kp3 Marquee Post title test: 17 January 2024

Learning Web Pentesting With DVWA Part 3: Blind SQL Injection

Posted by KP-3မိသားစု |

In this article we are going to do the SQL Injection (Blind) challenge of DVWA.
OWASP describes Blind SQL Injection as:
"Blind SQL (Structured Query Language) injection is a type of attack that asks the database true or false questions and determines the answer based on the applications response. This attack is often used when the web application is configured to show generic error messages, but has not mitigated the code that is vulnerable to SQL injection.

When an attacker exploits SQL injection, sometimes the web application displays error messages from the database complaining that the SQL Query's syntax is incorrect. Blind SQL injection is nearly identical to normal , the only difference being the way the data is retrieved from the database. When the database does not output data to the web page, an attacker is forced to steal data by asking the database a series of true or false questions. This makes exploiting the SQL Injection vulnerability more difficult, but not impossible."

To follow along click on the SQL Injection (Blind) navigation link. You will be presented with a page like this:

Lets first try to enter a valid User ID to see what the response looks like. Enter 1 in the User ID field and click submit. The result should look like this:

Lets call this response as valid response for the ease of reference in the rest of the article. Now lets try to enter an invalid ID to see what the response for that would be. Enter something like 1337 the response would be like this:

We will call this invalid response. Since we know both the valid and invalid response, lets try to attack the app now. We will again start with a single quote (') and see the response. The response we got back is the one which we saw when we entered the wrong User ID. This indicates that our query is either invalid or incomplete. Lets try to add an or statement to our query like this:

' or 1=1-- -

This returns a valid response. Which means our query is complete and executes without errors. Lets try to figure out the size of the query output columns like we did with the sql injection before in Learning Web Pentesting With DVWA Part 2: SQL Injection.
Enter the following in the User ID field:

' or 1=1 order by 1-- -

Again we get a valid response lets increase the number to 2.

' or 1=1 order by 2-- -

We get a valid response again lets go for 3.

' or 1=1 order by 3-- -

We get an invalid response so that confirms the size of query columns (number of columns queried by the server SQL statement) is 2.
Lets try to get some data using the blind sql injection, starting by trying to figure out the version of dbms used by the server like this:

1' and substring(version(), 1,1) = 1-- -

Since we don't see any output we have to extract data character by character. Here we are trying to guess the first character of the string returned by version() function which in my case is 1. You'll notice the output returns a valid response when we enter the query above in the input field.
Lets examine the query a bit to further understand what we are trying to accomplish. We know 1 is the valid user id and it returns a valid response, we append it to the query. Following 1, we use a single quote to end the check string. After the single quote we start to build our own query with the and conditional statement which states that the answer is true if and only if both conditions are true. Since the user id 1 exists we know the first condition of the statement is true. In the second condition, we extract first character from the version() function using the substring() function and compare it with the value of 1 and then comment out the rest of server query. Since first condition is true, if the second condition is true as well we will get a valid response back otherwise we will get an invalid response. Since my the version of mariadb installed by the docker container starts with a 1 we will get a valid response. Lets see if we will get an invalid response if we compare the first character of the string returned by the version() function to 2 like this:

1' and substring(version(),1,1) = 2-- -

And we get the invalid response. To determine the second character of the string returned by the version() function, we will write our query like this:
1' and substring(version(),2,2) = 1-- -
We get invalid response. Changing 1 to 2 then 3 and so on we get invalid response back, then we try 0 and we get a valid response back indicating the second character in the string returned by the version() function is 0. Thus we have got so for 10 as the first two characters of the database version. We can try to get the third and fourth characters of the string but as you can guess it will be time consuming. So its time to automate the boring stuff. We can automate this process in two ways. One is to use our awesome programming skills to write a program that will automate this whole thing. Another way is not to reinvent the wheel and try sqlmap. I am going to show you how to use sqlmap but you can try the first method as well, as an exercise.
Lets use sqlmap to get data from the database. Enter 1 in the User ID field and click submit.
Then copy the URL from the URL bar which should look something like this
http://localhost:9000/vulnerabilities/sqli_blind/?id=1&Submit=Submit
Now open a terminal and type this command:

sqlmap --version

this will print the version of your sqlmap installation otherwise it will give an error indicating the package is not installed on your computer. If its not installed then go ahead and install it.
Now type the following command to get the names of the databases:

sqlmap -u "http://localhost:9000/vulnerabilities/sqli_blind/?id=1&Submit=Submit" --cookie="security=low; PHPSESSID=aks68qncbmtnd59q3ue7bmam30" -p id

