fix: prevent telemetry from leaking sensitive info

Prevent sensitive information from being logged in telemetry output by
assigning SecretStr type to sensitive fields. API keys, password from
KV store are now covered. All providers have been converted.

Signed-off-by: Sébastien Han <seb@redhat.com>

docs/docs/providers/files/remote_s3.mdx

@@ -17,7 +17,7 @@ AWS S3-based file storage provider for scalable cloud file management with metad
 | `bucket_name` | `<class 'str'>` | No |  | S3 bucket name to store files |
 | `region` | `<class 'str'>` | No | us-east-1 | AWS region where the bucket is located |
 | `aws_access_key_id` | `str \| None` | No |  | AWS access key ID (optional if using IAM roles) |
-| `aws_secret_access_key` | `str \| None` | No |  | AWS secret access key (optional if using IAM roles) |
+| `aws_secret_access_key` | `pydantic.types.SecretStr \| None` | No |  | AWS secret access key (optional if using IAM roles) |
 | `endpoint_url` | `str \| None` | No |  | Custom S3 endpoint URL (for MinIO, LocalStack, etc.) |
 | `auto_create_bucket` | `<class 'bool'>` | No | False | Automatically create the S3 bucket if it doesn't exist |
 | `metadata_store` | `utils.sqlstore.sqlstore.SqliteSqlStoreConfig \| utils.sqlstore.sqlstore.PostgresSqlStoreConfig` | No | sqlite | SQL store configuration for file metadata |

docs/docs/providers/inference/remote_anthropic.mdx

@@ -14,7 +14,7 @@ Anthropic inference provider for accessing Claude models and Anthropic's AI serv
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `api_key` | `str \| None` | No |  | API key for Anthropic models |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | API key for Anthropic models |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_bedrock.mdx

@@ -15,8 +15,8 @@ AWS Bedrock inference provider for accessing various AI models through AWS's man
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `aws_access_key_id` | `str \| None` | No |  | The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID |
-| `aws_secret_access_key` | `str \| None` | No |  | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
+| `aws_secret_access_key` | `pydantic.types.SecretStr \| None` | No |  | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
-| `aws_session_token` | `str \| None` | No |  | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |
+| `aws_session_token` | `pydantic.types.SecretStr \| None` | No |  | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |
 | `region_name` | `str \| None` | No |  | The default AWS Region to use, for example, us-west-1 or us-west-2.Default use environment variable: AWS_DEFAULT_REGION |
 | `profile_name` | `str \| None` | No |  | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE |
 | `total_max_attempts` | `int \| None` | No |  | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS |

docs/docs/providers/inference/remote_gemini.mdx

@@ -14,7 +14,7 @@ Google Gemini inference provider for accessing Gemini models and Google's AI ser
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `api_key` | `str \| None` | No |  | API key for Gemini models |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | API key for Gemini models |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_groq.mdx

@@ -14,7 +14,7 @@ Groq inference provider for ultra-fast inference using Groq's LPU technology.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `api_key` | `str \| None` | No |  | The Groq API key |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | The Groq API key |
 | `url` | `<class 'str'>` | No | https://api.groq.com | The URL for the Groq AI server |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_llama-openai-compat.mdx

@@ -14,7 +14,7 @@ Llama OpenAI-compatible provider for using Llama models with OpenAI API format.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `api_key` | `str \| None` | No |  | The Llama API key |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | The Llama API key |
 | `openai_compat_api_base` | `<class 'str'>` | No | https://api.llama.com/compat/v1/ | The URL for the Llama API server |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_openai.mdx

@@ -14,7 +14,7 @@ OpenAI inference provider for accessing GPT models and other OpenAI services.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `api_key` | `str \| None` | No |  | API key for OpenAI models |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | API key for OpenAI models |
 | `base_url` | `<class 'str'>` | No | https://api.openai.com/v1 | Base URL for OpenAI API |
 
 ## Sample Configuration

docs/docs/providers/inference/remote_runpod.mdx

@@ -15,7 +15,7 @@ RunPod inference provider for running models on RunPod's cloud GPU platform.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `url` | `str \| None` | No |  | The URL for the Runpod model serving endpoint |
-| `api_token` | `str \| None` | No |  | The API token |
+| `api_token` | `pydantic.types.SecretStr \| None` | No |  | The API token |
 
