Testing Web Applications¶

In this chapter, we explore how to generate tests for Graphical User Interfaces (GUIs), notably on Web interfaces. We set up a (vulnerable) Web server and demonstrate how to systematically explore its behavior – first with handwritten grammars, then with grammars automatically inferred from the user interface. We also show how to conduct systematic attacks on these servers, notably with code and SQL injection.

Synopsis¶

To use the code provided in this chapter, write

>>> from fuzzingbook.WebFuzzer import <identifier>

and then make use of the following features.

This chapter provides a simple (and vulnerable) Web server and two experimental fuzzers that are applied to it.

Fuzzing Web Forms¶

WebFormFuzzer demonstrates how to interact with a Web form. Given a URL with a Web form, it automatically extracts a grammar that produces a URL; this URL contains values for all form elements. Support is limited to GET forms and a subset of HTML form elements.

Here's the grammar extracted for our vulnerable Web server:

>>> web_form_fuzzer = WebFormFuzzer(httpd_url)
>>> web_form_fuzzer.grammar['<start>']
['<action>?<query>']
>>> web_form_fuzzer.grammar['<action>']
['/order']
>>> web_form_fuzzer.grammar['<query>']
['<item>&<name>&<email-1>&<city>&<zip>&<terms>&<submit-1>']

Using it for fuzzing yields a path with all form values filled; accessing this path acts like filling out and submitting the form.

>>> web_form_fuzzer.fuzz()
'/order?item=lockset&name=%43+&email=+c%40_+c&city=%37b_4&zip=5&terms=on&submit='

Repeated calls to WebFormFuzzer.fuzz() invoke the form again and again, each time with different (fuzzed) values.

Internally, WebFormFuzzer builds on a helper class named HTMLGrammarMiner; you can extend its functionality to include more features.

SQL Injection Attacks¶

SQLInjectionFuzzer is an experimental extension of WebFormFuzzer whose constructor takes an additional payload – an SQL command to be injected and executed on the server. Otherwise, it is used like WebFormFuzzer:

>>> sql_fuzzer = SQLInjectionFuzzer(httpd_url, "DELETE FROM orders")
>>> sql_fuzzer.fuzz()
"/order?item=lockset&name=+&email=0%404&city=+'+)%3b+DELETE+FROM+orders%3b+--&zip='+OR+1%3d1--'&terms=on&submit="

As you can see, the path to be retrieved contains the payload encoded into one of the form field values.

Internally, SQLInjectionFuzzer builds on a helper class named SQLInjectionGrammarMiner; you can extend its functionality to include more features.

SQLInjectionFuzzer is a proof-of-concept on how to build a malicious fuzzer; you should study and extend its code to make actual use of it.

A Web User Interface¶

Let us start with a simple example. We want to set up a Web server that allows readers of this book to buy fuzzingbook-branded fan articles ("swag"). In reality, we would make use of an existing Web shop (or an appropriate framework) for this purpose. For the purpose of this book, we write our own Web server, building on the HTTP server facilities provided by the Python library.

Excursion: Implementing a Web Server¶

All of our Web server is defined in a HTTPRequestHandler, which, as the name suggests, handles arbitrary Web page requests.

Taking Orders¶

For our Web server, we need a number of Web pages:

• We want one page where customers can place an order.
• We want one page where they see their order confirmed.
• Additionally, we need pages display error messages such as "Page Not Found".

We start with the order form. The dictionary FUZZINGBOOK_SWAG holds the items that customers can order, together with long descriptions:

This is the HTML code for the order form. The menu for selecting the swag to be ordered is created dynamically from FUZZINGBOOK_SWAG. We omit plenty of details such as precise shipping address, payment, shopping cart, and more.

This is what the order form looks like:

This form is not yet functional, as there is no server behind it; pressing "place order" will lead you to a nonexistent page.

Order Confirmation¶

Once we have gotten an order, we show a confirmation page, which is instantiated with the customer information submitted before. Here is the HTML and the rendering:

Terms and Conditions¶

A website can only be complete if it has the necessary legalese. This page shows some terms and conditions.

Storing Orders¶

To store orders, we make use of a database, stored in the file orders.db.

To interact with the database, we use SQL commands. The following commands create a table with five text columns for item, name, email, city, and zip – the exact same fields we also use in our HTML form.

At this point, the database is still empty:

We can add entries using the SQL INSERT command:

These values are now in the database:

We can also delete entries from the table again (say, after completion of the order):

Handling HTTP Requests¶

We have an order form and a database; now we need a Web server which brings it all together. The Python http.server module provides everything we need to build a simple HTTP server. A HTTPRequestHandler is an object that takes and processes HTTP requests – in particular, GET requests for retrieving Web pages.

