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FlowiseAI Flowise

Flowise (packages flowise and flowise-components) in versions <= 3.1.2 contain a sandbox escape in the vm2/@flowiseai/nodevm JavaScript sandbox. An authenticated user with access to the /api/v1/node-custom-function endpoint can escape the sandbox by supplying attacker-controlled executablePath and args parameters to puppeteer.launch(), which internally invokes child_process.spawn() outside the sandbox boundary. This allows execution of arbitrary OS commands as the Flowise process user (root in the official Docker image) and arbitrary host file disclosure via Chromium's file:// URL handling. In versions 3.0.8–3.1.2 exploitation requires ALLOW_BUILTIN_DEP=true; earlier versions are exploitable by default. Fixed in 3.1.3.

FlowiseAI Flowise

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, the SQLite Record Manager node in packages/components/nodes/recordmanager/SQLiteRecordManager/SQLiteRecordManager.ts accepted user-controlled additionalConfig and spread it after the intended database setting, allowing additionalConfig.database to overwrite the SQLite database path. An authenticated attacker using the published Docker image, which ran as root, could write a SQLite database to paths such as /etc/chromium/exploit.conf; by controlling the table name and namespace value, the attacker could place shell syntax into the database file and trigger execution when Puppeteer launched Chromium and sourced /etc/chromium/*.conf. This issue is fixed in version 3.1.3.

FlowiseAI Flowise

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, the CSVAgent node allowed users to provide Python code that is executed through pyodide; although a denylist blocked dangerous Python constructs, pandas.read_pickle() could deserialize a pickled payload and achieve code execution without matching the denied words. The affected file is flowise-components/nodes/agents/CSVAgent/CSVAgent.ts, where user-supplied customReadCSVFunc is evaluated as pd.${customReadCSVFunc}. An authenticated user who can create or modify a chatflow can add a CSV Agent, place a malicious read_pickle payload in the Additional Parameters, save the chatflow, and trigger /api/v1/prediction/<UUID> to execute commands. This issue is fixed in version 3.1.3.

FlowiseAI Flowise

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, the CSVAgent in packages/components/nodes/agents/CSVAgent/CSVAgent.ts extracted attacker-controlled CSV data with file.split(',').pop() and interpolated it directly into executable Python as base64_string = "${base64String}" before calling Pyodide. The validatePythonCodeForDataFrame() denylist only checked later LLM-generated code and did not validate this initial code block. An authenticated attacker could inject a closing quote followed by Python code, use Pyodide's js bridge to load Node.js child_process, and execute arbitrary operating system commands as root in the Flowise container. This issue is fixed in version 3.1.3.

FlowiseAI Flowise

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, executeJavaScriptCode() accepted caller-provided nodeVMOptions and merged them over the default NodeVM security settings in packages/components/src/utils.ts. An authenticated attacker reaching packages/server/src/routes/node-custom-functions/index.ts could run a custom function that imported flowise-components/dist/src/utils.js, called executeJavaScriptCode() again with nodeVMOptions.require.builtin set to allow all built-in modules, and then required child_process to execute arbitrary system commands as root on the Flowise server. This issue is fixed in version 3.1.3.

FlowiseAI Flowise

Flowise is a drag-and-drop user interface for building customized large language model (LLM) flows. Prior to version 3.1.3, several custom-tool components — AgentAsTool, ChatflowTool, and ExecuteFlow — ran code in the in-process  vm2  sandbox. To build that code, they inserted a user-controlled  baseURL  value straight into the JavaScript source, for example  const url = "${baseURL}/..."; . The only check on  baseURL  was  isValidURL , but a valid-looking URL can still contain characters that break out of a code string. An authenticated user could craft a  baseURL  that passed this check, closed the surrounding string, and injected their own JavaScript into the sandboxed script (code injection, CWE-94). The  vm2  sandbox runs in the same Node.js process as Flowise and exposes risky dependencies. As a result, the injected code could escape the sandbox and run arbitrary code on the Flowise server as the Flowise process user. Exploitation only requires an authenticated session. The issue is fixed in version 3.1.3, which passes the URL to the sandbox as data instead of inserting it into code and adds stricter URL validation.

FlowiseAI Flowise · FlowiseAI flowise-components

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, Flowise record manager and agent memory nodes allowed users to set arbitrary TypeORM DataSource options through the additionalConfig input in packages/components/nodes/recordmanager/MySQLRecordManager/MySQLrecordManager.ts, packages/components/nodes/recordmanager/PostgresRecordManager/PostgresRecordManager.ts, packages/components/nodes/recordmanager/SQLiteRecordManager/SQLiteRecordManager.ts, packages/components/nodes/memory/AgentMemory/MySQLAgentMemory/MySQLAgentMemory.ts, and packages/components/nodes/memory/AgentMemory/AgentMemory.ts. TypeORM DataSource options such as entities, subscribers, and migrations can load local JavaScript files, allowing an authenticated user to execute arbitrary code on the server by uploading a JavaScript payload and referencing it from additionalConfig.entities. This issue is fixed in version 3.1.3.