 ## Sample Configuration
 

docs/docs/providers/inference/remote_vllm.mdx

@@ -16,7 +16,7 @@ Remote vLLM inference provider for connecting to vLLM servers.
 |-------|------|----------|---------|-------------|
 | `url` | `str \| None` | No |  | The URL for the vLLM model serving endpoint |
 | `max_tokens` | `<class 'int'>` | No | 4096 | Maximum number of tokens to generate. |
-| `api_token` | `str \| None` | No | fake | The API token |
+| `api_token` | `pydantic.types.SecretStr \| None` | No | ********** | The API token |
 | `tls_verify` | `bool \| str` | No | True | Whether to verify TLS certificates. Can be a boolean or a path to a CA certificate file. |
 | `refresh_models` | `<class 'bool'>` | No | False | Whether to refresh models periodically |
 

docs/docs/providers/safety/remote_bedrock.mdx

@@ -15,8 +15,8 @@ AWS Bedrock safety provider for content moderation using AWS's safety services.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `aws_access_key_id` | `str \| None` | No |  | The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID |
-| `aws_secret_access_key` | `str \| None` | No |  | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
+| `aws_secret_access_key` | `pydantic.types.SecretStr \| None` | No |  | The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY |
-| `aws_session_token` | `str \| None` | No |  | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |
+| `aws_session_token` | `pydantic.types.SecretStr \| None` | No |  | The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN |
 | `region_name` | `str \| None` | No |  | The default AWS Region to use, for example, us-west-1 or us-west-2.Default use environment variable: AWS_DEFAULT_REGION |
 | `profile_name` | `str \| None` | No |  | The profile name that contains credentials to use.Default use environment variable: AWS_PROFILE |
 | `total_max_attempts` | `int \| None` | No |  | An integer representing the maximum number of attempts that will be made for a single request, including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS |

docs/docs/providers/scoring/inline_braintrust.mdx

@@ -14,7 +14,7 @@ Braintrust scoring provider for evaluation and scoring using the Braintrust plat
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `openai_api_key` | `str \| None` | No |  | The OpenAI API Key |
+| `openai_api_key` | `pydantic.types.SecretStr \| None` | No |  | The OpenAI API Key |
 
 ## Sample Configuration
 

docs/docs/providers/tool_runtime/remote_bing-search.mdx

@@ -14,7 +14,7 @@ Bing Search tool for web search capabilities using Microsoft's search engine.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `api_key` | `str \| None` | No |  |  |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  |  |
 | `top_k` | `<class 'int'>` | No | 3 |  |
 
 ## Sample Configuration

docs/docs/providers/tool_runtime/remote_brave-search.mdx

@@ -14,7 +14,7 @@ Brave Search tool for web search capabilities with privacy-focused results.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `api_key` | `str \| None` | No |  | The Brave Search API Key |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | The Brave Search API Key |
 | `max_results` | `<class 'int'>` | No | 3 | The maximum number of results to return |
 
 ## Sample Configuration

docs/docs/providers/tool_runtime/remote_tavily-search.mdx

@@ -14,7 +14,7 @@ Tavily Search tool for AI-optimized web search with structured results.
 
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
-| `api_key` | `str \| None` | No |  | The Tavily Search API Key |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | The Tavily Search API Key |
 | `max_results` | `<class 'int'>` | No | 3 | The maximum number of results to return |
 
 ## Sample Configuration

docs/docs/providers/vector_io/remote_pgvector.mdx

@@ -217,7 +217,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
 | `port` | `int \| None` | No | 5432 |  |
 | `db` | `str \| None` | No | postgres |  |
 | `user` | `str \| None` | No | postgres |  |
-| `password` | `str \| None` | No | mysecretpassword |  |
+| `password` | `pydantic.types.SecretStr \| None` | No | mysecretpassword |  |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No |  | Config for KV store backend (SQLite only for now) |
 
 ## Sample Configuration

llama_stack/providers/inline/scoring/braintrust/__init__.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, SecretStr
 
 from llama_stack.core.datatypes import Api
 
@@ -13,7 +13,7 @@ from .config import BraintrustScoringConfig
 
 
 class BraintrustProviderDataValidator(BaseModel):
-    openai_api_key: str
+    openai_api_key: SecretStr
 
 
 async def get_provider_impl(

llama_stack/providers/inline/scoring/braintrust/braintrust.py

@@ -17,7 +17,7 @@ from autoevals.ragas import (
     ContextRelevancy,
     Faithfulness,
 )
-from pydantic import BaseModel
+from pydantic import BaseModel, SecretStr
 
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.datasets import Datasets
@@ -152,9 +152,9 @@ class BraintrustScoringImpl(
                 raise ValueError(
                     'Pass OpenAI API Key in the header X-LlamaStack-Provider-Data as { "openai_api_key": <your api key>}'
                 )
-            self.config.openai_api_key = provider_data.openai_api_key
+            self.config.openai_api_key = SecretStr(provider_data.openai_api_key)
 
