PatchFirst · Latest vulnerabilities
Vulnerabilities published for flowise-components in the last 30 days: 0. Listed in the KEV catalogue: 0.
Vulnerabilities: 31
Flowise before 3.1.3 contains a regex-based Python code validator bypass in CSV and Airtable Agent nodes that allows unauthenticated attackers to inject malicious code via prompt injection. Attackers can exploit unblocked pandas functions like pd.read_json() to exfiltrate datasets, perform SSRF against internal services, or achieve code execution through the unauthenticated prediction API.
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_
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 fix
## Summary Flowise on current `main` allows an authenticated user with `documentStores:preview-process` permission to trigger the `S3 Directory` document loader with attacker-controlled S3 object keys. The loader joins each returned S3 key with a temporary directory using
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, Flowise validatePythonCodeForDataFrame in packages/components/src/pythonCodeValidator.ts can be bypassed with Unicode homoglyph identifiers, allowing arbitrary Python execution inside Pyodide and full OS command execution on the Flowise host via Pyodide js module interop. The validator gates pyodide.runPythonAsync in packages/components/nodes/agents/CSVAgent/CSVAgent.ts and packages/components/nodes/agents/AirtableAgent/AirtableAgent.ts with an ASCII word-boundary blacklist. JavaScript regex word boundaries are ASCII-only, while Python 3 N
Prior to 3.1.3, Flowise CSVAgent interpolates an attacker-controlled segment of the csvFile data URI directly into a Python source-code template that is then executed by Pyodide. Because Pyodide is loaded with the default js bridge to globalThis, which on Node.js exposes eval and dynamic import, the attacker can break out of the Python string literal, hand a JavaScript string to js.eval, dynamically import Node built-in modules such as fs and child_process, and execute arbitrary file I/O or OS commands as the Flowise process. The two validator paths around this code, validatePythonCodeForDataFrame and validateCustomReadCSVFunction, are never
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, the mitigation for CVE-2025-8943 blocked -y and --yes flags on npx, but packages/components/nodes/tools/MCP/core.ts denied only PATH, LD_LIBRARY_PATH, DYLD_LIBRARY_PATH, and NODE_OPTIONS by exact environment-variable name. Because npm reads configuration from npm_config_* variables, setting npm_config_yes=true reproduced --yes behavior without using a blocked flag, causing npx to auto-install and execute the named package when a Custom MCP server launched. This issue is fixed in version 3.1.3.
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
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.${customReadCSVFunc}. An authenticated user who can create or modify a chatflow can add a CSV Agent, place a malicious read_pickle payload in the Ad
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, the CSVAgent in packages/components/nodes/agents/CSVAgent/CSVAgent.ts extracted attacker-controlled CSV data with file.split(',').pop() and interpolated it directly into executable Python as base64_string = "${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
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 is
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
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. T
Flowise before 3.1.0 contains a path traversal vulnerability in Faiss and SimpleStore vector store implementations that accept unsanitized basePath parameters from authenticated users. Attackers with valid API tokens can write vector store data to arbitrary filesystem locations, potentially enabling code execution or data exfiltration.
Flowise before 3.1.0 contains a server-side request forgery vulnerability in the Execute Flow node that allows attackers to bypass security validation by providing intranet addresses through the base URL field. Attackers can initiate HTTP requests to internal network addresses, access cloud metadata, and enumerate internal services by exploiting the missing secureFetch verification in httpSecurity.ts.
Flowise before 3.1.2 contains multiple OS command injection vulnerabilities in the Custom MCP Server feature due to incomplete command-flag validation and a regex bypass in local file access restrictions. An attacker with a Flowise account of any role, or API access with view/update permissions for chatflows, can configure a malicious MCP server to bypass the validateCommandFlags blocklist (for example, 'docker build' is not blocked, and 'npx --yes' is not blocked while only '-y' is) and the validateArgsForLocalFileAccess checks, resulting in execution of arbitrary commands on the Flowise host.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, multiple tool implementations directly import and invoke raw HTTP clients (node-fetch, axios) instead of using the secured wrapper. These tools include (1) OpenAPIToolkit/OpenAPIToolkit.ts, (2) WebScraperTool/WebScraperTool.ts, (3) MCP/core.ts, and (4) Arxiv/core.ts. 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, the GraphCypherQAChain node forwards user-provided input directly into the Cypher query execution pipeline without proper sanitization. An attacker can inject arbitrary Cypher commands that are executed on the underlying Neo4j database, enabling data exfiltration, modification, or deletion. 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, the specific flaw exists within the run method of the CSV_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. An attacker can leverage this vulnerability to execute code in the context of the user running the server. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the Flowise server. Th
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the specific flaw exists within the run method of the Airtable_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the Airtable Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the flowise server. This vulnerability is fixed in 3.1.0.
Check your installed stack and versions against the current vulnerability feeds.