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https://github.com/klmitch/policies

An access policy language evaluator.
https://github.com/klmitch/policies

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An access policy language evaluator.

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README 

        
========

Policies

========



A package for interpretation and enforcement of access control

policies.



Introduction

============



It is often necessary to separate code that performs an action from

the code that performs the access check.  One reason for this is to

accommodate different users with different access control

requirements.  For instance, one user may be operating a system

internally, where all authenticated users should be able to perform

all actions, whereas another user may need to lock down specific

operations so they can only be executed by administrators.



The ``policies`` package is designed to accommodate these needs.

Access control policies can be expressed as strings, using a subset of

Python; then, these policies can be loaded into a ``policies.Policy``

object.  When an access determination needs to be made, a call to the

``policies.Policy.evaluate()`` method will evaluate a named policy

rule and return an ``Authorization`` object, which evaluates as either

``True`` or ``False``.



The policy strings may be loaded from any source.  They are simply

strings, written in a subset of the Python language, and allow much of

the expressive power of Python.  The policy language has syntax for

making function calls, including functions defined as entrypoints_;

this allows any desired access control policy to be implemented for

any application using ``policies``.



``policies`` for Developers

===========================



The ``policies`` package is easy for developers to use; simply

instantiate a ``policies.Policy`` object with an optional entrypoint

group and dictionary of built-in functions (defaults to select Python

builtins, available as ``policies.Policy.builtins``), then add rules

to the object.  This can be done by assigning the rule text using the

dictionary item setting syntax, like so::



    policy['rule_name'] = "user.is_admin()"



Alternatively, the rule text can be passed to ``policies.Rule`` and

set using the ``policies.Policy.set_rule()`` method, like so::



    rule = policies.Rule("rule_name", "user.is_admin()")

    policy.set_rule(rule)



These two different methods allow for the rules to be loaded from any

desired source, such as a file or a database.



Evaluation of a policy rule is as simple as calling the

``policies.Policy.evaluate()`` function::



    authz = policy.evaluate("rule_name", {'user': user})



The ``authz`` value can then be used to determine if the operation is

allowed by the policy::



    if authz:

        # Perform the operation here

	pass

    else:

        # Tell the user he's unprivileged

	pass



Note the dictionary passed as the second argument to

``policy.evaluate()`` above; this allows variables to be passed in to

policy rules.



Authorization Attributes

------------------------



The return value from ``policy.evaluate()`` is not a simple ``True``

or ``False`` value; it is an instance of ``policies.Authorization``.

The reason for this is that the policy language allows for setting

*authorization attributes*.  To explain what this is about, let's

assume that the operation we're writing a policy for is a user update

operation.  Obviously, we want the user to be able to update certain

parts of their own record, but others--say, payment status--should

only be available to administrators.  We can write this all in one

rule in the policy language::



    user.is_admin() or user == target {{ payment=user.is_admin() }}



When we evaluate this rule, the ``policies.Authorization`` object

returned will test ``True`` or ``False`` depending on the result of

evaluating the first part of the rule, ``user.is_admin() or user ==

target``.  However, the ``authz`` object will now also have an

attribute named ``payment``; this attribute will have the value

obtained by computing ``user.is_admin()``.



Authorization attributes default to ``None`` if the policy language

doesn't set them.  This default can be overridden by passing a

dictionary of attribute defaults to the ``policies.Rule`` instance

when it is created, or by declaring the rule using

``policies.Policy.declare()``.



Note that authorization attribute names CANNOT begin with an

underscore ("_").



Declaring Policy Rules

----------------------



Setting policy rules has been described above, but what about setting

up defaults for the policy rules?  This can be done using the

``policies.Policy.declare()`` method::



    policy.declare("rule_name", text="user.is_admin()")



This can also be used to set defaults for authorization attributes, by

passing a dictionary of those defaults as the ``attrs`` keyword

argument.



The ``policy.declare()`` method also allows associating documentation

text with the rule and the authorization attributes, using the ``doc``

and ``attr_docs`` keyword arguments; calling ``policy.declare()`` will

result in the creation of ``policies.RuleDoc`` objects to contain the

passed-in documentation.  These objects can be retrieved using the

``policies.Policy.get_doc()`` and ``policies.Policy.get_docs()``

methods, and could be used to generate sample policy configuration

files.



Variable Resolution in Policy Rules

-----------------------------------



When a variable is encountered in a policy rule, it must be resolved

to an actual value.  The first place searched when resolving variables

is the dictionary of variables that was passed to

``policies.Policy.evaluate()``; values passed here override any other

source.



