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feat: fine grained access control policy (#2264)

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This allows a set of rules to be defined for determining access to
resources. The rules are (loosely) based on the cedar policy format.

A rule defines a list of action either to permit or to forbid. It may
specify a principal or a resource that must match for the rule to take
effect. It may also specify a condition, either a 'when' or an 'unless',
with additional constraints as to where the rule applies.

A list of rules is held for each type to be protected and tried in order
to find a match. If a match is found, the request is permitted or
forbidden depening on the type of rule. If no match is found, the
request is denied. If no rules are specified for a given type, a rule
that allows any action as long as the resource attributes match the user
attributes is added (i.e. the previous behaviour is the default.

Some examples in yaml:

```
    model:
    - permit:
      principal: user-1
      actions: [create, read, delete]
      comment: user-1 has full access to all models
    - permit:
      principal: user-2
      actions: [read]
      resource: model-1
      comment: user-2 has read access to model-1 only
    - permit:
      actions: [read]
      when:
        user_in: resource.namespaces
      comment: any user has read access to models with matching attributes
    vector_db:
    - forbid:
      actions: [create, read, delete]
      unless:
        user_in: role::admin
      comment: only user with admin role can use vector_db resources
```

---------

Signed-off-by: Gordon Sim <gsim@redhat.com>
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grs 2025-06-03 17:51:12 -04:00 • committed by GitHub
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commit 7c1998db25
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4
llama_stack/distribution/routing_tables/models.py
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@ -9,7 +9,7 @@ from typing import Any
from llama_stack.apis.models import ListModelsResponse, Model, Models, ModelType, OpenAIListModelsResponse, OpenAIModel
from llama_stack.distribution.datatypes import (
ModelWithACL,
ModelWithOwner,
)
from llama_stack.log import get_logger
@ -65,7 +65,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
model_type = ModelType.llm
if "embedding_dimension" not in metadata and model_type == ModelType.embedding:
raise ValueError("Embedding model must have an embedding dimension in its metadata")
model = ModelWithACL(
model = ModelWithOwner(
identifier=model_id,
provider_resource_id=provider_model_id,
provider_id=provider_id,