Every few years, the Open Web Application Security Project (OWASP) ranks the most critical web application security risks. Since the first report, injection risks have always been on top. Among all injection types, SQL injection is one of the most common attack vectors, and arguably the most dangerous. As Python is one of the most popular programming languages in the world, knowing how to protect against Python SQL injection is critical.
In this tutorial, you’re going to learn:
- What Python SQL injection is and how to prevent it
- How to compose queries with both literals and identifiers as parameters
- How to safely execute queries in a database
This tutorial is suited for users of all database engines. The examples here use PostgreSQL, but the results can be reproduced in other database management systems (such as SQLite, MySQL, Microsoft SQL Server, Oracle, and so on).
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Understanding Python SQL Injection
SQL Injection attacks are such a common security vulnerability that the legendary xkcd webcomic devoted a comic to it:
"Exploits of a Mom" (Image: xkcd)
Generating and executing SQL queries is a common task. However, companies around the world often make horrible mistakes when it comes to composing SQL statements. While the ORM layer usually composes SQL queries, sometimes you have to write your own.
When you use Python to execute these queries directly into a database, there’s a chance you could make mistakes that might compromise your system. In this tutorial, you’ll learn how to successfully implement functions that compose dynamic SQL queries without putting your system at risk for Python SQL injection.
Setting Up a Database
To get started, you’re going to set up a fresh PostgreSQL database and populate it with data. Throughout the tutorial, you’ll use this database to witness firsthand how Python SQL injection works.
Creating a Database
First, open your shell and create a new PostgreSQL database owned by the user postgres:
$ createdb -O postgres psycopgtest
Here you used the command line option -O to set the owner of the database to the user postgres. You also specified the name of the database, which is psycopgtest.
Note: postgres is a special user, which you would normally reserve for administrative tasks, but for this tutorial, it’s fine to use postgres. In a real system, however, you should create a separate user to be the owner of the database.
Your new database is ready to go! You can connect to it using psql:
$ psql -U postgres -d psycopgtest
psql (11.2, server 10.5)
Type "help" for help.
You’re now connected to the database psycopgtest as the user postgres. This user is also the database owner, so you’ll have read permissions on every table in the database.
Creating a Table With Data
Next, you need to create a table with some user information and add data to it:
psycopgtest=# CREATE TABLE users (
username varchar(30),
admin boolean
);
CREATE TABLE
psycopgtest=# INSERT INTO users
(username, admin)
VALUES
('ran', true),
('haki', false);
INSERT 0 2
psycopgtest=# SELECT * FROM users;
username | admin
----------+-------
ran | t
haki | f
(2 rows)
The table has two columns: username and admin. The admin column indicates whether or not a user has administrative privileges. Your goal is to target the admin field and try to abuse it.
Setting Up a Python Virtual Environment
Now that you have a database, it’s time to set up your Python environment. For step-by-step instructions on how to do this, check out Python Virtual Environments: A Primer.
Create your virtual environment in a new directory:
~/src $ mkdir psycopgtest
~/src $ cd psycopgtest
~/src/psycopgtest $ python3 -m venv venv
After you run this command, a new directory called venv will be created. This directory will store all the packages you install inside the virtual environment.
Connecting to the Database
To connect to a database in Python, you need a database adapter. Most database adapters follow version 2.0 of the Python Database API Specification PEP 249. Every major database engine has a leading adapter:
To connect to a PostgreSQL database, you’ll need to install Psycopg, which is the most popular adapter for PostgreSQL in Python. Django ORM uses it by default, and it’s also supported by SQLAlchemy.
In your terminal, activate the virtual environment and use pip to install psycopg:
~/src/psycopgtest $ source venv/bin/activate
~/src/psycopgtest $ python -m pip install psycopg2>=2.8.0
Collecting psycopg2
Using cached https://....
psycopg2-2.8.2.tar.gz
Installing collected packages: psycopg2
Running setup.py install for psycopg2 ... done
Successfully installed psycopg2-2.8.2
Now you’re ready to create a connection to your database. Here’s the start of your Python script:
import psycopg2
connection = psycopg2.connect(
host="localhost",
database="psycopgtest",
user="postgres",
password=None,
)
connection.set_session(autocommit=True)
You used psycopg2.connect() to create the connection. This function accepts the following arguments:
-
hostis the IP address or the DNS of the server where your database is located. In this case, the host is your local machine, orlocalhost. -
databaseis the name of the database to connect to. You want to connect to the database you created earlier,psycopgtest. -
useris a user with permissions for the database. In this case, you want to connect to the database as the owner, so you pass the userpostgres. -
passwordis the password for whoever you specified inuser. In most development environments, users can connect to the local database without a password.