-        os.environ["OPENAI_API_KEY"] = self.config.openai_api_key
+        os.environ["OPENAI_API_KEY"] = self.config.openai_api_key.get_secret_value()
 
     async def score_batch(
         self,

llama_stack/providers/inline/scoring/braintrust/config.py

@@ -5,11 +5,11 @@
 # the root directory of this source tree.
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 
 class BraintrustScoringConfig(BaseModel):
-    openai_api_key: str | None = Field(
+    openai_api_key: SecretStr | None = Field(
         default=None,
         description="The OpenAI API Key",
     )

llama_stack/providers/inline/telemetry/meta_reference/console_span_processor.py

@@ -64,7 +64,9 @@ class ConsoleSpanProcessor(SpanProcessor):
                 for key, value in event.attributes.items():
                     if key.startswith("__") or key in ["message", "severity"]:
                         continue
-                    logger.info(f"[dim]{key}[/dim]: {value}")
+
+                    str_value = str(value)
+                    logger.info(f"[dim]{key}[/dim]: {str_value}")
 
     def shutdown(self) -> None:
         """Shutdown the processor."""

llama_stack/providers/remote/files/s3/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig
 
@@ -17,7 +17,7 @@ class S3FilesImplConfig(BaseModel):
     bucket_name: str = Field(description="S3 bucket name to store files")
     region: str = Field(default="us-east-1", description="AWS region where the bucket is located")
     aws_access_key_id: str | None = Field(default=None, description="AWS access key ID (optional if using IAM roles)")
-    aws_secret_access_key: str | None = Field(
+    aws_secret_access_key: SecretStr | None = Field(
         default=None, description="AWS secret access key (optional if using IAM roles)"
     )
     endpoint_url: str | None = Field(default=None, description="Custom S3 endpoint URL (for MinIO, LocalStack, etc.)")

llama_stack/providers/remote/files/s3/files.py

@@ -46,7 +46,7 @@ def _create_s3_client(config: S3FilesImplConfig) -> boto3.client:
             s3_config.update(
                 {
                     "aws_access_key_id": config.aws_access_key_id,
-                    "aws_secret_access_key": config.aws_secret_access_key,
+                    "aws_secret_access_key": config.aws_secret_access_key.get_secret_value(),
                 }
             )
 

llama_stack/providers/remote/inference/anthropic/anthropic.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
@@ -27,7 +28,7 @@ class AnthropicInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             litellm_provider_name="anthropic",
-            api_key_from_config=config.api_key,
+            api_key_from_config=config.api_key.get_secret_value() if config.api_key else None,
             provider_data_api_key_field="anthropic_api_key",
         )
         self.config = config

llama_stack/providers/remote/inference/anthropic/config.py

@@ -6,13 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.schema_utils import json_schema_type
 
 
 class AnthropicProviderDataValidator(BaseModel):
-    anthropic_api_key: str | None = Field(
+    anthropic_api_key: SecretStr | None = Field(
         default=None,
         description="API key for Anthropic models",
     )
@@ -20,7 +20,7 @@ class AnthropicProviderDataValidator(BaseModel):
 
 @json_schema_type
 class AnthropicConfig(BaseModel):
-    api_key: str | None = Field(
+    api_key: SecretStr | None = Field(
         default=None,
         description="API key for Anthropic models",
     )

llama_stack/providers/remote/inference/gemini/config.py

@@ -6,13 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.schema_utils import json_schema_type
 
 
 class GeminiProviderDataValidator(BaseModel):
-    gemini_api_key: str | None = Field(
+    gemini_api_key: SecretStr | None = Field(
         default=None,
         description="API key for Gemini models",
     )
@@ -20,7 +20,7 @@ class GeminiProviderDataValidator(BaseModel):
 
 @json_schema_type
 class GeminiConfig(BaseModel):
-    api_key: str | None = Field(
+    api_key: SecretStr | None = Field(
         default=None,
         description="API key for Gemini models",
     )

llama_stack/providers/remote/inference/gemini/gemini.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
@@ -19,7 +20,7 @@ class GeminiInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         LiteLLMOpenAIMixin.__init__(
             self,
             litellm_provider_name="gemini",
-            api_key_from_config=config.api_key,
+            api_key_from_config=config.api_key.get_secret_value() if config.api_key else None,
             provider_data_api_key_field="gemini_api_key",
         )
         self.config = config

llama_stack/providers/remote/inference/groq/config.py

@@ -6,13 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.schema_utils import json_schema_type
 
 
 class GroqProviderDataValidator(BaseModel):
-    groq_api_key: str | None = Field(
+    groq_api_key: SecretStr | None = Field(
         default=None,
         description="API key for Groq models",
     )
@@ -20,7 +20,7 @@ class GroqProviderDataValidator(BaseModel):
 
