diff --git a/vllm/model_executor/guided_decoding/xgrammar_decoding.py b/vllm/model_executor/guided_decoding/xgrammar_decoding.py
index 80e88dd5b4b37..fc45e37cf6f06 100644
--- a/vllm/model_executor/guided_decoding/xgrammar_decoding.py
+++ b/vllm/model_executor/guided_decoding/xgrammar_decoding.py
@@ -131,22 +131,25 @@ def from_guided_params(cls,
                            max_threads: int = 8) -> GrammarConfig:
 
         tokenizer_hash = hash(tokenizer)
-        # Only get tokenizer data if not already cached
-        if tokenizer_hash in TokenizerDataCache._cache:
-            encoded_vocab = None
-            stop_token_ids = None
-            backend_str = None
-        else:
-            tokenizer_data = TokenizerDataCache.get_tokenizer_data(tokenizer)
-            encoded_vocab = tokenizer_data.encoded_vocab
-            stop_token_ids = tokenizer_data.stop_token_ids
-            backend_str = tokenizer_data.backend_str
+        tokenizer_data = TokenizerDataCache.get_tokenizer_data(tokenizer)
+        encoded_vocab = tokenizer_data.encoded_vocab
+        stop_token_ids = tokenizer_data.stop_token_ids
+        backend_str = tokenizer_data.backend_str
 
         if guided_params.json:
             if not isinstance(guided_params.json, str):
                 json_str = json.dumps(guided_params.json)
             else:
                 json_str = guided_params.json
+
+            # Validate the schema and raise ValueError here if it is invalid.
+            # This is to avoid exceptions in model execution, which will crash
+            # the engine worker process.
+            try:
+                xgr.Grammar.from_json_schema(json_str)
+            except RuntimeError as err:
+                raise ValueError(str(err)) from err
+
             return cls(json_str=json_str,
                        vocab_size=model_config.hf_text_config.vocab_size,
                        encoded_vocab=encoded_vocab,
@@ -167,6 +170,15 @@ def from_guided_params(cls,
                         f"Conversion error: {str(e)}") from e
             else:
                 grammar_str = guided_params.grammar
+
+            # Validate the grammar and raise ValueError here if it is invalid.
+            # This is to avoid exceptions in model execution, which will crash
+            # the engine worker process.
+            try:
+                xgr.Grammar.from_ebnf(grammar_str)
+            except RuntimeError as err:
+                raise ValueError(str(err)) from err
+
             return cls(grammar_str=grammar_str,
                        vocab_size=model_config.hf_text_config.vocab_size,
                        encoded_vocab=encoded_vocab,
diff --git a/vllm/model_executor/guided_decoding/xgrammar_utils.py b/vllm/model_executor/guided_decoding/xgrammar_utils.py
index 12b42245f4e3d..9a0463964de49 100644
--- a/vllm/model_executor/guided_decoding/xgrammar_utils.py
+++ b/vllm/model_executor/guided_decoding/xgrammar_utils.py
@@ -26,15 +26,11 @@ def grammar_is_likely_lark(grammar_str: str) -> bool:
         if not line:
             continue
 
-        # Look for Lark-style rule definitions
-        if ':' in line and '::=' not in line:
-            return True
+        # Look for GBNF rule definition
+        if '::=' in line:
+            return False
 
-        # Look for Lark-specific features
-        if any(pattern in line for pattern in ['?start:', '|', '~']):
-            return True
-
-    return False
+    return True
 
 
 def convert_lark_to_gbnf(grammar_str: str) -> str: