CVE-2026-70477

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, a prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed Pyodide environment. The specific flaw exists within the run method of the CSV_Agents class, where untrusted data is used to construct an LLM prompt and the resulting pythonCode is validated by validatePythonCodeForDataFrame before execution. An attacker can leverage this to execute arbitrary code in the context of the service account. This issue is fixed in 3.1.3.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, a prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed Pyodide environment. The specific flaw exists within the run method of the CSV_Agents class, where untrusted data is used to construct an LLM prompt and the resulting pythonCode is validated by validatePythonCodeForDataFrame before execution. An attacker can leverage this to execute arbitrary code in the context of the service account. This issue is fixed in 3.1.3.

-- ABSTRACT -------------------------------------

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

-- VULNERABILITY DETAILS ------------------------

• Version tested: 3.1.1
• Installer file: https://github.com/FlowiseAI/Flowise (npm install [email protected])
• Platform tested: Ubuntu 25.10

A prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed pyodide environment. An attacker can leverage this to execute arbitrary code in the context of the user running the server.

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

The specific flaw exists within the run method of the CSV_Agents class. The issue results from insufficient input sanitization when using untrusted data to construct an LLM prompt. An attacker can leverage this vulnerability to execute code in the context of the service account.

Analysis

When a user makes a query against a chatflow using the CSV Agent node, the run method of the CSV_Agents class is called. This method reads the CSV file, loads a pyodide environment, and uses pandas to extract column names and data types into a dictionary. It then constructs a system prompt using that dictionary and the user's input, and sends this prompt to a configured LLM. The LLM response is stored in a variable named pythonCode. The method then attempts to validate this value using validatePythonCodeForDataFrame from packages/components/src/pythonCodeValidator.ts before evaluating it in pyodide.

The validator relies on a static regex blocklist. It can be bypassed using obfuscation techniques including string concatenation to reconstruct forbidden identifiers, chr() encoding, aliasing of dangerous builtins, __getattribute__ with concatenated attribute names, frame object inspection, MRO traversal, df.query() expression evaluation, and decorator syntax to invoke exec indirectly. Furthermore, pyodide is not sandboxed from the host operating system, so any Python code that passes the validator is executed with full access to OS interfaces.

From packages/components/nodes/agents/CSVAgent/CSVAgent.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 // user-controlled input substituted into prompt
    }
    const res = await chain.call(inputs, [loggerHandler, ...callbacks])
    pythonCode = res?.text // LLM response assigned to pythonCode
    pythonCode = pythonCode.replace(/^```[a-z]+\n|\n```$/gm, '')
}

let finalResult = ''
if (pythonCode) {
    const validation = validatePythonCodeForDataFrame(pythonCode) // blocklist validation applied
    if (!validation.valid) {
        throw new Error(
            `Generated code was rejected for security reasons (${
                validation.reason ?? 'unsafe construct'
            }). Please rephrase your question to use only pandas DataFrame operations.`
        )
    }
    try {
        const code = `import pandas as pd\nimport numpy as np\n${pythonCode}`
        finalResult = await pyodide.runPythonAsync(code) // executed in unsandboxed pyodide
    } catch (error) {
        throw new Error(`Sorry, I'm unable to find answer for question: "${input}" using following code: "${pythonCode}"`)
    }
}

An unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may use prompt injection to cause the LLM to respond with a malicious Python script. An authenticated attacker may instead configure a chatflow that points to an attacker-controlled…

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, a prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed Pyodide environment. The specific flaw exists within the run method of the CSV_Agents class, where untrusted data is used to construct an LLM prompt and the resulting pythonCode is validated by validatePythonCodeForDataFrame before execution. An attacker can leverage this to execute arbitrary code in the context of the service account. This issue is fixed in 3.1.3.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, a prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed Pyodide environment. The specific flaw exists within the run method of the CSV_Agents class, where untrusted data is used to construct an LLM prompt and the resulting pythonCode is validated by validatePythonCodeForDataFrame before execution. An attacker can leverage this to execute arbitrary code in the context of the service account. This issue is fixed in 3.1.3.

-- ABSTRACT -------------------------------------

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

-- VULNERABILITY DETAILS ------------------------

• Version tested: 3.1.1
• Installer file: https://github.com/FlowiseAI/Flowise (npm install [email protected])
• Platform tested: Ubuntu 25.10

A prompt injection sent to a chatflow using a CSV Agent node can cause the LLM to respond with a malicious Python script that bypasses the blocklist validator and executes in an unsandboxed pyodide environment. An attacker can leverage this to execute arbitrary code in the context of the user running the server.

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

The specific flaw exists within the run method of the CSV_Agents class. The issue results from insufficient input sanitization when using untrusted data to construct an LLM prompt. An attacker can leverage this vulnerability to execute code in the context of the service account.

Analysis

When a user makes a query against a chatflow using the CSV Agent node, the run method of the CSV_Agents class is called. This method reads the CSV file, loads a pyodide environment, and uses pandas to extract column names and data types into a dictionary. It then constructs a system prompt using that dictionary and the user's input, and sends this prompt to a configured LLM. The LLM response is stored in a variable named pythonCode. The method then attempts to validate this value using validatePythonCodeForDataFrame from packages/components/src/pythonCodeValidator.ts before evaluating it in pyodide.

The validator relies on a static regex blocklist. It can be bypassed using obfuscation techniques including string concatenation to reconstruct forbidden identifiers, chr() encoding, aliasing of dangerous builtins, __getattribute__ with concatenated attribute names, frame object inspection, MRO traversal, df.query() expression evaluation, and decorator syntax to invoke exec indirectly. Furthermore, pyodide is not sandboxed from the host operating system, so any Python code that passes the validator is executed with full access to OS interfaces.

From packages/components/nodes/agents/CSVAgent/CSVAgent.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 // user-controlled input substituted into prompt
    }
    const res = await chain.call(inputs, [loggerHandler, ...callbacks])
    pythonCode = res?.text // LLM response assigned to pythonCode
    pythonCode = pythonCode.replace(/^```[a-z]+\n|\n```$/gm, '')
}

let finalResult = ''
if (pythonCode) {
    const validation = validatePythonCodeForDataFrame(pythonCode) // blocklist validation applied
    if (!validation.valid) {
        throw new Error(
            `Generated code was rejected for security reasons (${
                validation.reason ?? 'unsafe construct'
            }). Please rephrase your question to use only pandas DataFrame operations.`
        )
    }
    try {
        const code = `import pandas as pd\nimport numpy as np\n${pythonCode}`
        finalResult = await pyodide.runPythonAsync(code) // executed in unsandboxed pyodide
    } catch (error) {
        throw new Error(`Sorry, I'm unable to find answer for question: "${input}" using following code: "${pythonCode}"`)
    }
}

An unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may use prompt injection to cause the LLM to respond with a malicious Python script. An authenticated attacker may instead configure a chatflow that points to an attacker-controlled…