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Fuzz techniques for testing web applications
How to perform basic fuzz testing on web applications. Especifically we fuzz over SQL injections on a vulnerable DB search site from bWAPP, using OWASP ZAProxy, obtaining mixed results. Only the most trivial injections succeded. We also show an example of an injection not feasible via fuzzing.

Web fuzzing is an automated, computerized technique to find bugs and vulnerabilities within a computer system. If you think protecting your site is a matter of simply blocking the most common types of malicious requests, think again. Read on.

Injecting SQL into a vulnerable site

A fairly common situation is a website providing the ability to search, add, and remove information from a database. But introducing this kind of feature demands great care in how you set up and access that database.

Let’s look at bWAPP, which has a movie database, and allows us to search for a given title:

bWAPP screenshot - movie search
Figure 1. bWAPP's movie search site

It looks like the site takes the user’s input as a POST request, then searches the database for that request, and finally prints back the result in table form. We can tell it’s a POST request since there is nothing in the URL that hints GET (consider that the huge POST title wouldn’t be there in a real app).

Let’s check that using the OWASP ZAP proxy. Indeed, we can see and confirm that the request is POST:

POST request when you search for a movie
POST /sqli_6.php HTTP/1.1
Host: localhost
User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:52.9) Gecko/20100101
Goanna/3.4 Firefox/52.9 PaleMoon/27.7.2
...
Content-Length: 24
title=the&action=search

Now we can intercept this request using ZAP and edit the title=the bit above, changing the by some SQL. For example, if we change it to:

Naive SQL injection
Iron Man' OR 1=1;

Because the 1=1 is always true, making the overall condition true, we should get all entries in the table,

But it doesn’t happen. We get an error:

Error: You have an error in your SQL syntax; check the manual
that corresponds to your MySQL server version for the right
syntax to use near '%'' at line 1

Well, at least now we know for sure they are using MySQL, because we can see it in the error message.

There are infinitely many strings (sequences of characters) we could try to use in order to complete the unknown SQL query the server is asking from the database.

What if we could try a bunch of them, at the same time, automatically?

Well, that’s what “fuzzing” is all about.

Web application fuzzing

There are other kinds of fuzzing: desktop application fuzzing using command-line or graphical interfaces (testing combinations of buttons, inputs, etc.), protocol fuzzing, file format fuzzing, and more.[1]

In this article we will focus only on web application fuzzing which is the semi-automated, pseudo-random manipulation of URLs, forms, user-generated content[1], requests, etc. We may tackle other kinds of fuzzing in future articles.

For a given fuzzing attack, the most comprehensive and sure-fire way to succeed would be to try every possible input. For example, if we’re fuzzing an input string, we should try every possible string, beginning with the empty string. This is due to the fact that sometimes programs have unexpected reactions to odd input, like the bug found in Mac OS last year, where you could log in as root by pressing the login button enough times (see CVE-2017-13872 for more info).

But this “try everything” approach is not really feasible or practical: the space complexity of such an attempt would be enormous. Thus we must bound the so-called explorable solutions space. This is usually achieved by limiting the input attempts to values that have a statistically higher probability of triggering a bug. These are known as fuzz vectors. In our case, they would be SQL queries. Some examples from OWASP:[2]

' OR 1=1;--
' OR 'a'='a
%22+OR+isnull%281%2F0%29+%2F*
Admin' OR '
'%20SELECT%20*%20FROM%20INFORMATION_SCHEMA.TABLES--
HAVING 1=1--
' OR username LIKE char(37);
' ; DROP TABLE temp --
GRANT CONNECT TO name; GRANT RESOURCE TO name;

Your fuzzer of choice will probably provide a healthy dose of fuzz vectors, as does ours, the OWASP ZAP Fuzzer. All we need to do is

  1. select the string we want to fuzz,

  2. invoke the fuzzer,

  3. select the payloads, i.e. the fuzz vectors, and

  4. run the fuzzer.

ZAP includes several of those by default; we will use the SQL injection vector from jbrofuzz:

Running ZAP fuzzer
Figure 2. How to run ZAP fuzzer

Successfully injected SQL queries are marked with the state “reflected” in the list:

bWAPP fuzz testing - reflected SQL injections
Figure 3. Reflected SQL injections in fuzz testing

Here we see a fuzz test is only as good as its payloads or fuzz vectors. Only the most trivial injections succeeded, i.e. the ones of the form

whatever' OR (something truthy)

which simply show all entries in the table movies.

When fuzzing, this is both a blessing and a curse. Usually, they don’t, but occasionally the simplest injections reveal unexpected outcomes, and when they do, they are real surprises such as the Apple bug mentioned above.

Comparison with manual injection

With information about the app and the database structure, we can inject more effective queries. For example, suppose you’ve found out that there is another table called users and we want to see what’s in there.

If we try to inject the following query:

%'; SELECT * FROM users;

we get an error, because the database management system does not allow query concatenation.

If we try with union instead:

%' UNION SELECT * FROM users;#

we still get an error, because the tables don’t match in size.

Suppose, for the sake of the example, that we also know (or guess) the names of the columns and select the most interesting ones:

%' UNION SELECT id, login, password, email, secret,
activated, admin FROM users;#

Then we get the most of the users' info (passwords are hashed, but can be recovered).

bWAPP SQL injection screenshot showing passwords
Figure 4. Succesful manual SQL injection

By itself fuzz testing cannot replace human expertise in the equation but it adds an important additional point of view. As seen in the Mac OS example, its greatest weakness can be a potential source of great surprises. We have merely glimpsed the tip of the iceberg here, but hope you find this short introduction helpful.


Author picture

Rafael Ballestas

Mathematician

with an itch for CS


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