CVE-2026-41138

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, 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.

Description

Summary

“AirtableAgent” is an agent function provided by FlowiseAI that retrieves search results by accessing private datasets from airtable.com. “AirtableAgent” uses Python, along with Pyodide and Pandas, to get and return results.

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.

The point is that an attacker can bypass the intended behavior of the LLM and trigger Remote Code Execution through a simple prompt injection.

About Airtable

The airtable.ts function retrieves and processes user datasets stored on Airtable.com through its API.

The usage of Airtable is as shown in the image above. After creating a Chatflow like above, you can ask data-related questions using prompts and receive answers.

Details

// packages/components/nodes/agents/AirtableAgent/AirtableAgent.ts
  let base64String = Buffer.from(JSON.stringify(airtableData)).toString('base64')

  const loggerHandler = new ConsoleCallbackHandler(options.logger)
  const callbacks = await additionalCallbacks(nodeData, options)

  const pyodide = await LoadPyodide()

  // First load the csv file and get the dataframe dictionary of column types
  // For example using titanic.csv: {'PassengerId': 'int64', 'Survived': 'int64', 'Pclass': 'int64', 'Name': 'object', 'Sex': 'object', 'Age': 'float64', 'SibSp': 'int64', 'Parch': 'int64', 'Ticket': 'object', 'Fare': 'float64', 'Cabin': 'object', 'Embarked': 'object'}
  let dataframeColDict = ''
  try {
      const code = `import pandas as pd
import base64
import json

base64_string = "${base64String}"

decoded_data = base64.b64decode(base64_string)

json_data = json.loads(decoded_data)

df = pd.DataFrame(json_data)
my_dict = df.dtypes.astype(str).to_dict()
print(my_dict)
json.dumps(my_dict)`
      dataframeColDict = await pyodide.runPythonAsync(code)
  } catch (error) {
      throw new Error(error)
  }

Airtable retrieves results by accessing datasets from airtable.com. When retrieving data, it is fetched as a JSON object encoded in base64. Then, when loading data, it is decoded and converted into an object using Python code.

// packages/components/nodes/agents/AirtableAgent/AirtableAgent.ts
let pythonCode = ''
if (dataframeColDict) {
    const chain = new LLMChain({
        llm: model,
        prompt: PromptTemplate.fromTemplate(systemPrompt),
        verbose: process.env.DEBUG === 'true' ? true : false
    })
    const inputs = {
        dict: dataframeColDict,
        question: input
    }
    const res = await chain.call(inputs, [loggerHandler, ...callbacks])
    pythonCode = res?.text
    // Regex to get rid of markdown code blocks syntax
    pythonCode = pythonCode.replace(/^```[a-z]+\n|\n```$/gm, '')
}

The dataframeColDict and input (user input received via prompt) are passed into the LLMChain function. After that, result of LLMChain is stored in the pythonCode variable.

// packages/components/nodes/agents/AirtableAgent/core.ts
export const systemPrompt = `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.

Question: {question}
Output Code:`

export const finalSystemPrompt = `You are given the question: {question}. You have an answer to the question: {answer}. Rephrase the answer into a standalone answer.
Standalone Answer:`
```…

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, 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.

Description

Summary

“AirtableAgent” is an agent function provided by FlowiseAI that retrieves search results by accessing private datasets from airtable.com. “AirtableAgent” uses Python, along with Pyodide and Pandas, to get and return results.

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.

The point is that an attacker can bypass the intended behavior of the LLM and trigger Remote Code Execution through a simple prompt injection.

About Airtable

The airtable.ts function retrieves and processes user datasets stored on Airtable.com through its API.

The usage of Airtable is as shown in the image above. After creating a Chatflow like above, you can ask data-related questions using prompts and receive answers.

Details

// packages/components/nodes/agents/AirtableAgent/AirtableAgent.ts
  let base64String = Buffer.from(JSON.stringify(airtableData)).toString('base64')

  const loggerHandler = new ConsoleCallbackHandler(options.logger)
  const callbacks = await additionalCallbacks(nodeData, options)

  const pyodide = await LoadPyodide()

  // First load the csv file and get the dataframe dictionary of column types
  // For example using titanic.csv: {'PassengerId': 'int64', 'Survived': 'int64', 'Pclass': 'int64', 'Name': 'object', 'Sex': 'object', 'Age': 'float64', 'SibSp': 'int64', 'Parch': 'int64', 'Ticket': 'object', 'Fare': 'float64', 'Cabin': 'object', 'Embarked': 'object'}
  let dataframeColDict = ''
  try {
      const code = `import pandas as pd
import base64
import json

base64_string = "${base64String}"

decoded_data = base64.b64decode(base64_string)

json_data = json.loads(decoded_data)

df = pd.DataFrame(json_data)
my_dict = df.dtypes.astype(str).to_dict()
print(my_dict)
json.dumps(my_dict)`
      dataframeColDict = await pyodide.runPythonAsync(code)
  } catch (error) {
      throw new Error(error)
  }

Airtable retrieves results by accessing datasets from airtable.com. When retrieving data, it is fetched as a JSON object encoded in base64. Then, when loading data, it is decoded and converted into an object using Python code.

// packages/components/nodes/agents/AirtableAgent/AirtableAgent.ts
let pythonCode = ''
if (dataframeColDict) {
    const chain = new LLMChain({
        llm: model,
        prompt: PromptTemplate.fromTemplate(systemPrompt),
        verbose: process.env.DEBUG === 'true' ? true : false
    })
    const inputs = {
        dict: dataframeColDict,
        question: input
    }
    const res = await chain.call(inputs, [loggerHandler, ...callbacks])
    pythonCode = res?.text
    // Regex to get rid of markdown code blocks syntax
    pythonCode = pythonCode.replace(/^```[a-z]+\n|\n```$/gm, '')
}

The dataframeColDict and input (user input received via prompt) are passed into the LLMChain function. After that, result of LLMChain is stored in the pythonCode variable.

// packages/components/nodes/agents/AirtableAgent/core.ts
export const systemPrompt = `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.

Question: {question}
Output Code:`

export const finalSystemPrompt = `You are given the question: {question}. You have an answer to the question: {answer}. Rephrase the answer into a standalone answer.
Standalone Answer:`
```…