 @json_schema_type
 class GroqConfig(BaseModel):
-    api_key: str | None = Field(
+    api_key: SecretStr | None = Field(
         # The Groq client library loads the GROQ_API_KEY environment variable by default
         default=None,
         description="The Groq API key",

llama_stack/providers/remote/inference/llama_openai_compat/config.py

@@ -6,13 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.schema_utils import json_schema_type
 
 
 class LlamaProviderDataValidator(BaseModel):
-    llama_api_key: str | None = Field(
+    llama_api_key: SecretStr | None = Field(
         default=None,
         description="API key for api.llama models",
     )
@@ -20,7 +20,7 @@ class LlamaProviderDataValidator(BaseModel):
 
 @json_schema_type
 class LlamaCompatConfig(BaseModel):
-    api_key: str | None = Field(
+    api_key: SecretStr | None = Field(
         default=None,
         description="The Llama API key",
     )

llama_stack/providers/remote/inference/openai/config.py

@@ -6,13 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.schema_utils import json_schema_type
 
 
 class OpenAIProviderDataValidator(BaseModel):
-    openai_api_key: str | None = Field(
+    openai_api_key: SecretStr | None = Field(
         default=None,
         description="API key for OpenAI models",
     )
@@ -20,7 +20,7 @@ class OpenAIProviderDataValidator(BaseModel):
 
 @json_schema_type
 class OpenAIConfig(BaseModel):
-    api_key: str | None = Field(
+    api_key: SecretStr | None = Field(
         default=None,
         description="API key for OpenAI models",
     )

llama_stack/providers/remote/inference/runpod/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.schema_utils import json_schema_type
 
@@ -17,7 +17,7 @@ class RunpodImplConfig(BaseModel):
         default=None,
         description="The URL for the Runpod model serving endpoint",
     )
-    api_token: str | None = Field(
+    api_token: SecretStr | None = Field(
         default=None,
         description="The API token",
     )

llama_stack/providers/remote/inference/runpod/runpod.py

@@ -103,7 +103,10 @@ class RunpodInferenceAdapter(
             tool_config=tool_config,
         )
 
-        client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
+        client = OpenAI(
+            base_url=self.config.url,
+            api_key=self.config.api_token.get_secret_value() if self.config.api_token else None,
+        )
         if stream:
             return self._stream_chat_completion(request, client)
         else:

llama_stack/providers/remote/inference/vertexai/vertexai.py

@@ -8,6 +8,7 @@ from typing import Any
 
 import google.auth.transport.requests
 from google.auth import default
+from pydantic import SecretStr
 
 from llama_stack.apis.inference import ChatCompletionRequest
 from llama_stack.providers.utils.inference.litellm_openai_mixin import (
@@ -43,7 +44,7 @@ class VertexAIInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):
         except Exception:
             # If we can't get credentials, return empty string to let LiteLLM handle it
             # This allows the LiteLLM mixin to work with ADC directly
-            return ""
+            return SecretStr("")
 
     def get_base_url(self) -> str:
         """

llama_stack/providers/remote/inference/vllm/config.py

@@ -6,7 +6,7 @@
 
 from pathlib import Path
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field, SecretStr, field_validator
 
 from llama_stack.schema_utils import json_schema_type
 
@@ -21,8 +21,8 @@ class VLLMInferenceAdapterConfig(BaseModel):
         default=4096,
         description="Maximum number of tokens to generate.",
     )
-    api_token: str | None = Field(
+    api_token: SecretStr | None = Field(
-        default="fake",
+        default=SecretStr("fake"),
         description="The API token",
     )
     tls_verify: bool | str = Field(

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -294,7 +294,7 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro
             self,
             model_entries=build_hf_repo_model_entries(),
             litellm_provider_name="vllm",
-            api_key_from_config=config.api_token,
+            api_key_from_config=config.api_token.get_secret_value(),
             provider_data_api_key_field="vllm_api_token",
             openai_compat_api_base=config.url,
         )

llama_stack/providers/remote/tool_runtime/bing_search/bing_search.py

@@ -40,7 +40,7 @@ class BingSearchToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsReq
 
     def _get_api_key(self) -> str:
         if self.config.api_key:
-            return self.config.api_key
+            return self.config.api_key.get_secret_value()
 
         provider_data = self.get_request_provider_data()
         if provider_data is None or not provider_data.bing_search_api_key:

llama_stack/providers/remote/tool_runtime/bing_search/config.py

@@ -6,13 +6,13 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, SecretStr
 
 
 class BingSearchToolConfig(BaseModel):
     """Configuration for Bing Search Tool Runtime"""
 