We implement the do_GET() method that, based on the given path, branches off to serve the requested Web pages. Requesting the path / produces the order form; a path beginning with /order sends an order to be processed. All other requests end in a Page Not Found message.

Order Form¶

Accessing the home page (i.e. getting the page at /) is simple: We go and serve the html_order_form as defined above.

Likewise, we can send out the terms and conditions:

Processing Orders¶

When the user clicks Submit on the order form, the Web browser creates and retrieves a URL of the form

<hostname>/order?field_1=value_1&field_2=value_2&field_3=value_3

where each field_i is the name of the field in the HTML form, and value_i is the value provided by the user. Values use the CGI encoding we have seen in the chapter on coverage – that is, spaces are converted into +, and characters that are not digits or letters are converted into %nn, where nn is the hexadecimal value of the character.

If Jane Doe <doe@example.com> from Seattle orders a T-Shirt, this is the URL the browser creates:

<hostname>/order?item=tshirt&name=Jane+Doe&email=doe%40example.com&city=Seattle&zip=98104

When processing a query, the attribute self.path of the HTTP request handler holds the path accessed – i.e., everything after <hostname>. The helper method get_field_values() takes self.path and returns a dictionary of values.

The method handle_order() takes these values from the URL, stores the order, and returns a page confirming the order. If anything goes wrong, it sends an internal server error.

Storing the order makes use of the database connection defined above; we create an SQL command instantiated with the values as extracted from the URL.

After storing the order, we send the confirmation HTML page, which again is instantiated with the values from the URL.

Other HTTP commands¶

Besides the GET command (which does all the heavy lifting), HTTP servers can also support other HTTP commands; we support the HEAD command, which returns the head information of a Web page. In our case, this is always empty.

Error Handling¶

We have defined pages for submitting and processing orders; now we also need a few pages for errors that might occur.

Page Not Found¶

This page is displayed if a non-existing page (i.e. anything except / or /order) is requested.

The method not_found() takes care of sending this out with the appropriate HTTP status code.

Internal Errors¶

This page is shown for any internal errors that might occur. For diagnostic purposes, we have it include the traceback of the failing function.

Logging¶

Our server runs as a separate process in the background, waiting to receive commands at all time. To see what it is doing, we implement a special logging mechanism. The httpd_message_queue establishes a queue into which one process (the server) can store Python objects, and in which another process (the notebook) can retrieve them. We use this to pass log messages from the server, which we can then display in the notebook.

For multiprocessing, we use the multiprocess module - a variant of the standard Python multiprocessing module that also works in notebooks. If you are running this code outside a notebook, you can also use multiprocessing instead.

Let us place two messages in the queue:

To distinguish server messages from other parts of the notebook, we format them specially:

The method print_httpd_messages() prints all messages accumulated in the queue so far:

With clear_httpd_messages(), we can silently discard all pending messages:

The method log_message() in the request handler makes use of the queue to store its messages:

In the chapter on carving, we had introduced a webbrowser() method which retrieves the contents of the given URL. We now extend it such that it also prints out any log messages produced by the server:

With webbrowser(), we are now ready to get the Web server up and running.

End of Excursion¶

Running the Server¶

We run the server on the local host – that is, the same machine which also runs this notebook. We check for an accessible port and put the resulting URL in the queue created earlier.

The function start_httpd() starts the server in a separate process, which we start using the multiprocess module. It retrieves its URL from the message queue and returns it, such that we can start talking to the server.

Let us now start the server and save its URL:

Interacting with the Server¶

Let us now access the server just created.

Direct Browser Access¶

If you are running the Jupyter notebook server on the local host as well, you can now access the server directly at the given URL. Simply open the address in httpd_url by clicking on the link below.

Note: This only works if you are running the Jupyter notebook server on the local host.

Even more convenient, you may be able to interact directly with the server using the window below.

Note: This only works if you are running the Jupyter notebook server on the local host.

After interaction, you can retrieve the messages produced by the server:

We can also see any orders placed in the orders database (db):

And we can clear the order database:

Retrieving the Home Page¶

Even if our browser cannot directly interact with the server, the notebook can. We can, for instance, retrieve the contents of the home page and display them:

Placing Orders¶

To test this form, we can generate URLs with orders and have the server process them.

The method urljoin() puts together a base URL (i.e., the URL of our server) and a path – say, the path towards our order.

