security: Enhance Hugging Face model loading security - resolves #136

BREAKING CHANGE: trust_remote_code now defaults to False for security

- Set trust_remote_code=False by default in HFChat class
- Add explicit trust_remote_code parameter to HFChat.__init__()
- Add security warning when trust_remote_code=True is used
- Update get_llm() function to support trust_remote_code parameter
- Update benchmark utilities (load_hf_model, load_vllm_model, load_qwen_vl_model)
- Add comprehensive documentation for security implications

Security Benefits:
- Prevents arbitrary code execution from compromised model repositories
- Requires explicit opt-in for models that need remote code execution
- Shows clear warnings when security is reduced
- Follows security-by-default principle

Migration Guide:
- Most users: No changes needed (more secure by default)
- Users with models requiring remote code: Add trust_remote_code=True explicitly
- Config users: Add 'trust_remote_code': true to LLM config if needed

Fixes #136

benchmarks/llm_utils.py

@@ -54,29 +54,51 @@ def extract_thinking_answer(response):
     return response.strip()
 
 
-def load_hf_model(model_name="Qwen/Qwen3-8B"):
-    """Load HuggingFace model"""
+def load_hf_model(model_name="Qwen/Qwen3-8B", trust_remote_code=False):
+    """Load HuggingFace model
+
+    Args:
+        model_name (str): Name of the model to load
+        trust_remote_code (bool): Whether to allow execution of code from the model repository.
+            Defaults to False for security. Only enable for trusted models.
+    """
     if not HF_AVAILABLE:
         raise ImportError("transformers not available")
 
+    if trust_remote_code:
+        print(
+            "⚠️  WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
+        )
+
     print(f"Loading HF: {model_name}")
-    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code)
     model = AutoModelForCausalLM.from_pretrained(
         model_name,
         torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
         device_map="auto",
-        trust_remote_code=True,
+        trust_remote_code=trust_remote_code,
     )
     return tokenizer, model
 
 
-def load_vllm_model(model_name="Qwen/Qwen3-8B"):
-    """Load vLLM model"""
+def load_vllm_model(model_name="Qwen/Qwen3-8B", trust_remote_code=False):
+    """Load vLLM model
+
+    Args:
+        model_name (str): Name of the model to load
+        trust_remote_code (bool): Whether to allow execution of code from the model repository.
+            Defaults to False for security. Only enable for trusted models.
+    """
     if not VLLM_AVAILABLE:
         raise ImportError("vllm not available")
 
+    if trust_remote_code:
+        print(
+            "⚠️  WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
+        )
+
     print(f"Loading vLLM: {model_name}")
-    llm = LLM(model=model_name, trust_remote_code=True)
+    llm = LLM(model=model_name, trust_remote_code=trust_remote_code)
 
     # Qwen3 specific config
     if is_qwen3_model(model_name):
@@ -178,19 +200,33 @@ def evaluate_rag(searcher, llm_func, queries, domain="default", top_k=3, complex
     }
 
 
-def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct"):
-    """Load Qwen2.5-VL multimodal model"""
+def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=False):
+    """Load Qwen2.5-VL multimodal model
+
+    Args:
+        model_name (str): Name of the model to load
+        trust_remote_code (bool): Whether to allow execution of code from the model repository.
+            Defaults to False for security. Only enable for trusted models.
+    """
     if not HF_AVAILABLE:
         raise ImportError("transformers not available")
 
+    if trust_remote_code:
+        print(
+            "⚠️  WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
+        )
+
     print(f"Loading Qwen2.5-VL: {model_name}")
 
     try:
         from transformers import AutoModelForVision2Seq, AutoProcessor
 
-        processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+        processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=trust_remote_code)
         model = AutoModelForVision2Seq.from_pretrained(
-            model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
+            model_name,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            trust_remote_code=trust_remote_code,
         )
 
         return processor, model
@@ -202,9 +238,14 @@ def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct"):
         try:
             from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
 
-            processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+            processor = AutoProcessor.from_pretrained(
+                model_name, trust_remote_code=trust_remote_code
+            )
             model = Qwen2VLForConditionalGeneration.from_pretrained(
-                model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
+                model_name,
+                torch_dtype=torch.bfloat16,
+                device_map="auto",
+                trust_remote_code=trust_remote_code,
             )
 
             return processor, model

packages/leann-core/src/leann/chat.py

@@ -546,11 +546,30 @@ class OllamaChat(LLMInterface):
 
 
 class HFChat(LLMInterface):
-    """LLM interface for local Hugging Face Transformers models with proper chat templates."""
+    """LLM interface for local Hugging Face Transformers models with proper chat templates.
 
-    def __init__(self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat"):
+    Args:
+        model_name (str): Name of the Hugging Face model to load.
+        trust_remote_code (bool): Whether to allow execution of code from the model repository.
+            Defaults to False for security. Only enable for trusted models as this can pose
+            a security risk if the model repository is compromised.
+    """
+
+    def __init__(
+        self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat", trust_remote_code: bool = False
+    ):
         logger.info(f"Initializing HFChat with model='{model_name}'")
 
+        # Security warning when trust_remote_code is enabled
+        if trust_remote_code:
+            logger.warning(
+                "SECURITY WARNING: trust_remote_code=True allows execution of arbitrary code from the model repository. "
+                "Only enable this for models from trusted sources. This creates a potential security risk if the model "
+                "repository is compromised."
+            )
+
+        self.trust_remote_code = trust_remote_code
+
         # Pre-check model availability with helpful suggestions
         model_error = validate_model_and_suggest(model_name, "hf")
         if model_error:
@@ -588,14 +607,16 @@ class HFChat(LLMInterface):
 
             try:
                 logger.info(f"Loading tokenizer for {model_name}...")
-                self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+                self.tokenizer = AutoTokenizer.from_pretrained(
+                    model_name, trust_remote_code=self.trust_remote_code
+                )
 
                 logger.info(f"Loading model {model_name}...")
                 self.model = AutoModelForCausalLM.from_pretrained(
                     model_name,
                     torch_dtype=torch.float16 if self.device != "cpu" else torch.float32,
                     device_map="auto" if self.device != "cpu" else None,
-                    trust_remote_code=True,
+                    trust_remote_code=self.trust_remote_code,
                 )
                 logger.info(f"Successfully loaded {model_name}")
             finally:
@@ -859,7 +880,10 @@ def get_llm(llm_config: Optional[dict[str, Any]] = None) -> LLMInterface:
             host=llm_config.get("host"),
         )
     elif llm_type == "hf":
-        return HFChat(model_name=model or "deepseek-ai/deepseek-llm-7b-chat")
+        return HFChat(
+            model_name=model or "deepseek-ai/deepseek-llm-7b-chat",
+            trust_remote_code=llm_config.get("trust_remote_code", False),
+        )
     elif llm_type == "openai":
         return OpenAIChat(
             model=model or "gpt-4o",