-    api_key: str | None = None
+    api_key: SecretStr | None = None
     top_k: int = 3
 
     @classmethod

llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py

@@ -39,7 +39,7 @@ class BraveSearchToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsRe
 
     def _get_api_key(self) -> str:
         if self.config.api_key:
-            return self.config.api_key
+            return self.config.api_key.get_secret_value()
 
         provider_data = self.get_request_provider_data()
         if provider_data is None or not provider_data.brave_search_api_key:

llama_stack/providers/remote/tool_runtime/brave_search/config.py

@@ -6,11 +6,11 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 
 class BraveSearchToolConfig(BaseModel):
-    api_key: str | None = Field(
+    api_key: SecretStr | None = Field(
         default=None,
         description="The Brave Search API Key",
     )

llama_stack/providers/remote/tool_runtime/tavily_search/config.py

@@ -6,11 +6,11 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 
 class TavilySearchToolConfig(BaseModel):
-    api_key: str | None = Field(
+    api_key: SecretStr | None = Field(
         default=None,
         description="The Tavily Search API Key",
     )

llama_stack/providers/remote/tool_runtime/tavily_search/tavily_search.py

@@ -39,7 +39,7 @@ class TavilySearchToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsR
 
     def _get_api_key(self) -> str:
         if self.config.api_key:
-            return self.config.api_key
+            return self.config.api_key.get_secret_value()
 
         provider_data = self.get_request_provider_data()
         if provider_data is None or not provider_data.tavily_search_api_key:

llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py

@@ -40,7 +40,7 @@ class WolframAlphaToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, NeedsR
 
     def _get_api_key(self) -> str:
         if self.config.api_key:
-            return self.config.api_key
+            return self.config.api_key.get_secret_value()
 
         provider_data = self.get_request_provider_data()
         if provider_data is None or not provider_data.wolfram_alpha_api_key:

llama_stack/providers/remote/vector_io/pgvector/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.providers.utils.kvstore.config import (
     KVStoreConfig,
@@ -21,7 +21,7 @@ class PGVectorVectorIOConfig(BaseModel):
     port: int | None = Field(default=5432)
     db: str | None = Field(default="postgres")
     user: str | None = Field(default="postgres")
-    password: str | None = Field(default="mysecretpassword")
+    password: SecretStr | None = Field(default="mysecretpassword")
     kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None)
 
     @classmethod

llama_stack/providers/remote/vector_io/pgvector/pgvector.py

@@ -366,7 +366,7 @@ class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoco
                 port=self.config.port,
                 database=self.config.db,
                 user=self.config.user,
-                password=self.config.password,
+                password=self.config.password.get_secret_value(),
             )
             self.conn.autocommit = True
             with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:

llama_stack/providers/utils/bedrock/client.py

@@ -50,8 +50,8 @@ def create_bedrock_client(config: BedrockBaseConfig, service_name: str = "bedroc
 
         session_args = {
             "aws_access_key_id": config.aws_access_key_id,
-            "aws_secret_access_key": config.aws_secret_access_key,
+            "aws_secret_access_key": config.aws_secret_access_key.get_secret_value(),
-            "aws_session_token": config.aws_session_token,
+            "aws_session_token": config.aws_session_token.get_secret_value(),
             "region_name": config.region_name,
             "profile_name": config.profile_name,
             "session_ttl": config.session_ttl,

llama_stack/providers/utils/bedrock/config.py

@@ -6,7 +6,7 @@
 
 import os
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 
 class BedrockBaseConfig(BaseModel):
@@ -14,12 +14,12 @@ class BedrockBaseConfig(BaseModel):
         default_factory=lambda: os.getenv("AWS_ACCESS_KEY_ID"),
         description="The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID",
     )
-    aws_secret_access_key: str | None = Field(
+    aws_secret_access_key: SecretStr | None = Field(
-        default_factory=lambda: os.getenv("AWS_SECRET_ACCESS_KEY"),
+        default_factory=lambda: SecretStr(val) if (val := os.getenv("AWS_SECRET_ACCESS_KEY")) else None,
         description="The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY",
     )
-    aws_session_token: str | None = Field(
+    aws_session_token: SecretStr | None = Field(
-        default_factory=lambda: os.getenv("AWS_SESSION_TOKEN"),
+        default_factory=lambda: SecretStr(val) if (val := os.getenv("AWS_SESSION_TOKEN")) else None,
         description="The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN",
     )
     region_name: str | None = Field(

