diff --git a/libs/experimental/langchain_experimental/sql/base.py b/libs/experimental/langchain_experimental/sql/base.py
index fcc664a75d..5773e45561 100644
--- a/libs/experimental/langchain_experimental/sql/base.py
+++ b/libs/experimental/langchain_experimental/sql/base.py
@@ -29,6 +29,15 @@ class SQLDatabaseChain(Chain):
             from langchain import OpenAI, SQLDatabase
             db = SQLDatabase(...)
             db_chain = SQLDatabaseChain.from_llm(OpenAI(), db)
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include the permissions this chain needs.
+        Failure to do so may result in data corruption or loss, since this chain may
+        attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this chain.
+        This issue shows an example negative outcome if these steps are not taken:
+        https://github.com/langchain-ai/langchain/issues/5923
     """
 
     llm_chain: LLMChain
@@ -49,7 +58,7 @@ class SQLDatabaseChain(Chain):
     return_direct: bool = False
     """Whether or not to return the result of querying the SQL table directly."""
     use_query_checker: bool = False
-    """Whether or not the query checker tool should be used to attempt 
+    """Whether or not the query checker tool should be used to attempt
     to fix the initial SQL from the LLM."""
     query_checker_prompt: Optional[BasePromptTemplate] = None
     """The prompt template that should be used by the query checker"""
@@ -197,6 +206,17 @@ class SQLDatabaseChain(Chain):
         prompt: Optional[BasePromptTemplate] = None,
         **kwargs: Any,
     ) -> SQLDatabaseChain:
+        """Create a SQLDatabaseChain from an LLM and a database connection.
+
+        *Security note*: Make sure that the database connection uses credentials
+            that are narrowly-scoped to only include the permissions this chain needs.
+            Failure to do so may result in data corruption or loss, since this chain may
+            attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
+            The best way to guard against such negative outcomes is to (as appropriate)
+            limit the permissions granted to the credentials used with this chain.
+            This issue shows an example negative outcome if these steps are not taken:
+            https://github.com/langchain-ai/langchain/issues/5923
+        """
         prompt = prompt or SQL_PROMPTS.get(db.dialect, PROMPT)
         llm_chain = LLMChain(llm=llm, prompt=prompt)
         return cls(llm_chain=llm_chain, database=db, **kwargs)