With urljoin(), we can create a full URL that is the same as the one generated by the browser as we submit the order form. Sending this URL to the browser effectively places the order, as we can see in the server log produced:

The web page returned confirms the order:

And the order is in the database, too:

Error Messages¶

We can also test whether the server correctly responds to invalid requests. Nonexistent pages, for instance, are correctly handled:

You may remember we also have a page for internal server errors. Can we get the server to produce this page? To find this out, we have to test the server thoroughly – which we do in the remainder of this chapter.

Fuzzing Input Forms¶

After setting up and starting the server, let us now go and systematically test it – first with expected, and then with less expected values.

Fuzzing with Expected Values¶

Since placing orders is all done by creating appropriate URLs, we define a grammar ORDER_GRAMMAR which encodes ordering URLs. It comes with a few sample values for names, email addresses, cities and (random) digits.

Excursion: Implementing cgi_decode()¶

To make it easier to define strings that become part of a URL, we define the function cgi_encode(), taking a string and automatically encoding it into CGI:

The optional parameter do_not_encode allows us to skip certain characters from encoding. This is useful when encoding grammar rules:

cgi_encode() is the exact counterpart of the cgi_decode() function defined in the chapter on coverage:

End of Excursion¶

In the grammar, we make use of cgi_encode() to encode strings:

Using one of our grammar fuzzers, we can instantiate this grammar and generate URLs:

Sending these URLs to the server will have them processed correctly:

Fuzzing with Unexpected Values¶

We can now see that the server does a good job when faced with "standard" values. But what happens if we feed it non-standard values? To this end, we make use of a mutation fuzzer which inserts random changes into the URL. Our seed (i.e. the value to be mutated) comes from the grammar fuzzer:

Mutating this string yields mutations not only in the field values, but also in field names as well as the URL structure.

Let us fuzz a little until we get an internal server error. We use the Python requests module to interact with the Web server such that we can directly access the HTTP status code.

That didn't take long. Here's the offending URL:

How does the URL cause this internal error? We make use of delta debugging to minimize the failure-inducing path, setting up a WebRunner class to define the failure condition:

This is the minimized path:

It turns out that our server encounters an internal error if we do not supply the requested fields:

We see that we might have a lot to do to make our Web server more robust against unexpected inputs. The exercises give some instructions on what to do.

Extracting Grammars for Input Forms¶

In our previous examples, we have assumed that we have a grammar that produces valid (or less valid) order queries. However, such a grammar does not need to be specified manually; we can also extract it automatically from a Web page at hand. This way, we can apply our test generators on arbitrary Web forms without a manual specification step.

Searching HTML for Input Fields¶

The key idea of our approach is to identify all input fields in a form. To this end, let us take a look at how the individual elements in our order form are encoded in HTML:

We see that there is a number of form elements that accept inputs, in particular <input>, but also <select> and <option>. The idea now is to parse the HTML of the Web page in question, to extract these individual input elements, and then to create a grammar that produces a matching URL, effectively filling out the form.

To parse the HTML page, we could define a grammar to parse HTML and make use of our own parser infrastructure. However, it is much easier to not reinvent the wheel and instead build on the existing, dedicated HTMLParser class from the Python library.

During parsing, we search for <form> tags and save the associated action (i.e., the URL to be invoked when the form is submitted) in the action attribute. While processing the form, we create a map fields that holds all input fields we have seen; it maps field names to the respective HTML input types ("text", "number", "checkbox", etc.). Exclusive selection options map to a list of possible values; the select stack holds the currently active selection.

While parsing, the parser calls handle_starttag() for every opening tag (such as <form>) found; conversely, it invokes handle_endtag() for closing tags (such as </form>). attributes gives us a map of associated attributes and values.

Here is how we process the individual tags:

• When we find a <form> tag, we save the associated action in the action attribute;
• When we find an <input> tag or similar, we save the type in the fields attribute;
• When we find a <select> tag or similar, we push its name on the select stack;
• When we find an <option> tag, we append the option to the list associated with the last pushed <select> tag.

Our implementation handles only one form per Web page; it also works on HTML only, ignoring all interaction coming from JavaScript. Also, it does not support all HTML input types.

Let us put this parser to action. We create a class HTMLGrammarMiner that takes a HTML document to parse. It then returns the associated action and the associated fields:

Applied on our order form, this is what we get:

From this structure, we can now generate a grammar that automatically produces valid form submission URLs.