After setting up the connection, you configured the session with autocommit=True. Activating autocommit means you won’t have to manually manage transactions by issuing a commit or rollback. This is the default behavior in most ORMs. You use this behavior here as well so that you can focus on composing SQL queries instead of managing transactions.
Note: Django users can get the instance of the connection used by the ORM from django.db.connection:
from django.db import connection
Executing a Query
Now that you have a connection to the database, you’re ready to execute a query:
>>> with connection.cursor() as cursor:
... cursor.execute('SELECT COUNT(*) FROM users')
... result = cursor.fetchone()
... print(result)
(2,)
You used the connection object to create a cursor. Just like a file in Python, cursor is implemented as a context manager. When you create the context, a cursor is opened for you to use to send commands to the database. When the context exits, the cursor closes and you can no longer use it.
Note: To learn more about context managers, check out Python Context Managers and the “with” Statement.
While inside the context, you used cursor to execute a query and fetch the results. In this case, you issued a query to count the rows in the users table. To fetch the result from the query, you executed cursor.fetchone() and received a tuple. Since the query can only return one result, you used fetchone(). If the query were to return more than one result, then you’d need to either iterate over cursor or use one of the other fetch* methods.
Using Query Parameters in SQL
In the previous section, you created a database, established a connection to it, and executed a query. The query you used was static. In other words, it had no parameters. Now you’ll start to use parameters in your queries.
First, you’re going to implement a function that checks whether or not a user is an admin. is_admin() accepts a username and returns that user’s admin status:
# BAD EXAMPLE. DON'T DO THIS!
def is_admin(username: str) -> bool:
with connection.cursor() as cursor:
cursor.execute("""
SELECT
admin
FROM
users
WHERE
username = '%s'
""" % username)
result = cursor.fetchone()
admin, = result
return admin
This function executes a query to fetch the value of the admin column for a given username. You used fetchone() to return a tuple with a single result. Then, you unpacked this tuple into the variable admin. To test your function, check some usernames:
>>> is_admin('haki')
False
>>> is_admin('ran')
True
So far so good. The function returned the expected result for both users. But what about non-existing user? Take a look at this Python traceback:
>>> is_admin('foo')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 12, in is_admin
TypeError: cannot unpack non-iterable NoneType object
When the user does not exist, a TypeError is raised. This is because .fetchone() returns None when no results are found, and unpacking None raises a TypeError. The only place you can unpack a tuple is where you populate admin from result.
To handle non-existing users, create a special case for when result is None:
# BAD EXAMPLE. DON'T DO THIS!
def is_admin(username: str) -> bool:
with connection.cursor() as cursor:
cursor.execute("""
SELECT
admin
FROM
users
WHERE
username = '%s'
""" % username)
result = cursor.fetchone()
if result is None:
# User does not exist
return False
admin, = result
return admin
Here, you’ve added a special case for handling None. If username does not exist, then the function should return False. Once again, test the function on some users:
>>> is_admin('haki')
False
>>> is_admin('ran')
True
>>> is_admin('foo')
False
Great! The function can now handle non-existing usernames as well.
Exploiting Query Parameters With Python SQL Injection
In the previous example, you used string interpolation to generate a query. Then, you executed the query and sent the resulting string directly to the database. However, there’s something you may have overlooked during this process.
Think back to the username argument you passed to is_admin(). What exactly does this variable represent? You might assume that username is just a string that represents an actual user’s name. As you’re about to see, though, an intruder can easily exploit this kind of oversight and cause major harm by performing Python SQL injection.
Try to check if the following user is an admin or not:
>>> is_admin("'; select true; --")
True
Wait… What just happened?
Let’s take another look at the implementation. Print out the actual query being executed in the database:
>>> print("select admin from users where username = '%s'" % "'; select true; --")
select admin from users where username = ''; select true; --'
The resulting text contains three statements. To understand exactly how Python SQL injection works, you need to inspect each part individually. The first statement is as follows:
select admin from users where username = '';
This is your intended query. The semicolon (;) terminates the query, so the result of this query does not matter. Next up is the second statement:
select true;
This statement was constructed by the intruder. It’s designed to always return True.
Lastly, you see this short bit of code:
--'
This snippet defuses anything that comes after it. The intruder added the comment symbol (--) to turn everything you might have put after the last placeholder into a comment.
When you execute the function with this argument, it will always return True. If, for example, you use this function in your login page, an intruder could log in with the username '; select true; --, and they’ll be granted access.
If you think this is bad, it could get worse! Intruders with knowledge of your table structure can use Python SQL injection to cause permanent damage. For example, the intruder can inject an update statement to alter the information in the database:
>>> is_admin('haki')
False
>>> is_admin("'; update users set admin = 'true' where username = 'haki'; select true; --")
True
>>> is_admin('haki')
True
Let’s break it down again:
';
This snippet terminates the query, just like in the previous injection. The next statement is as follows:
update users set admin = 'true' where username = 'haki';
This section updates admin to true for user haki.