Here replace the PHPSESSID with your session id which you can get by right clicking on the page and then clicking inspect in your browser (Firefox here). Then click on storage tab and expand cookie to get your PHPSESSID. Also your port for dvwa web app can be different so replace the URL with yours.
The command above uses -u to specify the url to be attacked, --cookie flag specifies the user authentication cookies, and -p is used to specify the parameter of the URL that we are going to attack.
We will now dump the tables of dvwa database using sqlmap like this:

sqlmap -u "http://localhost:9000/vulnerabilities/sqli_blind/?id=1&Submit=Submit" --cookie="security=low; PHPSESSID=aks68qncbmtnd59q3ue7bmam30" -p id -D dvwa --tables

After getting the list of tables its time to dump the columns of users table like this:

sqlmap -u "http://localhost:9000/vulnerabilities/sqli_blind/?id=1&Submit=Submit" --cookie="security=low; PHPSESSID=aks68qncbmtnd59q3ue7bmam30" -p id -D dvwa -T users --columns

And at last we will dump the passwords column of the users table like this:

sqlmap -u "http://localhost:9000/vulnerabilities/sqli_blind/?id=1&Submit=Submit" --cookie="security=low; PHPSESSID=aks68qncbmtnd59q3ue7bmam30" -p id -D dvwa -T users -C password --dump

Now you can see the password hashes.
As you can see automating this blind sqli using sqlmap made it simple. It would have taken us a lot of time to do this stuff manually. That's why in pentests both manual and automated testing is necessary. But its not a good idea to rely on just one of the two rather we should leverage power of both testing types to both understand and exploit the vulnerability.
By the way we could have used something like this to dump all databases and tables using this sqlmap command:

sqlmap -u "http://localhost:9000/vulnerabilities/sqli_blind/?id=1&Submit=Submit" --cookie="security=low; PHPSESSID=aks68qncbmtnd59q3ue7bmam30" -p id --dump-all

But obviously it is time and resource consuming so we only extracted what was interested to us rather than dumping all the stuff.
Also we could have used sqlmap in the simple sql injection that we did in the previous article. As an exercise redo the SQL Injection challenge using sqlmap.

References:

1. Blind SQL Injection: https://owasp.org/www-community/attacks/Blind_SQL_Injection
2. sqlmap: http://sqlmap.org/
3. MySQL SUBSTRING() Function: https://www.w3schools.com/sql/func_mysql_substring.asp

More articles

Msticpy - Microsoft Threat Intelligence Security Tools

Posted by KP-3မိသားစု |

Microsoft Threat Intelligence Python Security Tools.

msticpy is a library for InfoSec investigation and hunting in Jupyter Notebooks. It includes functionality to:

query log data from multiple sources
enrich the data with Threat Intelligence, geolocations and Azure resource data
extract Indicators of Activity (IoA) from logs and unpack encoded data
perform sophisticated analysis such as anomalous session detection and time series decomposition
visualize data using interactive timelines, process trees and multi-dimensional Morph Charts

It also includes some time-saving notebook tools such as widgets to set query time boundaries, select and display items from lists, and configure the notebook environment.

The msticpy package was initially developed to support Jupyter Notebooks authoring for Azure Sentinel. While Azure Sentinel is still a big focus of our work, we are extending the data query/acquisition components to pull log data from other sources (currently Splunk, Microsoft Defender for Endpoint and Microsoft Graph are supported but we are actively working on support for data from other SIEM platforms). Most of the components can also be used with data from any source. Pandas DataFrames are used as the ubiquitous input and output format of almost all components. There is also a data provider to make it easy to and process data from local CSV files and pickled DataFrames.

The package addresses three central needs for security investigators and hunters:

Acquiring and enriching data
Analyzing data
Visualizing data

We welcome feedback, bug reports, suggestions for new features and contributions.

Installing

For core install:

pip install msticpy

If you are using MSTICPy with Azure Sentinel you should install with the "azsentinel" extra package:

pip install msticpy[azsentinel]

or for the latest dev build

pip install git+https://github.com/microsoft/msticpy

Documentation

Full documentation is at ReadTheDocs

Sample notebooks for many of the modules are in the docs/notebooks folder and accompanying notebooks.

You can also browse through the sample notebooks referenced at the end of this document to see some of the functionality used in context. You can play with some of the package functions in this interactive demo on mybinder.org.

Log Data Acquisition

QueryProvider is an extensible query library targeting Azure Sentinel/Log Analytics, Splunk, OData and other log data sources. It also has special support for Mordor data sets and using local data.

Built-in parameterized queries allow complex queries to be run from a single function call. Add your own queries using a simple YAML schema.

Data Queries Notebook

Data Enrichment

Threat Intelligence providers

The TILookup class can lookup IoCs across multiple TI providers. built-in providers include AlienVault OTX, IBM XForce, VirusTotal and Azure Sentinel.

