REST API Policy Enforcement¶
The following describes some of the shortcomings in how policy is used and enforced in nova, along with some benefits of fixing those issues. Each issue has a section dedicated to describing the underlying cause and historical context in greater detail.
Problems with current system¶
The following is a list of issues with the existing policy enforcement system:
Addressing the list above helps operators by:
Providing them with flexible and useful defaults
Reducing the likelihood of writing and maintaining custom policies
Improving interoperability between deployments
Increasing RBAC confidence through first-class testing and verification
Reducing complexity by using consistent policy naming conventions
Exposing more functionality to end-users, safely, making the entire nova API more self-serviceable resulting in less operational overhead for operators to do things on behalf of users
Additionally, the following is a list of benefits to contributors:
Reduce developer maintenance and cost by isolating policy enforcement into a single layer
Reduce complexity by using consistent policy naming conventions
Increased confidence in RBAC refactoring through exhaustive testing that prevents regressions before they merge
Testing default policies¶
Testing default policies is important in protecting against authoritative regression. Authoritative regression is when a change accidentally allows someone to do something or see something they shouldn’t. It can also be when a change accidentally restricts a user from doing something they used to have the authorization to perform. This testing is especially useful prior to refactoring large parts of the policy system. For example, this level of testing would be invaluable prior to pulling policy enforcement logic from the database layer up to the API layer.
Testing documentation exists that describes the process for developing these types of tests.
Inconsistent conventions for policy names are scattered across most OpenStack services, nova included. Recently, there was an effort that introduced a convention that factored in service names, resources, and use cases. This new convention is applicable to nova policy names. The convention is formally documented in oslo.policy and we can use policy deprecation tooling to gracefully rename policies.
Incorporating default roles¶
Up until the Rocky release, keystone only ensured a single role called
was available to the deployment upon installation. In Rocky, this support was
expanded to include
reader roles as first-class citizens during
keystone’s installation. This allows service developers to rely on these roles
and include them in their default policy definitions. Standardizing on a set of
role names for default policies increases interoperability between deployments
and decreases operator overhead.
Compartmentalized policy enforcement¶
Policy logic and processing is inherently sensitive and often complicated. It is sensitive in that coding mistakes can lead to security vulnerabilities. It is complicated in the resources and APIs it needs to protect and the vast number of use cases it needs to support. These reasons make a case for isolating policy enforcement and processing into a compartmentalized space, as opposed to policy logic bleeding through to different layers of nova. Not having all policy logic in a single place makes evolving the policy enforcement system arduous and makes the policy system itself fragile.
Currently, the database and API components of nova contain policy logic. At some point, we should refactor these systems into a single component that is easier to maintain. Before we do this, we should consider approaches for bolstering testing coverage, which ensures we are aware of or prevent policy regressions. There are examples and documentation in API protection testing guides.
Refactoring hard-coded permission checks¶
The policy system in nova is designed to be configurable. Despite this design, there are some APIs that have hard-coded checks for specific roles. This makes configuration impossible, misleading, and frustrating for operators. Instead, we can remove hard-coded policies and ensure a configuration-driven approach, which reduces technical debt, increases consistency, and provides better user-experience for operators. Additionally, moving hard-coded checks into first-class policy rules let us use existing policy tooling to deprecate, document, and evolve policies.
Granular policy checks¶
Policies should be as granular as possible to ensure consistency and reasonable
defaults. Using a single policy to protect CRUD for an entire API is
restrictive because it prevents us from using default roles to make delegation
to that API flexible. For example, a policy for
compute:foobar could be
Breaking policies down this way allows us to set read-only policies for
readable operations or use another default role for creation and management of
foobar resources. The oslo.policy library has examples that show how to do
this using deprecated policy rules.