Mining Grammars for Web Pages¶

To create a grammar from the fields extracted from HTML, we build on the CGI_GRAMMAR defined in the chapter on grammars. The key idea is to define rules for every HTML input type: An HTML number type will get values from the <number> rule; likewise, values for the HTML email type will be defined from the <email> rule. Our default grammar provides very simple rules for these types.

Our grammar miner now takes the fields extracted from HTML, converting them into rules. Essentially, every input field encountered gets included in the resulting query URL; and it gets a rule expanding it into the appropriate type.

Let us show HTMLGrammarMiner in action, again applied on our order form. Here is the full resulting grammar:

Let us take a look into the structure of the grammar. It produces URL paths of this form:

Here, the <action> comes from the action attribute of the HTML form:

The <query> is composed of the individual field items:

Each of these fields has the form <field-name>=<field-type>, where <field-type> is already defined in the grammar:

These are the query URLs produced from the grammar. We see that these are similar to the ones produced from our hand-crafted grammar, except that the string values for names, email addresses, and cities are now completely random:

We can again feed these directly into our Web browser:

We see (one more time) that we can mine a grammar automatically from given data.

A Fuzzer for Web Forms¶

To make things most convenient, let us define a WebFormFuzzer class that does everything in one place. Given a URL, it extracts its HTML content, mines the grammar and then produces inputs for it.

All it now takes to fuzz a Web form is to provide its URL:

We can combine the fuzzer with a WebRunner as defined above to run the resulting fuzz inputs directly on our Web server:

While convenient to use, this fuzzer is still very rudimentary:

• It is limited to one form per page.
• It only supports GET actions (i.e., inputs encoded into the URL). A full Web form fuzzer would have to at least support POST actions.
• The fuzzer is build on HTML only. There is no Javascript handling for dynamic Web pages.

Let us clear any pending messages before we get to the next section:

Crawling User Interfaces¶

So far, we have assumed there would be only one form to explore. A real Web server, of course, has several pages – and possibly several forms, too. We define a simple crawler that explores all the links that originate from one page.

Our crawler is pretty straightforward. Its main component is again a HTMLParser that analyzes the HTML code for links of the form

<a href="<link>">

and saves all the links found in a list called links.

The actual crawler comes as a generator function crawl() which produces one URL after another. By default, it returns only URLs that reside on the same host; the parameter max_pages controls how many pages (default: 1) should be scanned. We also respect the robots.txt file on the remote site to check which pages we are allowed to scan.

Excursion: Implementing a Crawler¶

End of Excursion¶

We can run the crawler on our own server, where it will quickly return the order page and the terms and conditions page.

We can also crawl over other sites, such as the home page of this project.

Once we have crawled over all the links of a site, we can generate tests for all the forms we found:

For even better effects, one could integrate crawling and fuzzing – and also analyze the order confirmation pages for further links. We leave this to the reader as an exercise.

Let us get rid of any server messages accumulated above:

Crafting Web Attacks¶

Before we close the chapter, let us take a look at a special class of "uncommon" inputs that not only yield generic failures, but actually allow attackers to manipulate the server at their will. We will illustrate three common attacks using our server, which (surprise) actually turns out to be vulnerable against all of them.

HTML Injection Attacks¶

The first kind of attack we look at is HTML injection. The idea of HTML injection is to supply the Web server with data that can also be interpreted as HTML. If this HTML data is then displayed to users in their Web browsers, it can serve malicious purposes, although (seemingly) originating from a reputable site. If this data is also stored, it becomes a persistent attack; the attacker does not even have to lure victims towards specific pages.

Here is an example of a (simple) HTML injection. For the name field, we not only use plain text, but also embed HTML tags – in this case, a link towards a malware-hosting site.

If we use this grammar to create inputs, the resulting URL will have all of the HTML encoded in:

What happens if we send this string to our Web server? It turns out that the HTML is left in the confirmation page and shown as link. This also happens in the log:

Since the link seemingly comes from a trusted origin, users are much more likely to follow it. The link is even persistent, as it is stored in the database:

This means that if anyone ever queries the database (for instance, operators processing the order), they will also see the link, multiplying its impact. By carefully crafting the injected HTML, one can thus expose malicious content to numerous users – until the injected HTML is finally deleted.

Cross-Site Scripting Attacks¶

If one can inject HTML code into a Web page, one can also inject JavaScript code as part of the injected HTML. This code would then be executed as soon as the injected HTML is rendered.

