -g flowise@3.0.13
npx flowise start

### Root Cause Analysis

FlowiseAI Flowise is an open source low-code tool for developers to build customized large language model (LLM) applications and AI agents. It supports integration with various LLMs, data sources, and tools in order to facilitate rapid development and deployment of AI solutions. Flowise offers a web interface with a drag-and-drop editor, as well as an API, through an Express web server accessible over HTTP on port 3000/TCP.

One such feature of Flowise is the ability to create chatflows. Chatflows use a drag and drop editor that allow a developer to place nodes which control how an interaction with a LLM will occur. One such node is the Airtable Agent node that represents an Agent used to answer queries on a provided Airtable table.

When a user makes a query against a chatflow using the Airtable Agent node, the run method of the Airtable_Agents class will be called. This method will first request the contents of the Airtable table passed to the node and convert it to a base64 string. It will then set up a pyodide environment and create a python script to be executed in this environment. This python script will use pandas to extract the column names and their types from the Airtable table. The method will then create a system prompt for an LLM using this data as follows:

```
You are working with a pandas dataframe in Python. The name of the dataframe is df.

The columns and data types of a dataframe are given below as a Python dictionary with keys showing column names and values showing the data types.
{dict}

I will ask question, and you will output the Python code using pandas dataframe to answer my question. Do not provide any explanations. Do not respond with anything except the output of the code.

Security: Output ONLY pandas/numpy operations on the dataframe (df). Do not use import, exec, eval,