Sec fixes as raised by bandit (#917)

minor fixes to hashlib and jinja

docs/openapi_generator/pyopenapi/generator.py

@@ -250,7 +250,9 @@ class ContentBuilder:
             value = sample_transformer(object_to_json(example))
 
             hash_string = (
-                hashlib.md5(json_dump_string(value).encode("utf-8")).digest().hex()
+                hashlib.sha256(json_dump_string(value).encode("utf-8"))
+                .digest()
+                .hex()[:16]
             )
             name = f"ex-{hash_string}"
 

docs/source/distributions/self_hosted_distro/sambanova.md

@@ -38,13 +38,15 @@ The following models are available by default:
 - `meta-llama/Llama-3.1-405B-Instruct-FP8 (Meta-Llama-3.1-405B-Instruct)`
 - `meta-llama/Llama-3.2-1B-Instruct (Meta-Llama-3.2-1B-Instruct)`
 - `meta-llama/Llama-3.2-3B-Instruct (Meta-Llama-3.2-3B-Instruct)`
+- `meta-llama/Llama-3.3-70B-Instruct (Meta-Llama-3.3-70B-Instruct)`
 - `meta-llama/Llama-3.2-11B-Vision-Instruct (Llama-3.2-11B-Vision-Instruct)`
 - `meta-llama/Llama-3.2-90B-Vision-Instruct (Llama-3.2-90B-Vision-Instruct)`
+- `meta-llama/Llama-Guard-3-8B (Meta-Llama-Guard-3-8B)`
 
 
 ### Prerequisite: API Keys
 
-Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](https://cloud.sambanova.ai/).
+Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](https://sambanova.ai/).
 
 
 ## Running Llama Stack with SambaNova

llama_stack/cli/verify_download.py

@@ -50,7 +50,10 @@ def setup_verify_download_parser(parser: argparse.ArgumentParser) -> None:
 
 
 def calculate_md5(filepath: Path, chunk_size: int = 8192) -> str:
-    md5_hash = hashlib.md5()
+    # NOTE: MD5 is used here only for download integrity verification,
+    # not for security purposes
+    # TODO: switch to SHA256
+    md5_hash = hashlib.md5(usedforsecurity=False)
     with open(filepath, "rb") as f:
         for chunk in iter(lambda: f.read(chunk_size), b""):
             md5_hash.update(chunk)

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

@@ -52,6 +52,6 @@ class NVIDIAConfig(BaseModel):
     @classmethod
     def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
         return {
-            "url": "https://integrate.api.nvidia.com",
-            "api_key": "${env.NVIDIA_API_KEY}",
+            "url": "${env.NVIDIA_BASE_URL:https://integrate.api.nvidia.com}",
+            "api_key": "${env.NVIDIA_API_KEY:}",
         }

llama_stack/templates/sambanova/build.yaml

@@ -14,19 +14,9 @@ distribution_spec:
     - inline::meta-reference
     telemetry:
     - inline::meta-reference
-    eval:
-    - inline::meta-reference
-    datasetio:
-    - remote::huggingface
-    - inline::localfs
-    scoring:
-    - inline::basic
-    - inline::llm-as-judge
-    - inline::braintrust
     tool_runtime:
     - remote::brave-search
     - remote::tavily-search
     - inline::code-interpreter
     - inline::rag-runtime
-    - remote::model-context-protocol
 image_type: conda

llama_stack/templates/sambanova/doc_template.md

@@ -37,7 +37,7 @@ The following models are available by default:
 
 ### Prerequisite: API Keys
 
-Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaBova.ai](https://sambanova.ai/).
+Make sure you have access to a SambaNova API Key. You can get one by visiting [SambaNova.ai](https://sambanova.ai/).
 