This is particularly dangerous because executed JavaScript always executes in the origin of the page which contains it. Therefore, an attacker can normally not force a user to run JavaScript in any origin he does not control himself. When an attacker, however, can inject his code into a vulnerable Web application, he can have the client run the code with the (trusted) Web application as origin.

In such a cross-site scripting (XSS) attack, the injected script can do a lot more than just plain HTML. For instance, the code can access sensitive page content or session cookies. If the code in question runs in the operator's browser (for instance, because an operator is reviewing the list of orders), it could retrieve any other information shown on the screen and thus steal order details for a variety of customers.

Here is a very simple example of a script injection. Whenever the name is displayed, it causes the browser to "steal" the current session cookie – the piece of data the browser uses to identify the user with the server. In our case, we could steal the cookie of the Jupyter session.

The message looks as always – but if you have a look at your browser title, it should now show the first 10 characters of your "secret" notebook cookie. Instead of showing its prefix in the title, the script could also silently send the cookie to a remote server, allowing attackers to highjack your current notebook session and interact with the server on your behalf. It could also go and access and send any other data that is shown in your browser or otherwise available. It could run a keylogger and steal passwords and other sensitive data as it is typed in. Again, it will do so every time the compromised order with Jane Doe's name is shown in the browser and the associated script is executed.

Let us go and reset the title to a less sensitive value:

SQL Injection Attacks¶

Cross-site scripts have the same privileges as web pages – most notably, they cannot access or change data outside your browser. So-called SQL injection targets databases, allowing to inject commands that can read or modify data in the database, or change the purpose of the original query.

To understand how SQL injection works, let us take a look at the code that produces the SQL command to insert a new order into the database:

sql_command = ("INSERT INTO orders " +
    "VALUES ('{item}', '{name}', '{email}', '{city}', '{zip}')".format(**values))

What happens if any of the values (say, name) has a value that can also be interpreted as a SQL command? Then, instead of the intended INSERT command, we would execute the command imposed by name.

Let us illustrate this by an example. We set the individual values as they would be found during execution:

and format the string as seen above:

All fine, right? But now, we define a very "special" name that can also be interpreted as a SQL command:

What happens here is that we now get a command to insert values into the database (with a few "dummy" values x), followed by a SQL DELETE command that would delete all entries of the orders table. The string -- starts a SQL comment such that the remainder of the original query would be easily ignored. By crafting strings that can also be interpreted as SQL commands, attackers can alter or delete database data, bypass authentication mechanisms and many more.

Is our server also vulnerable to such attacks? Of course, it is. We create a special grammar such that we can set the <name> parameter to a string with SQL injection, just as shown above.

These are the current orders:

Let us go and send our URL with SQL injection to the server. From the log, we see that the "malicious" SQL command is formed just as sketched above, and executed, too.

All orders are now gone:

This effect is also illustrated in this very popular XKCD comic:

{width=100%}

Even if we had not been able to execute arbitrary commands, being able to compromise an orders database offers several possibilities for mischief. For instance, we could use the address and matching credit card number of an existing person to go through validation and submit an order, only to have the order then delivered to an address of our choice. We could also use SQL injection to inject HTML and JavaScript code as above, bypassing possible sanitization geared at these domains.

To avoid such effects, the remedy is to sanitize all third-party inputs – no character in the input must be interpretable as plain HTML, JavaScript, or SQL. This is achieved by properly quoting and escaping inputs. The exercises give some instructions on what to do.

Leaking Internal Information¶

To craft the above SQL queries, we have used insider information – for instance, we knew the name of the table as well as its structure. Surely, an attacker would not know this and thus not be able to run the attack, right? Unfortunately, it turns out we are leaking all of this information out to the world in the first place. The error message produced by our server reveals everything we need:

The best way to avoid information leakage through failures is of course not to fail in the first place. But if you fail, make it hard for the attacker to establish a link between the attack and the failure. In particular,

• Do not produce "internal error" messages (and certainly not ones with internal information).
• Do not become unresponsive; just go back to the home page and ask the user to supply correct data.

One more time, the exercises give some instructions on how to fix the server.

If you can manipulate the server not only to alter information, but also to retrieve information, you can learn about table names and structure by accessing special tables (also called data dictionary) in which database servers store their metadata. In the MySQL server, for instance, the special table information_schema holds metadata such as the names of databases and tables, data types of columns, or access privileges.

Fully Automatic Web Attacks¶

So far, we have demonstrated the above attacks using our manually written order grammar. However, the attacks also work for generated grammars. We extend HTMLGrammarMiner by adding a number of common SQL injection attacks:

We see that several fields now are tested for vulnerabilities: