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An access policy language evaluator.

Project description

A package for interpretation and enforcement of access control policies.


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()")

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
    # Tell the user he's unprivileged

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:


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, and the parser is defined in 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 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.

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