Finally, there’s this code snippet:
select true; --
As in the previous example, this piece returns true and comments out everything that follows it.
Why is this worse? Well, if the intruder manages to execute the function with this input, then user haki will become an admin:
psycopgtest=# select * from users;
username | admin
----------+-------
ran | t
haki | t
(2 rows)
The intruder no longer has to use the hack. They can just log in with the username haki. (If the intruder really wanted to cause harm, then they could even issue a DROP DATABASE command.)
Before you forget, restore haki back to its original state:
psycopgtest=# update users set admin = false where username = 'haki';
UPDATE 1
So, why is this happening? Well, what do you know about the username argument? You know it should be a string representing the username, but you don’t actually check or enforce this assertion. This can be dangerous! It’s exactly what attackers are looking for when they try to hack your system.
Crafting Safe Query Parameters
In the previous section, you saw how an intruder can exploit your system and gain admin permissions by using a carefully crafted string. The issue was that you allowed the value passed from the client to be executed directly to the database, without performing any sort of check or validation. SQL injections rely on this type of vulnerability.
Any time user input is used in a database query, there’s a possible vulnerability for SQL injection. The key to preventing Python SQL injection is to make sure the value is being used as the developer intended. In the previous example, you intended for username to be used as a string. In reality, it was used as a raw SQL statement.
To make sure values are used as they’re intended, you need to escape the value. For example, to prevent intruders from injecting raw SQL in the place of a string argument, you can escape quotation marks:
>>> # BAD EXAMPLE. DON'T DO THIS!
>>> username = username.replace("'", "''")
This is just one example. There are a lot of special characters and scenarios to think about when trying to prevent Python SQL injection. Lucky for you, modern database adapters, come with built-in tools for preventing Python SQL injection by using query parameters. These are used instead of plain string interpolation to compose a query with parameters.
Note: Different adapters, databases, and programming languages refer to query parameters by different names. Common names include bind variables, replacement variables, and substitution variables.
Now that you have a better understanding of the vulnerability, you’re ready to rewrite the function using query parameters instead of string interpolation:
1 def is_admin(username: str) -> bool:
2 with connection.cursor() as cursor:
3 cursor.execute("""
4 SELECT
5 admin
6 FROM
7 users
8 WHERE
9 username = %(username)s
10 """, {
11 'username': username
12 })
13 result = cursor.fetchone()
14
15 if result is None:
16 # User does not exist
17 return False
18
19 admin, = result
20 return admin
Here’s what’s different in this example:
-
In line 9, you used a named parameter
usernameto indicate where the username should go. Notice how the parameterusernameis no longer surrounded by single quotation marks. -
In line 11, you passed the value of
usernameas the second argument tocursor.execute(). The connection will use the type and value ofusernamewhen executing the query in the database.
To test this function, try some valid and invalid values, including the dangerous string from before:
>>> is_admin('haki')
False
>>> is_admin('ran')
True
>>> is_admin('foo')
False
>>> is_admin("'; select true; --")
False
Amazing! The function returned the expected result for all values. What’s more, the dangerous string no longer works. To understand why, you can inspect the query generated by execute():
>>> with connection.cursor() as cursor:
... cursor.execute("""
... SELECT
... admin
... FROM
... users
... WHERE
... username = %(username)s
... """, {
... 'username': "'; select true; --"
... })
... print(cursor.query.decode('utf-8'))
SELECT
admin
FROM
users
WHERE
username = '''; select true; --'
The connection treated the value of username as a string and escaped any characters that might terminate the string and introduce Python SQL injection.
Passing Safe Query Parameters
Database adapters usually offer several ways to pass query parameters. Named placeholders are usually the best for readability, but some implementations might benefit from using other options.
Let’s take a quick look at some of the right and wrong ways to use query parameters. The following code block shows the types of queries you’ll want to avoid:
# BAD EXAMPLES. DON'T DO THIS!
cursor.execute("SELECT admin FROM users WHERE username = '" + username + '");
cursor.execute("SELECT admin FROM users WHERE username = '%s' % username);
cursor.execute("SELECT admin FROM users WHERE username = '{}'".format(username));
cursor.execute(f"SELECT admin FROM users WHERE username = '{username}'");
Each of these statements passes username from the client directly to the database, without performing any sort of check or validation. This sort of code is ripe for inviting Python SQL injection.
In contrast, these types of queries should be safe for you to execute:
# SAFE EXAMPLES. DO THIS!
cursor.execute("SELECT admin FROM users WHERE username = %s'", (username, ));
cursor.execute("SELECT admin FROM users WHERE username = %(username)s", {'username': username});
In these statements, username is passed as a named parameter. Now, the database will use the specified type and value of username when executing the query, offering protection from Python SQL injection.