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -8,6 +8,7 @@ from collections.abc import AsyncGenerator, AsyncIterator
 from typing import Any
 
 import litellm
+from pydantic import SecretStr
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -68,7 +69,7 @@ class LiteLLMOpenAIMixin(
     def __init__(
         self,
         litellm_provider_name: str,
-        api_key_from_config: str | None,
+        api_key_from_config: SecretStr | None,
         provider_data_api_key_field: str,
         model_entries: list[ProviderModelEntry] | None = None,
         openai_compat_api_base: str | None = None,
@@ -247,14 +248,14 @@ class LiteLLMOpenAIMixin(
 
         return {
             "model": request.model,
-            "api_key": self.get_api_key(),
+            "api_key": self.get_api_key().get_secret_value(),
             "api_base": self.api_base,
             **input_dict,
             "stream": request.stream,
             **get_sampling_options(request.sampling_params),
         }
 
-    def get_api_key(self) -> str:
+    def get_api_key(self) -> SecretStr:
         provider_data = self.get_request_provider_data()
         key_field = self.provider_data_api_key_field
         if provider_data and getattr(provider_data, key_field, None):
@@ -305,7 +306,7 @@ class LiteLLMOpenAIMixin(
         response = litellm.embedding(
             model=self.get_litellm_model_name(model_obj.provider_resource_id),
             input=input_list,
-            api_key=self.get_api_key(),
+            api_key=self.get_api_key().get_secret_value(),
             api_base=self.api_base,
             dimensions=dimensions,
         )
@@ -368,7 +369,7 @@ class LiteLLMOpenAIMixin(
             user=user,
             guided_choice=guided_choice,
             prompt_logprobs=prompt_logprobs,
-            api_key=self.get_api_key(),
+            api_key=self.get_api_key().get_secret_value(),
             api_base=self.api_base,
         )
         return await litellm.atext_completion(**params)
@@ -424,7 +425,7 @@ class LiteLLMOpenAIMixin(
             top_logprobs=top_logprobs,
             top_p=top_p,
             user=user,
-            api_key=self.get_api_key(),
+            api_key=self.get_api_key().get_secret_value(),
             api_base=self.api_base,
         )
         return await litellm.acompletion(**params)

llama_stack/providers/utils/inference/openai_mixin.py

@@ -11,6 +11,7 @@ from collections.abc import AsyncIterator
 from typing import Any
 
 from openai import NOT_GIVEN, AsyncOpenAI
+from pydantic import SecretStr
 
 from llama_stack.apis.inference import (
     Model,
@@ -70,14 +71,14 @@ class OpenAIMixin(ModelRegistryHelper, ABC):
     allowed_models: list[str] = []
 
     @abstractmethod
-    def get_api_key(self) -> str:
+    def get_api_key(self) -> SecretStr:
         """
         Get the API key.
 
         This method must be implemented by child classes to provide the API key
         for authenticating with the OpenAI API or compatible endpoints.
 
-        :return: The API key as a string
+        :return: The API key as a SecretStr
         """
         pass
 

llama_stack/providers/utils/kvstore/config.py

@@ -8,7 +8,7 @@ import re
 from enum import Enum
 from typing import Annotated, Literal
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import BaseModel, Field, SecretStr, field_validator
 
 from llama_stack.core.utils.config_dirs import RUNTIME_BASE_DIR
 
@@ -74,7 +74,7 @@ class PostgresKVStoreConfig(CommonConfig):
     port: int = 5432
     db: str = "llamastack"
     user: str
-    password: str | None = None
+    password: SecretStr | None = None
     ssl_mode: str | None = None
     ca_cert_path: str | None = None
     table_name: str = "llamastack_kvstore"
@@ -118,7 +118,7 @@ class MongoDBKVStoreConfig(CommonConfig):
     port: int = 27017
     db: str = "llamastack"
     user: str | None = None
-    password: str | None = None
+    password: SecretStr | None = None
     collection_name: str = "llamastack_kvstore"
 
     @classmethod

llama_stack/providers/utils/kvstore/mongodb/mongodb.py

@@ -34,7 +34,7 @@ class MongoDBKVStoreImpl(KVStore):
                 "host": self.config.host,
                 "port": self.config.port,
                 "username": self.config.user,
-                "password": self.config.password,
+                "password": self.config.password.get_secret_value(),
             }
             conn_creds = {k: v for k, v in conn_creds.items() if v is not None}
             self.conn = AsyncMongoClient(**conn_creds)

llama_stack/providers/utils/kvstore/postgres/postgres.py

@@ -30,7 +30,7 @@ class PostgresKVStoreImpl(KVStore):
                 port=self.config.port,
                 database=self.config.db,
                 user=self.config.user,
-                password=self.config.password,
+                password=self.config.password.get_secret_value(),
                 sslmode=self.config.ssl_mode,
                 sslrootcert=self.config.ca_cert_path,
             )

llama_stack/providers/utils/sqlstore/sqlstore.py

@@ -9,7 +9,7 @@ from enum import StrEnum
 from pathlib import Path
 from typing import Annotated, Literal
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.core.utils.config_dirs import RUNTIME_BASE_DIR
 