If the variable cannot be found in the dictionary passed to

``policies.Policy.evaluate()``, then a dictionary of builtins is

searched; by default, these builtins are the ones in

``policies.Policy.builtins``, and represent a subset of the Python

builtins.  These builtins can be overridden by passing a dictionary as

the ``builtins`` parameter of the ``policies.Policy`` constructor.

Note that one special builtin exists which is not listed in

``policies.Policy.builtins``, and which will be added to the builtins

passed to the ``policies.Policy`` constructor: the ``rule()`` builtin

allows for one rule to call another.  It can be overridden, if

desired, by passing an alternate value for the "rule" key in the

``builtins`` dictionary.



If the variable cannot be resolved from either of the sources above,

it is next searched for using entrypoints_.  The entrypoint group to

search can be specified as the ``group`` argument to the

``policies.Policy`` constructor.  There is no default for the

entrypoint group, so if left unset, no entrypoints will be resolved.

Any entrypoints found will be cached for the lifetime of the

``policies.Policy`` object.  It is recommended that you set ``group``

to be the name of your application, followed by a period, followed by

the name "policies"; e.g., if your application was called "spam", you

would use "spam.policies".  Using an entrypoint group allows your

users to set up arbitrary functions for use in evaluating access

control policies, and thus allows them ultimate control over access.



If a variable cannot be resolved using any of the above sources, its

value will be ``None``.  This is as opposed to the standard Python

behavior of raising a ``NameError``.  The ``policies`` package is

designed to be as tolerant of user errors as possible.



``policies`` for Users

======================



Policy rules are written in a subset of the Python expression

language.  The singleton values ``True``, ``False``, and ``None`` are

recognized, as are single- and double-quoted strings, integers, and

floats.  The set literal syntax is also recognized, i.e., ``{1, 2,

3}`` represents the value ``frozenset([1, 2, 3])``.  Tuple literals,

list literals, dictionary literals, and comprehensions are not

supported, although the ``tuple()``, ``list()``, and ``dict()``

builtins are available, as are ``set()`` and ``frozenset()``.



In addition to the literal values mentioned above, the policy language

also supports attribute reference, subscription (``x[index]``), and

function calls.  Note that "slicing" (``x[index:index]``) is not

supported, however.  Finally, all arithmetic, logical, and comparison

operators are supported, as is the Python "trinary" syntax (``a if b

else c``).



As an example, let's suppose that a particular rule is controlling

update access to a user record.  The ``user`` variable will be the

user requesting the operation, and ``target`` will be the user record

the operation is to act upon.  The policy we want to implement is to

allow a given user to update only their own record, but we want

administrators to be able to update any user record.  We'll assume

that ``user`` has a boolean attribute named ``admin`` that is ``True``

if the user is an administrator.  Under these assumptions, the policy

rule could be written as::



    user == target or user.admin



It is also possible to call methods on an object.  Lets say that,

instead of a boolean attribute named ``admin`` that specifies whether

a user is an admin, we instead base administrator status on the

members of a group.  We assume that the ``user`` object has an

``in_group()`` method.  We could then write the rule as::



    user == target or user.in_group("administrators")



Finally, it is also possible to call functions.  If the

``policies.Policy()`` class was instantiated with an entrypoint group,

you can install a package with a function defined in that entrypoint

group (see entrypoints_), which will then be available to policy

rules.  This allows ultimate control over access control.  Note that

only positional arguments can be passed to functions; keyword

arguments are not available.



Note that operator short-circuiting is implemented; that is, in an

expression like ``user == target or user.admin``, if the ``user ==

target`` clause evaluates to ``True``, then ``user.admin`` will not be

evaluated.  This applies for the logical operators (``and`` and

``or``), as well as in the "trinary" syntax.  Constant folding is also

implemented, so rule text like ``5 + 23 > user.spam`` will only

compute the operation ``5 + 23`` once, during rule parsing.



Authorization Attributes

------------------------



Let us take the example from above and add one more requirement.

Let's say that one of the things the user update operation can update

is the current payment status on a user.  Obviously, that is something

that we don't want a user to be able to update; only administrators

should be able to update the payment status.  A developer can allow

this particular subset of functionality to be controlled separately

using an *authorization attribute*.  For the example above, let's

assume that the ``payment`` authorization attribute can control access

to the update of the payment status.  Now we can rewrite the policy

rule as::



    user == target or user.admin {{ payment=user.admin }}



More than one authorization attribute can be computed by separating

them with commas.  Let's assume that we have an authorization

attribute ``name`` that allows updating the user's name, and we want

to allow only the user to alter the name; we could write the rule as::



    user == target or user.admin {{ payment=user.admin,

                                    name=user==target }}



Evaluating Other Rules

----------------------



Each rule has an associated name.  It is possible to define an

arbitrary rule, and then evaluate it from another rule.  Taking our

example from above, let's assume that an admin must not only be in the

"administrators" group, but must also have ``admin`` set to ``True``

on their user record.  (This could be the case if your policy requires

administrators to explicitly turn on their administrative privileges.)