The input can be a single IoC observable or a pandas DataFrame containing multiple observables. Depending on the provider, you may require an account and an API key. Some providers also enforce throttling (especially for free tiers), which might affect performing bulk lookups.

TIProviders and TILookup Usage Notebook

GeoLocation Data

The GeoIP lookup classes allow you to match the geo-locations of IP addresses using either:

GeoLiteLookup - Maxmind Geolite (see https://www.maxmind.com)
IPStackLookup - IPStack (see https://ipstack.com)

GeoIP Lookup and GeoIP Notebook

Azure Resource Data, Storage and Azure Sentinel API

The AzureData module contains functionality for enriching data regarding Azure host details with additional host details exposed via the Azure API. The AzureSentinel module allows you to query incidents, retrieve detector and hunting queries. AzureBlogStorage lets you read and write data from blob storage.

Azure Resource APIs, Azure Sentinel APIs, Azure Storage

Security Analysis

This subpackage contains several modules helpful for working on security investigations and hunting:

Anomalous Sequence Detection

Detect unusual sequences of events in your Office, Active Directory or other log data. You can extract sessions (e.g. activity initiated by the same account) and identify and visualize unusual sequences of activity. For example, detecting an attacker setting a mail forwarding rule on someone's mailbox.

Anomalous Sessions and Anomalous Sequence Notebook

Time Series Analysis

Time series analysis allows you to identify unusual patterns in your log data taking into account normal seasonal variations (e.g. the regular ebb and flow of events over hours of the day, days of the week, etc.). Using both analysis and visualization highlights unusual traffic flows or event activity for any data set.

Time Series

Visualization

Event Timelines

Display any log events on an interactive timeline. Using the Bokeh Visualization Library the timeline control enables you to visualize one or more event streams, interactively zoom into specific time slots and view event details for plotted events.

Timeline and Timeline Notebook

Process Trees

The process tree functionality has two main components:

Process Tree creation - taking a process creation log from a host and building the parent-child relationships between processes in the data set.
Process Tree visualization - this takes the processed output displays an interactive process tree using Bokeh plots.

There are a set of utility functions to extract individual and partial trees from the processed data set.

Process Tree and Process Tree Notebook

Data Manipulation and Utility functions

Pivot Functions

Lets you use MSTICPy functionality in an "entity-centric" way. All functions, queries and lookups that relate to a particular entity type (e.g. Host, IpAddress, Url) are collected together as methods of that entity class. So, if you want to do things with an IP address, just load the IpAddress entity and browse its methods.

Pivot Functions and Pivot Functions Notebook

base64unpack

Base64 and archive (gz, zip, tar) extractor. It will try to identify any base64 encoded strings and try decode them. If the result looks like one of the supported archive types it will unpack the contents. The results of each decode/unpack are rechecked for further base64 content and up to a specified depth.

Base64 Decoding and Base64Unpack Notebook

iocextract

Uses regular expressions to look for Indicator of Compromise (IoC) patterns - IP Addresses, URLs, DNS domains, Hashes, file paths. Input can be a single string or a pandas dataframe.

IoC Extraction and IoCExtract Notebook

eventcluster (experimental)

This module is intended to be used to summarize large numbers of events into clusters of different patterns. High volume repeating events can often make it difficult to see unique and interesting items.

This is an unsupervised learning module implemented using SciKit Learn DBScan.

Event Clustering and Event Clustering Notebook

auditdextract

Module to load and decode Linux audit logs. It collapses messages sharing the same message ID into single events, decodes hex-encoded data fields and performs some event-specific formatting and normalization (e.g. for process start events it will re-assemble the process command line arguments into a single string).

syslog_utils

Module to support an investigation of a Linux host with only syslog logging enabled. This includes functions for collating host data, clustering logon events and detecting user sessions containing suspicious activity.

cmd_line

A module to support he detection of known malicious command line activity or suspicious patterns of command line activity.

domain_utils

A module to support investigation of domain names and URLs with functions to validate a domain name and screenshot a URL.

Notebook widgets

These are built from the Jupyter ipywidgets collection and group common functionality useful in InfoSec tasks such as list pickers, query time boundary settings and event display into an easy-to-use format.

More Notebooks on Azure Sentinel Notebooks GitHub

Azure Sentinel Notebooks

Example notebooks:

View directly on GitHub or copy and paste the link into nbviewer.org

Notebook examples with saved data

See the following notebooks for more examples of the use of this package in practice:

Windows Alert Investigation in GitHub or NbViewer
Office 365 Exploration in GitHub or NbViewer
Cross-Network Hunting in GitHubor NbViewer

Supported Platforms and Packages

msticpy is OS-independent
Requires Python 3.6 or later
See requirements.txt for more details and version requirements.

Contributing

For (brief) developer guidelines, see this wiki article Contributor Guidelines

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.