@@ -63,11 +63,11 @@ class PostgresSqlStoreConfig(SqlAlchemySqlStoreConfig):
     port: int = 5432
     db: str = "llamastack"
     user: str
-    password: str | None = None
+    password: SecretStr | None = None
 
     @property
     def engine_str(self) -> str:
-        return f"postgresql+asyncpg://{self.user}:{self.password}@{self.host}:{self.port}/{self.db}"
+        return f"postgresql+asyncpg://{self.user}:{self.password.get_secret_value() if self.password else ''}@{self.host}:{self.port}/{self.db}"
 
     @classmethod
     def pip_packages(cls) -> list[str]:

tests/unit/providers/inference/test_inference_client_caching.py

@@ -33,7 +33,7 @@ def test_groq_provider_openai_client_caching():
         with request_provider_data_context(
             {"x-llamastack-provider-data": json.dumps({inference_adapter.provider_data_api_key_field: api_key})}
         ):
-            assert inference_adapter.client.api_key == api_key
+            assert inference_adapter.client.api_key.get_secret_value() == api_key
 
 
 def test_openai_provider_openai_client_caching():
@@ -52,7 +52,7 @@ def test_openai_provider_openai_client_caching():
             {"x-llamastack-provider-data": json.dumps({inference_adapter.provider_data_api_key_field: api_key})}
         ):
             openai_client = inference_adapter.client
-            assert openai_client.api_key == api_key
+            assert openai_client.api_key.get_secret_value() == api_key
 
 
 def test_together_provider_openai_client_caching():
@@ -86,4 +86,4 @@ def test_llama_compat_provider_openai_client_caching():
 
     for api_key in ["test1", "test2"]:
         with request_provider_data_context({"x-llamastack-provider-data": json.dumps({"llama_api_key": api_key})}):
-            assert inference_adapter.client.api_key == api_key
+            assert inference_adapter.client.api_key.get_secret_value() == api_key

tests/unit/providers/inference/test_litellm_openai_mixin.py

@@ -8,7 +8,7 @@ import json
 from unittest.mock import MagicMock
 
 import pytest
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, SecretStr
 
 from llama_stack.core.request_headers import request_provider_data_context
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
@@ -16,11 +16,11 @@ from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOp
 
 # Test fixtures and helper classes
 class TestConfig(BaseModel):
-    api_key: str | None = Field(default=None)
+    api_key: SecretStr | None = Field(default=None)
 
 
 class TestProviderDataValidator(BaseModel):
-    test_api_key: str | None = Field(default=None)
+    test_api_key: SecretStr | None = Field(default=None)
 
 
 class TestLiteLLMAdapter(LiteLLMOpenAIMixin):
@@ -36,7 +36,7 @@ class TestLiteLLMAdapter(LiteLLMOpenAIMixin):
 @pytest.fixture
 def adapter_with_config_key():
     """Fixture to create adapter with API key in config"""
-    config = TestConfig(api_key="config-api-key")
+    config = TestConfig(api_key=SecretStr("config-api-key"))
     adapter = TestLiteLLMAdapter(config)
     adapter.__provider_spec__ = MagicMock()
     adapter.__provider_spec__.provider_data_validator = (
@@ -59,7 +59,7 @@ def adapter_without_config_key():
 
 def test_api_key_from_config_when_no_provider_data(adapter_with_config_key):
     """Test that adapter uses config API key when no provider data is available"""
-    api_key = adapter_with_config_key.get_api_key()
+    api_key = adapter_with_config_key.get_api_key().get_secret_value()
     assert api_key == "config-api-key"
 