 
 ## Running Llama Stack with SambaNova

llama_stack/templates/sambanova/run.yaml

@@ -2,11 +2,8 @@ version: '2'
 image_name: sambanova
 apis:
 - agents
-- datasetio
-- eval
 - inference
 - safety
-- scoring
 - telemetry
 - tool_runtime
 - vector_io
@@ -25,6 +22,12 @@ providers:
         type: sqlite
         namespace: null
         db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/faiss_store.db
+  - provider_id: chromadb
+    provider_type: remote::chromadb
+    config: {}
+  - provider_id: pgvector
+    provider_type: remote::pgvector
+    config: {}
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard
@@ -44,28 +47,6 @@ providers:
       service_name: ${env.OTEL_SERVICE_NAME:llama-stack}
       sinks: ${env.TELEMETRY_SINKS:console,sqlite}
       sqlite_db_path: ${env.SQLITE_DB_PATH:~/.llama/distributions/sambanova/trace_store.db}
-  eval:
-  - provider_id: meta-reference
-    provider_type: inline::meta-reference
-    config: {}
-  datasetio:
-  - provider_id: huggingface
-    provider_type: remote::huggingface
-    config: {}
-  - provider_id: localfs
-    provider_type: inline::localfs
-    config: {}
-  scoring:
-  - provider_id: basic
-    provider_type: inline::basic
-    config: {}
-  - provider_id: llm-as-judge
-    provider_type: inline::llm-as-judge
-    config: {}
-  - provider_id: braintrust
-    provider_type: inline::braintrust
-    config:
-      openai_api_key: ${env.OPENAI_API_KEY:}
   tool_runtime:
   - provider_id: brave-search
     provider_type: remote::brave-search
@@ -83,9 +64,6 @@ providers:
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
     config: {}
-  - provider_id: model-context-protocol
-    provider_type: remote::model-context-protocol
-    config: {}
 metadata_store:
   type: sqlite
   db_path: ${env.SQLITE_STORE_DIR:~/.llama/distributions/sambanova}/registry.db
@@ -97,7 +75,6 @@ models:
   model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-3.1-70B-Instruct
-  model_type: llm
   provider_id: sambanova
   provider_model_id: Meta-Llama-3.1-70B-Instruct
   model_type: llm
@@ -131,15 +108,10 @@ models:
   provider_id: sambanova
   provider_model_id: Llama-3.2-90B-Vision-Instruct
   model_type: llm
-- metadata: {}
-  model_id: meta-llama/Llama-3.2-90B-Vision-Instruct
-  provider_id: sambanova
-  provider_model_id: Llama-3.2-90B-Vision-Instruct
-  model_type: llm
 - metadata: {}
   model_id: meta-llama/Llama-Guard-3-8B
   provider_id: sambanova
-  provider_model_id: Llama-Guard-3-8B
+  provider_model_id: Meta-Llama-Guard-3-8B
   model_type: llm
 shields:
 - shield_id: meta-llama/Llama-Guard-3-8B

llama_stack/templates/sambanova/sambanova.py

@@ -8,7 +8,12 @@ from pathlib import Path
 
 from llama_models.sku_list import all_registered_models
 
-from llama_stack.distribution.datatypes import ModelInput, Provider, ShieldInput
+from llama_stack.distribution.datatypes import (
+    ModelInput,
+    Provider,
+    ShieldInput,
+    ToolGroupInput,
+)
 from llama_stack.providers.remote.inference.sambanova import SambaNovaImplConfig
 from llama_stack.providers.remote.inference.sambanova.sambanova import MODEL_ALIASES
 
@@ -29,10 +34,11 @@ def get_distribution_template() -> DistributionTemplate:
             "inline::rag-runtime",
         ],
     }
+    name = "sambanova"
 
     inference_provider = Provider(
-        provider_id="sambanova",
-        provider_type="remote::sambanova",
+        provider_id=name,
+        provider_type=f"remote::{name}",
         config=SambaNovaImplConfig.sample_run_config(),
     )
 
@@ -43,12 +49,28 @@ def get_distribution_template() -> DistributionTemplate:
         ModelInput(
             model_id=core_model_to_hf_repo[m.llama_model],
             provider_model_id=m.provider_model_id,
+            provider_id=name,
         )
         for m in MODEL_ALIASES
     ]
 
+    default_tool_groups = [
+        ToolGroupInput(
+            toolgroup_id="builtin::websearch",
+            provider_id="tavily-search",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::rag",
+            provider_id="rag-runtime",
+        ),
+        ToolGroupInput(
+            toolgroup_id="builtin::code_interpreter",
+            provider_id="code-interpreter",
+        ),
+    ]
+
     return DistributionTemplate(
-        name="sambanova",
+        name=name,
         distro_type="self_hosted",
         description="Use SambaNova.AI for running LLM inference",
         docker_image=None,
@@ -62,6 +84,7 @@ def get_distribution_template() -> DistributionTemplate:
                 },
                 default_models=default_models,
                 default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
+                default_tool_groups=default_tool_groups,
             ),
         },
         run_config_env_vars={

llama_stack/templates/template.py

@@ -137,7 +137,12 @@ class DistributionTemplate(BaseModel):
 
         template = self.template_path.read_text()
         # Render template with rich-generated table
-        env = jinja2.Environment(trim_blocks=True, lstrip_blocks=True)
+        env = jinja2.Environment(
+            trim_blocks=True,
+            lstrip_blocks=True,
+            # NOTE: autoescape is required to prevent XSS attacks
+            autoescape=True,
+        )
         template = env.from_string(template)
         return template.render(
             name=self.name,