We could create an "is_admin" rule that looks like this::



    user.in_group("administrators") and user.admin



We could then write the rule controlling access to the user update

operation as::



    user == target or rule("is_admin")



Note that any authorization attributes on the "is_admin" rule will be

ignored; to set an authorization attribute on the user update

operation, they have to be explicitly declared::



    user == target or rule("is_admin") {{ payment=rule("is_admin"),

                                          name=user==target }}



Available Builtins

------------------



The following Python builtins are available:



* ``abs()``

* ``basestring()``

* ``bin()``

* ``bool()``

* ``bytes()``

* ``callable()``

* ``chr()``

* ``complex()``

* ``dict()``

* ``divmod()``

* ``enumerate()``

* ``float()``

* ``format()``

* ``frozenset()``

* ``getattr()``

* ``hasattr()``

* ``hash()``

* ``hex()``

* ``id()``

* ``int()``

* ``isinstance()``

* ``issubclass()``

* ``iter()``

* ``len()``

* ``list()``

* ``long()``

* ``max()``

* ``min()``

* ``next()``

* ``object()``

* ``oct()``

* ``ord()``

* ``pow()``

* ``range()``

* ``repr()``

* ``reversed()``

* ``round()``

* ``set()``

* ``sorted()``

* ``str()``

* ``sum()``

* ``tuple()``

* ``type()``

* ``unichr()``

* ``unicode()``

* ``xrange()``

* ``zip()``



Advanced Function Calls

=======================



Under normal circumstances, functions are called with only the

arguments passed in the rule text, and their return values are then

pushed onto the stack in place of those function arguments.  However,

certain functions--such as the ``rule()`` function--need access to the

context object (``policies.PolicyContext``).  In the case of

``rule()``, this allows it to keep a cache of rules that have been

evaluated for the duration of the ``policies.Policy.evaluate()`` call,

as well as looking up the rule to be evaluated.



To facilitate functions like ``rule()``, use the

``@policies.want_context`` decorator.  The ``policies.PolicyContext``

object will be passed as the first argument of the function, with

remaining arguments passed after that.  Note that all the arguments

will be popped off the stack, but the function's return value will

*not* be pushed on the stack; a function decorated with

``@policies.want_context`` must perform its own manipulation of the

stack.  For a function like this to push a return value on the stack,

and assuming that the context argument is ``ctxt``, the relevant code

would be::



    ctxt.stack.append("value")



In instances where you're using functions decorated with

``@policies.want_context``, it may be necessary to perform some

application-specific initialization on the ``policies.PolicyContext``

class, such as initializing a context attribute.  This may be done by

changing the ``policies.Policy.context_class`` setting.  Ideally, this

would be on an instance of ``policies.Policy``, rather than altering

the class itself, i.e.::



    policy = policies.Policy(...)

    policy.context_class = MyPolicyContext



Be very careful using ``@policies.want_context``.  Failing to push a

function return value onto the evaluation context stack could corrupt

the stack and cause a crash during rule evaluation.



``policies`` Internals

======================



This section intended for developers interested in developing the

``policies`` package itself.



Rule Parsing

------------



The policy rules work by parsing the rule text, using a parser built

with ``pyparsing``, into a sequence of *instructions*.  The

instructions are stored in postfix order; that is, an expression like

"1+2" would become a sequence of instructions that would first push

the value "1" onto a stack; then push the value "2" onto the stack;

then pop the top two values from the stack, add them, and push the

result onto the stack.  The instructions are all defined in

``instructions.py``, and the parser is defined in ``parser.py``.  The

``policies.Policy.evaluate()`` method simply constructs an evaluation

context (a ``policies.policy.PolicyContext`` object), then executes

the instructions.  Included in the instructions are instructions that

create a ``policy.Authorization`` object and set up the authorization

attributes (if any were defined); this authorization object is then

returned.



Caching

-------



Caching is used wherever possible to achieve the highest possible

efficiency.  Policy rules are compiled the first time they are

evaluated, and the instructions are then cached.  The results of an

entrypoint look-up are also cached, as are the results of calling

rules--in the example above::



    user == target or rule("is_admin") {{ payment=rule("is_admin"),

                                          name=user==target }}



The "is_admin" rule will only be evaluated one time.  This cache is

stored in the ``policies.PolicyContext`` object, in the ``rule_cache``

attribute.



.. _entrypoints: http://pythonhosted.org/distribute/pkg_resources.html#entry-points