 
@@ -68,28 +68,28 @@ def test_provider_data_takes_priority_over_config(adapter_with_config_key):
     with request_provider_data_context(
         {"x-llamastack-provider-data": json.dumps({"test_api_key": "provider-data-key"})}
     ):
-        api_key = adapter_with_config_key.get_api_key()
+        api_key = adapter_with_config_key.get_api_key().get_secret_value()
         assert api_key == "provider-data-key"
 
 
 def test_fallback_to_config_when_provider_data_missing_key(adapter_with_config_key):
     """Test fallback to config when provider data doesn't have the required key"""
     with request_provider_data_context({"x-llamastack-provider-data": json.dumps({"wrong_key": "some-value"})}):
-        api_key = adapter_with_config_key.get_api_key()
+        api_key = adapter_with_config_key.get_api_key().get_secret_value()
         assert api_key == "config-api-key"
 
 
 def test_error_when_no_api_key_available(adapter_without_config_key):
     """Test that ValueError is raised when neither config nor provider data have API key"""
     with pytest.raises(ValueError, match="API key is not set"):
-        adapter_without_config_key.get_api_key()
+        adapter_without_config_key.get_api_key().get_secret_value()
 
 
 def test_error_when_provider_data_has_wrong_key(adapter_without_config_key):
     """Test that ValueError is raised when provider data exists but doesn't have required key"""
     with request_provider_data_context({"x-llamastack-provider-data": json.dumps({"wrong_key": "some-value"})}):
         with pytest.raises(ValueError, match="API key is not set"):
-            adapter_without_config_key.get_api_key()
+            adapter_without_config_key.get_api_key().get_secret_value()
 
 
 def test_provider_data_works_when_config_is_none(adapter_without_config_key):
@@ -97,14 +97,14 @@ def test_provider_data_works_when_config_is_none(adapter_without_config_key):
     with request_provider_data_context(
         {"x-llamastack-provider-data": json.dumps({"test_api_key": "provider-only-key"})}
     ):
-        api_key = adapter_without_config_key.get_api_key()
+        api_key = adapter_without_config_key.get_api_key().get_secret_value()
         assert api_key == "provider-only-key"
 
 
 def test_error_message_includes_correct_field_names(adapter_without_config_key):
     """Test that error message includes correct field name and header information"""
     try:
-        adapter_without_config_key.get_api_key()
+        adapter_without_config_key.get_api_key().get_secret_value()
         raise AssertionError("Should have raised ValueError")
     except ValueError as e:
         assert "test_api_key" in str(e)  # Should mention the correct field name

tests/unit/providers/inference/test_openai_base_url_config.py

@@ -7,6 +7,8 @@
 import os
 from unittest.mock import MagicMock, patch
 
+from pydantic import SecretStr
+
 from llama_stack.core.stack import replace_env_vars
 from llama_stack.providers.remote.inference.openai.config import OpenAIConfig
 from llama_stack.providers.remote.inference.openai.openai import OpenAIInferenceAdapter
@@ -59,14 +61,14 @@ class TestOpenAIBaseURLConfig:
         adapter = OpenAIInferenceAdapter(config)
 
         # Mock the get_api_key method since it's delegated to LiteLLMOpenAIMixin
-        adapter.get_api_key = MagicMock(return_value="test-key")
+        adapter.get_api_key = MagicMock(return_value=SecretStr("test-key"))
 
         # Access the client property to trigger AsyncOpenAI initialization
         _ = adapter.client
 
         # Verify AsyncOpenAI was called with the correct base_url
         mock_openai_class.assert_called_once_with(
-            api_key="test-key",
+            api_key=SecretStr("test-key"),
             base_url=custom_url,
         )
 
@@ -78,7 +80,7 @@ class TestOpenAIBaseURLConfig:
         adapter = OpenAIInferenceAdapter(config)
 
         # Mock the get_api_key method
-        adapter.get_api_key = MagicMock(return_value="test-key")
+        adapter.get_api_key = MagicMock(return_value=SecretStr("test-key"))
 
         # Mock a model object that will be returned by models.list()
         mock_model = MagicMock()
@@ -101,7 +103,7 @@ class TestOpenAIBaseURLConfig:
 
         # Verify the client was created with the custom URL
         mock_openai_class.assert_called_with(
-            api_key="test-key",
+            api_key=SecretStr("test-key"),
             base_url=custom_url,
         )
 
@@ -119,7 +121,7 @@ class TestOpenAIBaseURLConfig:
         adapter = OpenAIInferenceAdapter(config)
 
         # Mock the get_api_key method
-        adapter.get_api_key = MagicMock(return_value="test-key")
+        adapter.get_api_key = MagicMock(return_value=SecretStr("test-key"))
 
         # Mock a model object that will be returned by models.list()
         mock_model = MagicMock()
@@ -142,6 +144,6 @@ class TestOpenAIBaseURLConfig:
 
         # Verify the client was created with the environment variable URL
         mock_openai_class.assert_called_with(
-            api_key="test-key",
+            api_key=SecretStr("test-key"),
             base_url="https://proxy.openai.com/v1",
         )