Using SQL Composition
So far you’ve used parameters for literals. Literals are values such as numbers, strings, and dates. But what if you have a use case that requires composing a different query—one where the parameter is something else, like a table or column name?
Inspired by the previous example, let’s implement a function that accepts the name of a table and returns the number of rows in that table:
# BAD EXAMPLE. DON'T DO THIS!
def count_rows(table_name: str) -> int:
with connection.cursor() as cursor:
cursor.execute("""
SELECT
count(*)
FROM
%(table_name)s
""", {
'table_name': table_name,
})
result = cursor.fetchone()
rowcount, = result
return rowcount
Try to execute the function on your users table:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 9, in count_rows
psycopg2.errors.SyntaxError: syntax error at or near "'users'"
LINE 5: 'users'
^
The command failed to generate the SQL. As you’ve seen already, the database adapter treats the variable as a string or a literal. A table name, however, is not a plain string. This is where SQL composition comes in.
You already know it’s not safe to use string interpolation to compose SQL. Luckily, Psycopg provides a module called psycopg.sql to help you safely compose SQL queries. Let’s rewrite the function using psycopg.sql.SQL():
from psycopg2 import sql
def count_rows(table_name: str) -> int:
with connection.cursor() as cursor:
stmt = sql.SQL("""
SELECT
count(*)
FROM
{table_name}
""").format(
table_name = sql.Identifier(table_name),
)
cursor.execute(stmt)
result = cursor.fetchone()
rowcount, = result
return rowcount
There are two differences in this implementation. First, you used sql.SQL() to compose the query. Then, you used sql.Identifier() to annotate the argument value table_name. (An identifier is a column or table name.)
Note: Users of the popular package django-debug-toolbar might get an error in the SQL panel for queries composed with psycopg.sql.SQL(). A fix is expected for release in version 2.0.
Now, try executing the function on the users table:
>>> count_rows('users')
2
Great! Next, let’s see what happens when the table does not exist:
>>> count_rows('foo')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 11, in count_rows
psycopg2.errors.UndefinedTable: relation "foo" does not exist
LINE 5: "foo"
^
The function throws the UndefinedTable exception. In the following steps, you’ll use this exception as an indication that your function is safe from a Python SQL injection attack.
Note: The exception UndefinedTable was added in psycopg2 version 2.8. If you’re working with an earlier version of Psycopg, then you’ll get a different exception.
To put it all together, add an option to count rows in the table up to a certain limit. This feature might be useful for very large tables. To implement this, add a LIMIT clause to the query, along with query parameters for the limit’s value:
from psycopg2 import sql
def count_rows(table_name: str, limit: int) -> int:
with connection.cursor() as cursor:
stmt = sql.SQL("""
SELECT
COUNT(*)
FROM (
SELECT
1
FROM
{table_name}
LIMIT
{limit}
) AS limit_query
""").format(
table_name = sql.Identifier(table_name),
limit = sql.Literal(limit),
)
cursor.execute(stmt)
result = cursor.fetchone()
rowcount, = result
return rowcount
In this code block, you annotated limit using sql.Literal(). As in the previous example, psycopg will bind all query parameters as literals when using the simple approach. However, when using sql.SQL(), you need to explicitly annotate each parameter using either sql.Identifier() or sql.Literal().
Note: Unfortunately, the Python API specification does not address the binding of identifiers, only literals. Psycopg is the only popular adapter that added the ability to safely compose SQL with both literals and identifiers. This fact makes it even more important to pay close attention when binding identifiers.
Execute the function to make sure that it works:
>>> count_rows('users', 1)
1
>>> count_rows('users', 10)
2
Now that you see the function is working, make sure it’s also safe:
>>> count_rows("(select 1) as foo; update users set admin = true where name = 'haki'; --", 1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 18, in count_rows
psycopg2.errors.UndefinedTable: relation "(select 1) as foo; update users set admin = true where name = '" does not exist
LINE 8: "(select 1) as foo; update users set adm...
^
This traceback shows that psycopg escaped the value, and the database treated it as a table name. Since a table with this name doesn’t exist, an UndefinedTable exception was raised and you were not hacked!
Conclusion
You’ve successfully implemented a function that composes dynamic SQL without putting your system at risk for Python SQL injection! You’ve used both literals and identifiers in your query without compromising security.
You’ve learned:
- What Python SQL injection is and how it can be exploited
- How to prevent Python SQL injection using query parameters
- How to safely compose SQL statements that use literals and identifiers as parameters
You’re now able to create programs that can withstand attacks from the outside. Go forth and thwart the hackers!
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